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4 Commits

Author SHA1 Message Date
copilot-swe-agent[bot]
eae90cafac Final: Fix complete and tested - MCP toggle now shows for all models with MCP config
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2025-11-19 22:16:36 +00:00
copilot-swe-agent[bot]
d2ed2b48a8 Fix: Show MCP toggle for all models with MCP config, not just gallery models
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2025-11-19 22:13:39 +00:00
copilot-swe-agent[bot]
22d8b48fd1 Add test for multiple configs sharing same model file
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2025-11-19 22:11:28 +00:00
copilot-swe-agent[bot]
f2ba636290 Initial plan 2025-11-19 21:52:23 +00:00
539 changed files with 18719 additions and 64991 deletions

9
.env
View File

@@ -32,6 +32,15 @@
# Forces shutdown of the backends if busy (only if LOCALAI_SINGLE_ACTIVE_BACKEND is set)
# LOCALAI_FORCE_BACKEND_SHUTDOWN=true
## Specify a build type. Available: cublas, openblas, clblas.
## cuBLAS: This is a GPU-accelerated version of the complete standard BLAS (Basic Linear Algebra Subprograms) library. It's provided by Nvidia and is part of their CUDA toolkit.
## OpenBLAS: This is an open-source implementation of the BLAS library that aims to provide highly optimized code for various platforms. It includes support for multi-threading and can be compiled to use hardware-specific features for additional performance. OpenBLAS can run on many kinds of hardware, including CPUs from Intel, AMD, and ARM.
## clBLAS: This is an open-source implementation of the BLAS library that uses OpenCL, a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. clBLAS is designed to take advantage of the parallel computing power of GPUs but can also run on any hardware that supports OpenCL. This includes hardware from different vendors like Nvidia, AMD, and Intel.
# BUILD_TYPE=openblas
## Uncomment and set to true to enable rebuilding from source
# REBUILD=true
## Path where to store generated images
# LOCALAI_IMAGE_PATH=/tmp/generated/images

View File

@@ -2,16 +2,11 @@ package main
import (
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"regexp"
"slices"
"strings"
"github.com/ghodss/yaml"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
cogito "github.com/mudler/cogito"
@@ -50,12 +45,7 @@ func cleanTextContent(text string) string {
}
// Remove trailing empty lines from the result
result := strings.Join(cleanedLines, "\n")
return stripThinkingTags(strings.TrimRight(result, "\n"))
}
type galleryModel struct {
Name string `yaml:"name"`
Urls []string `yaml:"urls"`
return strings.TrimRight(result, "\n")
}
// isModelExisting checks if a specific model ID exists in the gallery using text search
@@ -66,20 +56,9 @@ func isModelExisting(modelID string) (bool, error) {
return false, fmt.Errorf("failed to read %s: %w", indexPath, err)
}
var galleryModels []galleryModel
err = yaml.Unmarshal(content, &galleryModels)
if err != nil {
return false, fmt.Errorf("failed to unmarshal %s: %w", indexPath, err)
}
for _, galleryModel := range galleryModels {
if slices.Contains(galleryModel.Urls, modelID) {
return true, nil
}
}
return false, nil
contentStr := string(content)
// Simple text search - if the model ID appears anywhere in the file, it exists
return strings.Contains(contentStr, modelID), nil
}
// filterExistingModels removes models that already exist in the gallery
@@ -113,16 +92,6 @@ func getGalleryIndexPath() string {
return "gallery/index.yaml"
}
func stripThinkingTags(content string) string {
// Remove content between <thinking> and </thinking> (including multi-line)
content = regexp.MustCompile(`(?s)<thinking>.*?</thinking>`).ReplaceAllString(content, "")
// Remove content between <think> and </think> (including multi-line)
content = regexp.MustCompile(`(?s)<think>.*?</think>`).ReplaceAllString(content, "")
// Clean up any extra whitespace
content = strings.TrimSpace(content)
return content
}
func getRealReadme(ctx context.Context, repository string) (string, error) {
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
@@ -151,11 +120,6 @@ func getRealReadme(ctx context.Context, repository string) (string, error) {
}
func selectMostInterestingModels(ctx context.Context, searchResult *SearchResult) ([]ProcessedModel, error) {
if len(searchResult.Models) == 1 {
return searchResult.Models, nil
}
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
AddMessage("user",
@@ -254,192 +218,71 @@ Return your analysis and selection reasoning.`)
return filteredModels, nil
}
// ModelMetadata represents extracted metadata from a model
type ModelMetadata struct {
Tags []string `json:"tags"`
License string `json:"license"`
// ModelFamily represents a YAML anchor/family
type ModelFamily struct {
Anchor string `json:"anchor"`
Name string `json:"name"`
}
// extractModelMetadata extracts tags and license from model README and documentation
func extractModelMetadata(ctx context.Context, model ProcessedModel) ([]string, string, error) {
// selectModelFamily selects the appropriate model family/anchor for a given model
func selectModelFamily(ctx context.Context, model ProcessedModel, availableFamilies []ModelFamily) (string, error) {
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
AddMessage("user",
`Your task is to extract metadata from an AI model's README and documentation. You will be provided with:
1. Model information (ID, author, description)
2. README content
`Your task is to select the most appropriate model family/anchor for a given AI model. You will be provided with:
1. Information about the model (name, description, etc.)
2. A list of available model families/anchors
You need to extract:
1. **Tags**: An array of relevant tags that describe the model. Use common tags from the gallery such as:
- llm, gguf, gpu, cpu, multimodal, image-to-text, text-to-text, text-to-speech, tts
- thinking, reasoning, chat, instruction-tuned, code, vision
- Model family names (e.g., llama, qwen, mistral, gemma) if applicable
- Any other relevant descriptive tags
Select 3-8 most relevant tags.
You need to select the family that best matches the model's architecture, capabilities, or characteristics. Consider:
- Model architecture (e.g., Llama, Qwen, Mistral, etc.)
- Model capabilities (e.g., vision, coding, chat, etc.)
- Model size/type (e.g., small, medium, large)
- Model purpose (e.g., general purpose, specialized, etc.)
2. **License**: The license identifier (e.g., "apache-2.0", "mit", "llama2", "gpl-3.0", "bsd", "cc-by-4.0").
If no license is found, return an empty string.
Return the extracted metadata in a structured format.`)
Return the anchor name that best fits the model.`)
// Add model information
modelInfo := "Model Information:\n"
modelInfo += fmt.Sprintf(" ID: %s\n", model.ModelID)
modelInfo += fmt.Sprintf(" Author: %s\n", model.Author)
modelInfo += fmt.Sprintf(" Downloads: %d\n", model.Downloads)
if model.ReadmeContent != "" {
modelInfo += fmt.Sprintf(" README Content:\n%s\n", model.ReadmeContent)
} else if model.ReadmeContentPreview != "" {
modelInfo += fmt.Sprintf(" README Preview: %s\n", model.ReadmeContentPreview)
}
modelInfo += fmt.Sprintf(" Description: %s\n", model.ReadmeContentPreview)
fragment = fragment.AddMessage("user", modelInfo)
fragment = fragment.AddMessage("user", "Extract the tags and license from the model information. Return the metadata as a JSON object with 'tags' (array of strings) and 'license' (string).")
// Add available families
familiesInfo := "Available Model Families:\n"
for _, family := range availableFamilies {
familiesInfo += fmt.Sprintf(" - %s (%s)\n", family.Anchor, family.Name)
}
fragment = fragment.AddMessage("user", familiesInfo)
fragment = fragment.AddMessage("user", "Select the most appropriate family anchor for this model. Return just the anchor name.")
// Get a response
newFragment, err := llm.Ask(ctx, fragment)
if err != nil {
return nil, "", err
return "", err
}
// Extract structured metadata
metadata := ModelMetadata{}
// Extract the selected family
selectedFamily := strings.TrimSpace(newFragment.LastMessage().Content)
s := structures.Structure{
Schema: jsonschema.Definition{
Type: jsonschema.Object,
AdditionalProperties: false,
Properties: map[string]jsonschema.Definition{
"tags": {
Type: jsonschema.Array,
Items: &jsonschema.Definition{Type: jsonschema.String},
Description: "Array of relevant tags describing the model",
},
"license": {
Type: jsonschema.String,
Description: "License identifier (e.g., apache-2.0, mit, llama2). Empty string if not found.",
},
},
Required: []string{"tags", "license"},
},
Object: &metadata,
}
err = newFragment.ExtractStructure(ctx, llm, s)
if err != nil {
return nil, "", err
}
return metadata.Tags, metadata.License, nil
}
// extractIconFromReadme scans the README content for image URLs and returns the first suitable icon URL found
func extractIconFromReadme(readmeContent string) string {
if readmeContent == "" {
return ""
}
// Regular expressions to match image URLs in various formats (case-insensitive)
// Match markdown image syntax: ![alt](url) - case insensitive extensions
markdownImageRegex := regexp.MustCompile(`(?i)!\[[^\]]*\]\(([^)]+\.(png|jpg|jpeg|svg|webp|gif))\)`)
// Match HTML img tags: <img src="url">
htmlImageRegex := regexp.MustCompile(`(?i)<img[^>]+src=["']([^"']+\.(png|jpg|jpeg|svg|webp|gif))["']`)
// Match plain URLs ending with image extensions
plainImageRegex := regexp.MustCompile(`(?i)https?://[^\s<>"']+\.(png|jpg|jpeg|svg|webp|gif)`)
// Try markdown format first
matches := markdownImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 1 && matches[1] != "" {
url := strings.TrimSpace(matches[1])
// Prefer HuggingFace CDN URLs or absolute URLs
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
// Validate that the selected family exists in our list
for _, family := range availableFamilies {
if family.Anchor == selectedFamily {
return selectedFamily, nil
}
}
// Try HTML img tags
matches = htmlImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 1 && matches[1] != "" {
url := strings.TrimSpace(matches[1])
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
// If no exact match, try to find a close match
for _, family := range availableFamilies {
if strings.Contains(strings.ToLower(family.Anchor), strings.ToLower(selectedFamily)) ||
strings.Contains(strings.ToLower(selectedFamily), strings.ToLower(family.Anchor)) {
return family.Anchor, nil
}
}
// Try plain URLs
matches = plainImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 0 {
url := strings.TrimSpace(matches[0])
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
}
}
return ""
}
// getHuggingFaceAvatarURL attempts to get the HuggingFace avatar URL for a user
func getHuggingFaceAvatarURL(author string) string {
if author == "" {
return ""
}
// Try to fetch user info from HuggingFace API
// HuggingFace API endpoint: https://huggingface.co/api/users/{username}
baseURL := "https://huggingface.co"
userURL := fmt.Sprintf("%s/api/users/%s", baseURL, author)
req, err := http.NewRequest("GET", userURL, nil)
if err != nil {
return ""
}
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
return ""
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return ""
}
// Parse the response to get avatar URL
var userInfo map[string]interface{}
body, err := io.ReadAll(resp.Body)
if err != nil {
return ""
}
if err := json.Unmarshal(body, &userInfo); err != nil {
return ""
}
// Try to extract avatar URL from response
if avatar, ok := userInfo["avatarUrl"].(string); ok && avatar != "" {
return avatar
}
if avatar, ok := userInfo["avatar"].(string); ok && avatar != "" {
return avatar
}
return ""
}
// extractModelIcon extracts icon URL from README or falls back to HuggingFace avatar
func extractModelIcon(model ProcessedModel) string {
// First, try to extract icon from README
if icon := extractIconFromReadme(model.ReadmeContent); icon != "" {
return icon
}
// Fallback: Try to get HuggingFace user avatar
if model.Author != "" {
if avatar := getHuggingFaceAvatarURL(model.Author); avatar != "" {
return avatar
}
}
return ""
// Default fallback
return "llama3", nil
}

View File

@@ -2,61 +2,13 @@ package main
import (
"context"
"encoding/json"
"fmt"
"os"
"strings"
"github.com/ghodss/yaml"
"github.com/mudler/LocalAI/core/gallery/importers"
)
func formatTextContent(text string) string {
return formatTextContentWithIndent(text, 4, 6)
}
// formatTextContentWithIndent formats text content with specified base and list item indentation
func formatTextContentWithIndent(text string, baseIndent int, listItemIndent int) string {
var formattedLines []string
lines := strings.Split(text, "\n")
for _, line := range lines {
trimmed := strings.TrimRight(line, " \t\r")
if trimmed == "" {
// Keep empty lines as empty (no indentation)
formattedLines = append(formattedLines, "")
} else {
// Preserve relative indentation from yaml.Marshal output
// Count existing leading spaces to preserve relative structure
leadingSpaces := len(trimmed) - len(strings.TrimLeft(trimmed, " \t"))
trimmedStripped := strings.TrimLeft(trimmed, " \t")
var totalIndent int
if strings.HasPrefix(trimmedStripped, "-") {
// List items: use listItemIndent (ignore existing leading spaces)
totalIndent = listItemIndent
} else {
// Regular lines: use baseIndent + preserve relative indentation
// This handles both top-level keys (leadingSpaces=0) and nested properties (leadingSpaces>0)
totalIndent = baseIndent + leadingSpaces
}
indentStr := strings.Repeat(" ", totalIndent)
formattedLines = append(formattedLines, indentStr+trimmedStripped)
}
}
formattedText := strings.Join(formattedLines, "\n")
// Remove any trailing spaces from the formatted description
formattedText = strings.TrimRight(formattedText, " \t")
return formattedText
}
// generateYAMLEntry generates a YAML entry for a model using the specified anchor
func generateYAMLEntry(model ProcessedModel, quantization string) string {
modelConfig, err := importers.DiscoverModelConfig("https://huggingface.co/"+model.ModelID, json.RawMessage(`{ "quantization": "`+quantization+`"}`))
if err != nil {
panic(err)
}
func generateYAMLEntry(model ProcessedModel, familyAnchor string) string {
// Extract model name from ModelID
parts := strings.Split(model.ModelID, "/")
modelName := model.ModelID
@@ -70,6 +22,18 @@ func generateYAMLEntry(model ProcessedModel, quantization string) string {
modelName = strings.ReplaceAll(modelName, "-q3_k_m", "")
modelName = strings.ReplaceAll(modelName, "-q2_k", "")
fileName := ""
checksum := ""
if model.PreferredModelFile != nil {
fileParts := strings.Split(model.PreferredModelFile.Path, "/")
if len(fileParts) > 0 {
fileName = fileParts[len(fileParts)-1]
}
checksum = model.PreferredModelFile.SHA256
} else {
fileName = model.ModelID
}
description := model.ReadmeContent
if description == "" {
description = fmt.Sprintf("AI model: %s", modelName)
@@ -77,88 +41,142 @@ func generateYAMLEntry(model ProcessedModel, quantization string) string {
// Clean up description to prevent YAML linting issues
description = cleanTextContent(description)
formattedDescription := formatTextContent(description)
configFile := formatTextContent(modelConfig.ConfigFile)
filesYAML, _ := yaml.Marshal(modelConfig.Files)
// Files section: list items need 4 spaces (not 6), since files: is at 2 spaces
files := formatTextContentWithIndent(string(filesYAML), 4, 4)
// Build metadata sections
var metadataSections []string
// Add license if present
if model.License != "" {
metadataSections = append(metadataSections, fmt.Sprintf(` license: "%s"`, model.License))
// Format description for YAML (indent each line and ensure no trailing spaces)
lines := strings.Split(description, "\n")
var formattedLines []string
for _, line := range lines {
if strings.TrimSpace(line) == "" {
// Keep empty lines as empty (no indentation)
formattedLines = append(formattedLines, "")
} else {
// Add indentation to non-empty lines
formattedLines = append(formattedLines, " "+line)
}
}
// Add tags if present
if len(model.Tags) > 0 {
tagsYAML, _ := yaml.Marshal(model.Tags)
tagsFormatted := formatTextContentWithIndent(string(tagsYAML), 4, 4)
tagsFormatted = strings.TrimRight(tagsFormatted, "\n")
metadataSections = append(metadataSections, fmt.Sprintf(" tags:\n%s", tagsFormatted))
}
// Add icon if present
if model.Icon != "" {
metadataSections = append(metadataSections, fmt.Sprintf(` icon: %s`, model.Icon))
}
// Build the metadata block
metadataBlock := ""
if len(metadataSections) > 0 {
metadataBlock = strings.Join(metadataSections, "\n") + "\n"
}
formattedDescription := strings.Join(formattedLines, "\n")
// Remove any trailing spaces from the formatted description
formattedDescription = strings.TrimRight(formattedDescription, " \t")
yamlTemplate := ""
yamlTemplate = `- name: "%s"
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
if checksum != "" {
yamlTemplate = `- !!merge <<: *%s
name: "%s"
urls:
- https://huggingface.co/%s
description: |
%s%s
overrides:
%s
overrides:
parameters:
model: %s
files:
%s`
// Trim trailing newlines from formatted sections to prevent extra blank lines
formattedDescription = strings.TrimRight(formattedDescription, "\n")
configFile = strings.TrimRight(configFile, "\n")
files = strings.TrimRight(files, "\n")
// Add newline before metadata block if present
if metadataBlock != "" {
metadataBlock = "\n" + strings.TrimRight(metadataBlock, "\n")
- filename: %s
sha256: %s
uri: huggingface://%s/%s`
return fmt.Sprintf(yamlTemplate,
familyAnchor,
modelName,
model.ModelID,
formattedDescription,
fileName,
fileName,
checksum,
model.ModelID,
fileName,
)
} else {
yamlTemplate = `- !!merge <<: *%s
name: "%s"
urls:
- https://huggingface.co/%s
description: |
%s
overrides:
parameters:
model: %s`
return fmt.Sprintf(yamlTemplate,
familyAnchor,
modelName,
model.ModelID,
formattedDescription,
fileName,
)
}
return fmt.Sprintf(yamlTemplate,
modelName,
model.ModelID,
formattedDescription,
metadataBlock,
configFile,
files,
)
}
// extractModelFamilies extracts all YAML anchors from the gallery index.yaml file
func extractModelFamilies() ([]ModelFamily, error) {
// Read the index.yaml file
indexPath := getGalleryIndexPath()
content, err := os.ReadFile(indexPath)
if err != nil {
return nil, fmt.Errorf("failed to read %s: %w", indexPath, err)
}
lines := strings.Split(string(content), "\n")
var families []ModelFamily
for _, line := range lines {
line = strings.TrimSpace(line)
// Look for YAML anchors (lines starting with "- &")
if strings.HasPrefix(line, "- &") {
// Extract the anchor name (everything after "- &")
anchor := strings.TrimPrefix(line, "- &")
// Remove any trailing colon or other characters
anchor = strings.Split(anchor, ":")[0]
anchor = strings.Split(anchor, " ")[0]
if anchor != "" {
families = append(families, ModelFamily{
Anchor: anchor,
Name: anchor, // Use anchor as name for now
})
}
}
}
return families, nil
}
// generateYAMLForModels generates YAML entries for selected models and appends to index.yaml
func generateYAMLForModels(ctx context.Context, models []ProcessedModel, quantization string) error {
func generateYAMLForModels(ctx context.Context, models []ProcessedModel) error {
// Extract available model families
families, err := extractModelFamilies()
if err != nil {
return fmt.Errorf("failed to extract model families: %w", err)
}
fmt.Printf("Found %d model families: %v\n", len(families),
func() []string {
var names []string
for _, f := range families {
names = append(names, f.Anchor)
}
return names
}())
// Generate YAML entries for each model
var yamlEntries []string
for _, model := range models {
fmt.Printf("Generating YAML entry for model: %s\n", model.ModelID)
fmt.Printf("Selecting family for model: %s\n", model.ModelID)
// Select appropriate family for this model
familyAnchor, err := selectModelFamily(ctx, model, families)
if err != nil {
fmt.Printf("Error selecting family for %s: %v, using default\n", model.ModelID, err)
familyAnchor = "llama3" // Default fallback
}
fmt.Printf("Selected family '%s' for model %s\n", familyAnchor, model.ModelID)
// Generate YAML entry
yamlEntry := generateYAMLEntry(model, quantization)
yamlEntry := generateYAMLEntry(model, familyAnchor)
yamlEntries = append(yamlEntries, yamlEntry)
}
// Prepend to index.yaml (write at the top)
// Append to index.yaml
if len(yamlEntries) > 0 {
indexPath := getGalleryIndexPath()
fmt.Printf("Prepending YAML entries to %s...\n", indexPath)
fmt.Printf("Appending YAML entries to %s...\n", indexPath)
// Read current content
content, err := os.ReadFile(indexPath)
@@ -166,26 +184,11 @@ func generateYAMLForModels(ctx context.Context, models []ProcessedModel, quantiz
return fmt.Errorf("failed to read %s: %w", indexPath, err)
}
existingContent := string(content)
// Append new entries
// Remove trailing whitespace from existing content and join entries without extra newlines
existingContent := strings.TrimRight(string(content), " \t\n\r")
yamlBlock := strings.Join(yamlEntries, "\n")
// Check if file starts with "---"
var newContent string
if strings.HasPrefix(existingContent, "---\n") {
// File starts with "---", prepend new entries after it
restOfContent := strings.TrimPrefix(existingContent, "---\n")
// Ensure proper spacing: "---\n" + new entries + "\n" + rest of content
newContent = "---\n" + yamlBlock + "\n" + restOfContent
} else if strings.HasPrefix(existingContent, "---") {
// File starts with "---" but no newline after
restOfContent := strings.TrimPrefix(existingContent, "---")
newContent = "---\n" + yamlBlock + "\n" + strings.TrimPrefix(restOfContent, "\n")
} else {
// No "---" at start, prepend new entries at the very beginning
// Trim leading whitespace from existing content
existingContent = strings.TrimLeft(existingContent, " \t\n\r")
newContent = yamlBlock + "\n" + existingContent
}
newContent := existingContent + "\n" + yamlBlock + "\n"
// Write back to file
err = os.WriteFile(indexPath, []byte(newContent), 0644)
@@ -193,7 +196,7 @@ func generateYAMLForModels(ctx context.Context, models []ProcessedModel, quantiz
return fmt.Errorf("failed to write %s: %w", indexPath, err)
}
fmt.Printf("Successfully prepended %d models to %s\n", len(yamlEntries), indexPath)
fmt.Printf("Successfully added %d models to %s\n", len(yamlEntries), indexPath)
}
return nil

View File

@@ -34,9 +34,6 @@ type ProcessedModel struct {
ReadmeContentPreview string `json:"readme_content_preview,omitempty"`
QuantizationPreferences []string `json:"quantization_preferences"`
ProcessingError string `json:"processing_error,omitempty"`
Tags []string `json:"tags,omitempty"`
License string `json:"license,omitempty"`
Icon string `json:"icon,omitempty"`
}
// SearchResult represents the complete result of searching and processing models
@@ -119,24 +116,14 @@ func main() {
}
fmt.Println(result.FormattedOutput)
var models []ProcessedModel
if len(result.Models) > 1 {
fmt.Println("More than one model found (", len(result.Models), "), using AI agent to select the most interesting models")
for _, model := range result.Models {
fmt.Println("Model: ", model.ModelID)
}
// Use AI agent to select the most interesting models
fmt.Println("Using AI agent to select the most interesting models...")
models, err = selectMostInterestingModels(context.Background(), result)
if err != nil {
fmt.Fprintf(os.Stderr, "Error in model selection: %v\n", err)
// Continue with original result if selection fails
models = result.Models
}
} else if len(result.Models) == 1 {
// Use AI agent to select the most interesting models
fmt.Println("Using AI agent to select the most interesting models...")
models, err := selectMostInterestingModels(context.Background(), result)
if err != nil {
fmt.Fprintf(os.Stderr, "Error in model selection: %v\n", err)
// Continue with original result if selection fails
models = result.Models
fmt.Println("Only one model found, using it directly")
}
fmt.Print(models)
@@ -167,7 +154,7 @@ func main() {
addedModelURLs = append(addedModelURLs, modelURL)
}
fmt.Println("Generating YAML entries for selected models...")
err = generateYAMLForModels(context.Background(), models, quantization)
err = generateYAMLForModels(context.Background(), models)
if err != nil {
fmt.Fprintf(os.Stderr, "Error generating YAML entries: %v\n", err)
os.Exit(1)
@@ -325,28 +312,9 @@ func searchAndProcessModels(searchTerm string, limit int, quantization string) (
outputBuilder.WriteString(fmt.Sprintf(" README Content Preview: %s\n",
processedModel.ReadmeContentPreview))
} else {
fmt.Printf(" Warning: Failed to get real readme: %v\n", err)
continue
}
fmt.Println("Real readme got", readmeContent)
// Extract metadata (tags, license) from README using LLM
fmt.Println("Extracting metadata for", model.ModelID, "waiting...")
tags, license, err := extractModelMetadata(context.Background(), processedModel)
if err == nil {
processedModel.Tags = tags
processedModel.License = license
outputBuilder.WriteString(fmt.Sprintf(" Tags: %v\n", tags))
outputBuilder.WriteString(fmt.Sprintf(" License: %s\n", license))
} else {
fmt.Printf(" Warning: Failed to extract metadata: %v\n", err)
}
// Extract icon from README or use HuggingFace avatar
icon := extractModelIcon(processedModel)
if icon != "" {
processedModel.Icon = icon
outputBuilder.WriteString(fmt.Sprintf(" Icon: %s\n", icon))
}
// Get README content
// readmeContent, err := client.GetReadmeContent(model.ModelID, details.ReadmeFile.Path)
// if err == nil {

View File

@@ -25,7 +25,7 @@ func runSyntheticMode() error {
// Generate YAML entries and append to gallery/index.yaml
fmt.Println("Generating YAML entries for synthetic models...")
err := generateYAMLForModels(context.Background(), models, "Q4_K_M")
err := generateYAMLForModels(context.Background(), models)
if err != nil {
return fmt.Errorf("error generating YAML entries: %w", err)
}
@@ -138,25 +138,6 @@ func (g *SyntheticDataGenerator) GenerateProcessedModel() ProcessedModel {
readmeContent := g.generateReadmeContent(modelName, author)
// Generate sample metadata
licenses := []string{"apache-2.0", "mit", "llama2", "gpl-3.0", "bsd", ""}
license := licenses[g.rand.Intn(len(licenses))]
sampleTags := []string{"llm", "gguf", "gpu", "cpu", "text-to-text", "chat", "instruction-tuned"}
numTags := g.rand.Intn(4) + 3 // 3-6 tags
tags := make([]string, numTags)
for i := 0; i < numTags; i++ {
tags[i] = sampleTags[g.rand.Intn(len(sampleTags))]
}
// Remove duplicates
tags = g.removeDuplicates(tags)
// Optionally include icon (50% chance)
icon := ""
if g.rand.Intn(2) == 0 {
icon = fmt.Sprintf("https://cdn-avatars.huggingface.co/v1/production/uploads/%s.png", g.randomString(24))
}
return ProcessedModel{
ModelID: modelID,
Author: author,
@@ -169,9 +150,6 @@ func (g *SyntheticDataGenerator) GenerateProcessedModel() ProcessedModel {
ReadmeContentPreview: truncateString(readmeContent, 200),
QuantizationPreferences: []string{"Q4_K_M", "Q4_K_S", "Q3_K_M", "Q2_K"},
ProcessingError: "",
Tags: tags,
License: license,
Icon: icon,
}
}
@@ -201,18 +179,6 @@ func (g *SyntheticDataGenerator) randomDate() string {
return pastDate.Format("2006-01-02T15:04:05.000Z")
}
func (g *SyntheticDataGenerator) removeDuplicates(slice []string) []string {
keys := make(map[string]bool)
result := []string{}
for _, item := range slice {
if !keys[item] {
keys[item] = true
result = append(result, item)
}
}
return result
}
func (g *SyntheticDataGenerator) generateReadmeContent(modelName, author string) string {
templates := []string{
fmt.Sprintf("# %s Model\n\nThis is a %s model developed by %s. It's designed for various natural language processing tasks including text generation, question answering, and conversation.\n\n## Features\n\n- High-quality text generation\n- Efficient inference\n- Multiple quantization options\n- Easy to use with LocalAI\n\n## Usage\n\nUse this model with LocalAI for various AI tasks.", strings.Title(modelName), modelName, author),

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View File

@@ -1,5 +1,5 @@
---
name: 'build backend container images (reusable)'
name: 'build python backend container images (reusable)'
on:
workflow_call:
@@ -53,11 +53,6 @@ on:
description: 'Skip drivers'
default: 'false'
type: string
ubuntu-version:
description: 'Ubuntu version'
required: false
default: '2204'
type: string
secrets:
dockerUsername:
required: false
@@ -102,7 +97,7 @@ jobs:
&& sudo apt-get install -y git
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
@@ -213,7 +208,6 @@ jobs:
CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
BASE_IMAGE=${{ inputs.base-image }}
BACKEND=${{ inputs.backend }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=gha
@@ -234,7 +228,6 @@ jobs:
CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
BASE_IMAGE=${{ inputs.base-image }}
BACKEND=${{ inputs.backend }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=gha

View File

@@ -50,7 +50,7 @@ jobs:
go-version: ['${{ inputs.go-version }}']
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
@@ -74,7 +74,7 @@ jobs:
BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend
- name: Upload ${{ inputs.backend }}.tar
uses: actions/upload-artifact@v6
uses: actions/upload-artifact@v5
with:
name: ${{ inputs.backend }}-tar
path: backend-images/${{ inputs.backend }}.tar
@@ -85,7 +85,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Download ${{ inputs.backend }}.tar
uses: actions/download-artifact@v7
uses: actions/download-artifact@v6
with:
name: ${{ inputs.backend }}-tar
path: .

View File

@@ -17,7 +17,7 @@ jobs:
has-backends-darwin: ${{ steps.set-matrix.outputs.has-backends-darwin }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Setup Bun
uses: oven-sh/setup-bun@v2
@@ -52,7 +52,6 @@ jobs:
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
@@ -70,7 +69,7 @@ jobs:
tag-suffix: ${{ matrix.tag-suffix }}
lang: ${{ matrix.lang || 'python' }}
use-pip: ${{ matrix.backend == 'diffusers' }}
runs-on: "macos-latest"
runs-on: "macOS-14"
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}

View File

@@ -11,7 +11,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go
@@ -25,7 +25,7 @@ jobs:
runs-on: macos-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go
@@ -37,7 +37,7 @@ jobs:
make build-launcher-darwin
ls -liah dist
- name: Upload macOS launcher artifacts
uses: actions/upload-artifact@v6
uses: actions/upload-artifact@v5
with:
name: launcher-macos
path: dist/
@@ -47,7 +47,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go
@@ -60,7 +60,7 @@ jobs:
sudo apt-get install golang gcc libgl1-mesa-dev xorg-dev libxkbcommon-dev
make build-launcher-linux
- name: Upload Linux launcher artifacts
uses: actions/upload-artifact@v6
uses: actions/upload-artifact@v5
with:
name: launcher-linux
path: local-ai-launcher-linux.tar.xz

View File

@@ -31,7 +31,7 @@ jobs:
file: "backend/go/piper/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v5
- name: Bump dependencies 🔧
id: bump
run: |
@@ -49,7 +49,7 @@ jobs:
rm -rfv ${{ matrix.variable }}_message.txt
rm -rfv ${{ matrix.variable }}_commit.txt
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI

View File

@@ -12,12 +12,12 @@ jobs:
- repository: "mudler/LocalAI"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v5
- name: Bump dependencies 🔧
run: |
bash .github/bump_docs.sh ${{ matrix.repository }}
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI

View File

@@ -15,7 +15,7 @@ jobs:
&& sudo add-apt-repository -y ppa:git-core/ppa \
&& sudo apt-get update \
&& sudo apt-get install -y git
- uses: actions/checkout@v6
- uses: actions/checkout@v5
- name: Install dependencies
run: |
sudo apt-get update
@@ -35,7 +35,7 @@ jobs:
sudo chmod 777 /hf_cache
bash .github/checksum_checker.sh gallery/index.yaml
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI

View File

@@ -14,13 +14,13 @@ jobs:
steps:
- name: Dependabot metadata
id: metadata
uses: dependabot/fetch-metadata@v2.5.0
uses: dependabot/fetch-metadata@v2.4.0
with:
github-token: "${{ secrets.GITHUB_TOKEN }}"
skip-commit-verification: true
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Approve a PR if not already approved
run: |

View File

@@ -15,7 +15,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- uses: actions/setup-go@v5
@@ -33,7 +33,7 @@ jobs:
run: |
CGO_ENABLED=0 make build
- name: rm
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.3
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}
@@ -53,7 +53,7 @@ jobs:
rm: true
target: ./local-ai
- name: restarting
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.3
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}

View File

@@ -30,7 +30,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
token: ${{ secrets.GITHUB_TOKEN }}
@@ -38,33 +38,20 @@ jobs:
uses: actions/setup-go@v5
with:
go-version: '1.21'
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- uses: mudler/localai-github-action@v1.1
with:
model: 'https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF'
- name: Run gallery agent
env:
#OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
OPENAI_MODE: Qwen_Qwen3-1.7B-GGUF
OPENAI_BASE_URL: "http://localhost:8080"
OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
#OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
SEARCH_TERM: ${{ github.event.inputs.search_term || 'GGUF' }}
LIMIT: ${{ github.event.inputs.limit || '15' }}
QUANTIZATION: ${{ github.event.inputs.quantization || 'Q4_K_M' }}
MAX_MODELS: ${{ github.event.inputs.max_models || '1' }}
run: |
export GALLERY_INDEX_PATH=$PWD/gallery/index.yaml
go run ./.github/gallery-agent
go run .github/gallery-agent
- name: Check for changes
id: check_changes
@@ -82,28 +69,28 @@ jobs:
id: read_summary
if: steps.check_changes.outputs.changes == 'true'
run: |
if [ -f "./gallery-agent-summary.json" ]; then
if [ -f ".github/gallery-agent/gallery-agent-summary.json" ]; then
echo "summary_exists=true" >> $GITHUB_OUTPUT
# Extract summary data using jq
echo "search_term=$(jq -r '.search_term' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "total_found=$(jq -r '.total_found' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "models_added=$(jq -r '.models_added' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "quantization=$(jq -r '.quantization' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "processing_time=$(jq -r '.processing_time' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "search_term=$(jq -r '.search_term' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "total_found=$(jq -r '.total_found' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "models_added=$(jq -r '.models_added' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "quantization=$(jq -r '.quantization' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
echo "processing_time=$(jq -r '.processing_time' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
# Create a formatted list of added models with URLs
added_models=$(jq -r 'range(0; .added_model_ids | length) as $i | "- [\(.added_model_ids[$i])](\(.added_model_urls[$i]))"' ./gallery-agent-summary.json | tr '\n' '\n')
added_models=$(jq -r 'range(0; .added_model_ids | length) as $i | "- [\(.added_model_ids[$i])](\(.added_model_urls[$i]))"' .github/gallery-agent/gallery-agent-summary.json | tr '\n' '\n')
echo "added_models<<EOF" >> $GITHUB_OUTPUT
echo "$added_models" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
rm -f ./gallery-agent-summary.json
rm -f .github/gallery-agent/gallery-agent-summary.json
else
echo "summary_exists=false" >> $GITHUB_OUTPUT
fi
- name: Create Pull Request
if: steps.check_changes.outputs.changes == 'true'
uses: peter-evans/create-pull-request@v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI

View File

@@ -16,7 +16,7 @@ jobs:
strategy:
matrix:
include:
- grpc-base-image: ubuntu:24.04
- grpc-base-image: ubuntu:22.04
runs-on: 'ubuntu-latest'
platforms: 'linux/amd64,linux/arm64'
runs-on: ${{matrix.runs-on}}
@@ -73,7 +73,7 @@ jobs:
uses: docker/setup-buildx-action@master
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Cache GRPC
uses: docker/build-push-action@v6

View File

@@ -15,8 +15,8 @@ jobs:
strategy:
matrix:
include:
- base-image: intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04
runs-on: 'arc-runner-set'
- base-image: intel/oneapi-basekit:2025.2.0-0-devel-ubuntu22.04
runs-on: 'ubuntu-latest'
platforms: 'linux/amd64'
runs-on: ${{matrix.runs-on}}
steps:
@@ -43,7 +43,7 @@ jobs:
uses: docker/setup-buildx-action@master
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Cache Intel images
uses: docker/build-push-action@v6
@@ -53,7 +53,7 @@ jobs:
BASE_IMAGE=${{ matrix.base-image }}
context: .
file: ./Dockerfile
tags: quay.io/go-skynet/intel-oneapi-base:24.04
tags: quay.io/go-skynet/intel-oneapi-base:latest
push: true
target: intel
platforms: ${{ matrix.platforms }}

View File

@@ -1,95 +1,68 @@
---
name: 'build container images tests'
on:
pull_request:
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
# Pushing with all jobs in parallel
# eats the bandwidth of all the nodes
max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
fail-fast: false
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'vulkan'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'false'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
name: 'build container images tests'
on:
pull_request:
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
# Pushing with all jobs in parallel
# eats the bandwidth of all the nodes
max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
fail-fast: false
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-22.04:6.4.3"
grpc-base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
grpc-base-image: "ubuntu:22.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
- build-type: 'vulkan'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=4 --output-sync=target"

View File

@@ -1,187 +1,154 @@
---
name: 'build container images'
on:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
hipblas-jobs:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
aio: ${{ matrix.aio }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-hipblas"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
core-image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
#max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
matrix:
include:
- build-type: ''
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
aio: "-aio-cpu"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-12"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-13"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'vulkan'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-vulkan"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'intel'
platforms: 'linux/amd64'
tag-latest: 'auto'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: '-gpu-intel'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-intel"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
gh-runner:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'true'
ubuntu-version: "2204"
ubuntu-codename: 'jammy'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
name: 'build container images'
on:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
hipblas-jobs:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
aio: ${{ matrix.aio }}
makeflags: ${{ matrix.makeflags }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-22.04:6.4.3"
grpc-base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-hipblas"
core-image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
#max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
matrix:
include:
- build-type: ''
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
aio: "-aio-cpu"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
- build-type: 'cublas'
cuda-major-version: "11"
cuda-minor-version: "7"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-11'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
aio: "-aio-gpu-nvidia-cuda-11"
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-12"
- build-type: 'vulkan'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-vulkan"
- build-type: 'intel'
platforms: 'linux/amd64'
tag-latest: 'auto'
base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
grpc-base-image: "ubuntu:22.04"
tag-suffix: '-gpu-intel'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-intel"
gh-runner:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'true'

View File

@@ -23,7 +23,7 @@ on:
type: string
cuda-minor-version:
description: 'CUDA minor version'
default: "9"
default: "4"
type: string
platforms:
description: 'Platforms'
@@ -56,16 +56,6 @@ on:
required: false
default: ''
type: string
ubuntu-version:
description: 'Ubuntu version'
required: false
default: '2204'
type: string
ubuntu-codename:
description: 'Ubuntu codename'
required: false
default: 'noble'
type: string
secrets:
dockerUsername:
required: true
@@ -104,7 +94,7 @@ jobs:
&& sudo apt-get update \
&& sudo apt-get install -y git
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
@@ -248,8 +238,6 @@ jobs:
GRPC_VERSION=v1.65.0
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
context: .
file: ./Dockerfile
cache-from: type=gha
@@ -277,8 +265,6 @@ jobs:
GRPC_VERSION=v1.65.0
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
context: .
file: ./Dockerfile
cache-from: type=gha

View File

@@ -14,7 +14,7 @@ jobs:
if: ${{ github.actor == 'localai-bot' && !contains(github.event.pull_request.title, 'chore(model gallery):') }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v5
- name: Approve a PR if not already approved
run: |

View File

@@ -15,7 +15,7 @@ jobs:
MODEL_NAME: gemma-3-12b-it-qat
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v5
with:
fetch-depth: 0 # needed to checkout all branches for this Action to work
ref: ${{ github.event.pull_request.head.sha }} # Checkout the PR head to get the actual changes
@@ -95,7 +95,7 @@ jobs:
MODEL_NAME: gemma-3-12b-it-qat
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v5
with:
fetch-depth: 0 # needed to checkout all branches for this Action to work
ref: ${{ github.event.pull_request.head.sha }} # Checkout the PR head to get the actual changes

View File

@@ -10,7 +10,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go
@@ -28,7 +28,7 @@ jobs:
runs-on: macos-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go
@@ -46,7 +46,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Set up Go

View File

@@ -14,7 +14,7 @@ jobs:
GO111MODULE: on
steps:
- name: Checkout Source
uses: actions/checkout@v6
uses: actions/checkout@v5
if: ${{ github.actor != 'dependabot[bot]' }}
- name: Run Gosec Security Scanner
if: ${{ github.actor != 'dependabot[bot]' }}

View File

@@ -10,7 +10,7 @@ jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v9
- uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v9
with:
stale-issue-message: 'This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
stale-pr-message: 'This PR is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 10 days.'

View File

@@ -19,7 +19,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v6
# uses: actions/checkout@v5
# with:
# submodules: true
# - name: Dependencies
@@ -40,7 +40,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Dependencies
@@ -61,7 +61,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Dependencies
@@ -83,7 +83,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Dependencies
@@ -104,7 +104,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v6
# uses: actions/checkout@v5
# with:
# submodules: true
# - name: Dependencies
@@ -124,7 +124,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v6
# uses: actions/checkout@v5
# with:
# submodules: true
# - name: Dependencies
@@ -186,7 +186,7 @@ jobs:
# sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
# df -h
# - name: Clone
# uses: actions/checkout@v6
# uses: actions/checkout@v5
# with:
# submodules: true
# - name: Dependencies
@@ -211,7 +211,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v6
# uses: actions/checkout@v5
# with:
# submodules: true
# - name: Dependencies
@@ -232,13 +232,13 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch espeak espeak-ng python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -247,98 +247,3 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/coqui
make --jobs=5 --output-sync=target -C backend/python/coqui test
tests-moonshine:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test moonshine
run: |
make --jobs=5 --output-sync=target -C backend/python/moonshine
make --jobs=5 --output-sync=target -C backend/python/moonshine test
tests-pocket-tts:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test pocket-tts
run: |
make --jobs=5 --output-sync=target -C backend/python/pocket-tts
make --jobs=5 --output-sync=target -C backend/python/pocket-tts test
tests-qwen-tts:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test qwen-tts
run: |
make --jobs=5 --output-sync=target -C backend/python/qwen-tts
make --jobs=5 --output-sync=target -C backend/python/qwen-tts test
tests-qwen-asr:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg sox
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test qwen-asr
run: |
make --jobs=5 --output-sync=target -C backend/python/qwen-asr
make --jobs=5 --output-sync=target -C backend/python/qwen-asr test
tests-voxcpm:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test voxcpm
run: |
make --jobs=5 --output-sync=target -C backend/python/voxcpm
make --jobs=5 --output-sync=target -C backend/python/voxcpm test

View File

@@ -70,7 +70,7 @@ jobs:
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Setup Go ${{ matrix.go-version }}
@@ -109,6 +109,11 @@ jobs:
sudo apt-get update
sudo apt-get install -y cuda-nvcc-${CUDA_VERSION} libcublas-dev-${CUDA_VERSION}
export CUDACXX=/usr/local/cuda/bin/nvcc
# The python3-grpc-tools package in 22.04 is too old
pip install --user grpcio-tools==1.71.0 grpcio==1.71.0
make -C backend/python/transformers
make backends/huggingface backends/llama-cpp backends/local-store backends/silero-vad backends/piper backends/whisper backends/stablediffusion-ggml
@@ -161,7 +166,7 @@ jobs:
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Dependencies
@@ -185,13 +190,13 @@ jobs:
limit-access-to-actor: true
tests-apple:
runs-on: macos-latest
runs-on: macOS-14
strategy:
matrix:
go-version: ['1.25.x']
steps:
- name: Clone
uses: actions/checkout@v6
uses: actions/checkout@v5
with:
submodules: true
- name: Setup Go ${{ matrix.go-version }}
@@ -205,7 +210,7 @@ jobs:
- name: Dependencies
run: |
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
pip install --user --no-cache-dir grpcio-tools grpcio
pip install --user --no-cache-dir grpcio-tools==1.71.0 grpcio==1.71.0
- name: Build llama-cpp-darwin
run: |
make protogen-go

View File

@@ -1,56 +0,0 @@
---
name: 'E2E Backend Tests'
on:
pull_request:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-tests-e2e-backend-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
tests-e2e-backend:
runs-on: ubuntu-latest
strategy:
matrix:
go-version: ['1.25.x']
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
cache: false
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential
- name: Test Backend E2E
run: |
PATH="$PATH:$HOME/go/bin" make build-mock-backend test-e2e
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
with:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true

View File

@@ -9,7 +9,7 @@ jobs:
fail-fast: false
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v5
- uses: actions/setup-go@v5
with:
go-version: 'stable'
@@ -25,7 +25,7 @@ jobs:
run: |
make protogen-go swagger
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI

3
.gitignore vendored
View File

@@ -25,7 +25,6 @@ go-bert
# LocalAI build binary
LocalAI
/local-ai
/local-ai-launcher
# prevent above rules from omitting the helm chart
!charts/*
# prevent above rules from omitting the api/localai folder
@@ -36,8 +35,6 @@ LocalAI
models/*
test-models/
test-dir/
tests/e2e-aio/backends
tests/e2e-aio/models
release/

View File

@@ -22,9 +22,6 @@ builds:
goarch:
- amd64
- arm64
ignore:
- goos: darwin
goarch: amd64
archives:
- formats: [ 'binary' ] # this removes the tar of the archives, leaving the binaries alone
name_template: local-ai-{{ .Tag }}-{{ .Os }}-{{ .Arch }}{{ if .Arm }}v{{ .Arm }}{{ end }}

290
AGENTS.md
View File

@@ -1,290 +0,0 @@
# Build and testing
Building and testing the project depends on the components involved and the platform where development is taking place. Due to the amount of context required it's usually best not to try building or testing the project unless the user requests it. If you must build the project then inspect the Makefile in the project root and the Makefiles of any backends that are effected by changes you are making. In addition the workflows in .github/workflows can be used as a reference when it is unclear how to build or test a component. The primary Makefile contains targets for building inside or outside Docker, if the user has not previously specified a preference then ask which they would like to use.
## Building a specified backend
Let's say the user wants to build a particular backend for a given platform. For example let's say they want to build coqui for ROCM/hipblas
- The Makefile has targets like `docker-build-coqui` created with `generate-docker-build-target` at the time of writing. Recently added backends may require a new target.
- At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
- Use .github/workflows/backend.yml as a reference it lists the needed args in the `include` job strategy matrix
- l4t and cublas also requires the CUDA major and minor version
- You can pretty print a command like `DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:6.4.4 make docker-build-coqui`
- Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
- The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.
- Sometimes the user may need extra parameters to be added to `docker build` (e.g. `--platform` for cross-platform builds or `--progress` to view the full logs), in which case you can generate the `docker build` command directly.
## Adding a New Backend
When adding a new backend to LocalAI, you need to update several files to ensure the backend is properly built, tested, and registered. Here's a step-by-step guide based on the pattern used for adding backends like `moonshine`:
### 1. Create Backend Directory Structure
Create the backend directory under the appropriate location:
- **Python backends**: `backend/python/<backend-name>/`
- **Go backends**: `backend/go/<backend-name>/`
- **C++ backends**: `backend/cpp/<backend-name>/`
For Python backends, you'll typically need:
- `backend.py` - Main gRPC server implementation
- `Makefile` - Build configuration
- `install.sh` - Installation script for dependencies
- `protogen.sh` - Protocol buffer generation script
- `requirements.txt` - Python dependencies
- `run.sh` - Runtime script
- `test.py` / `test.sh` - Test files
### 2. Add Build Configurations to `.github/workflows/backend.yml`
Add build matrix entries for each platform/GPU type you want to support. Look at similar backends (e.g., `chatterbox`, `faster-whisper`) for reference.
**Placement in file:**
- CPU builds: Add after other CPU builds (e.g., after `cpu-chatterbox`)
- CUDA 12 builds: Add after other CUDA 12 builds (e.g., after `gpu-nvidia-cuda-12-chatterbox`)
- CUDA 13 builds: Add after other CUDA 13 builds (e.g., after `gpu-nvidia-cuda-13-chatterbox`)
**Additional build types you may need:**
- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:6.4.4"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"`
- L4T (ARM): Use `build-type: 'l4t'` with `platforms: 'linux/arm64'` and `runs-on: 'ubuntu-24.04-arm'`
### 3. Add Backend Metadata to `backend/index.yaml`
**Step 3a: Add Meta Definition**
Add a YAML anchor definition in the `## metas` section (around line 2-300). Look for similar backends to use as a template such as `diffusers` or `chatterbox`
**Step 3b: Add Image Entries**
Add image entries at the end of the file, following the pattern of similar backends such as `diffusers` or `chatterbox`. Include both `latest` (production) and `master` (development) tags.
### 4. Update the Makefile
The Makefile needs to be updated in several places to support building and testing the new backend:
**Step 4a: Add to `.NOTPARALLEL`**
Add `backends/<backend-name>` to the `.NOTPARALLEL` line (around line 2) to prevent parallel execution conflicts:
```makefile
.NOTPARALLEL: ... backends/<backend-name>
```
**Step 4b: Add to `prepare-test-extra`**
Add the backend to the `prepare-test-extra` target (around line 312) to prepare it for testing:
```makefile
prepare-test-extra: protogen-python
...
$(MAKE) -C backend/python/<backend-name>
```
**Step 4c: Add to `test-extra`**
Add the backend to the `test-extra` target (around line 319) to run its tests:
```makefile
test-extra: prepare-test-extra
...
$(MAKE) -C backend/python/<backend-name> test
```
**Step 4d: Add Backend Definition**
Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:
**For Python backends with root context** (like `faster-whisper`, `coqui`):
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|python|.|false|true
```
**For Python backends with `./backend` context** (like `chatterbox`, `moonshine`):
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true
```
**For Go backends**:
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true
```
**Step 4e: Generate Docker Build Target**
Add an eval call to generate the docker-build target (around line 480-501):
```makefile
$(eval $(call generate-docker-build-target,$(BACKEND_<BACKEND_NAME>)))
```
**Step 4f: Add to `docker-build-backends`**
Add `docker-build-<backend-name>` to the `docker-build-backends` target (around line 507):
```makefile
docker-build-backends: ... docker-build-<backend-name>
```
**Determining the Context:**
- If the backend is in `backend/python/<backend-name>/` and uses `./backend` as context in the workflow file, use `./backend` context
- If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
- Check similar backends to determine the correct context
### 5. Verification Checklist
After adding a new backend, verify:
- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/workflows/backend.yml` for all desired platforms
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
- [ ] Makefile updated with all 6 required changes (`.NOTPARALLEL`, `prepare-test-extra`, `test-extra`, backend definition, docker-build target eval, `docker-build-backends`)
- [ ] No YAML syntax errors (check with linter)
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
### 6. Example: Adding a Python Backend
For reference, when `moonshine` was added:
- **Files created**: `backend/python/moonshine/{backend.py, Makefile, install.sh, protogen.sh, requirements.txt, run.sh, test.py, test.sh}`
- **Workflow entries**: 3 build configurations (CPU, CUDA 12, CUDA 13)
- **Index entries**: 1 meta definition + 6 image entries (cpu, cuda12, cuda13 × latest/development)
- **Makefile updates**:
- Added to `.NOTPARALLEL` line
- Added to `prepare-test-extra` and `test-extra` targets
- Added `BACKEND_MOONSHINE = moonshine|python|./backend|false|true`
- Added eval for docker-build target generation
- Added `docker-build-moonshine` to `docker-build-backends`
# Coding style
- The project has the following .editorconfig
```
root = true
[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true
[*.go]
indent_style = tab
[Makefile]
indent_style = tab
[*.proto]
indent_size = 2
[*.py]
indent_size = 4
[*.js]
indent_size = 2
[*.yaml]
indent_size = 2
[*.md]
trim_trailing_whitespace = false
```
- Use comments sparingly to explain why code does something, not what it does. Comments are there to add context that would be difficult to deduce from reading the code.
- Prefer modern Go e.g. use `any` not `interface{}`
# Logging
Use `github.com/mudler/xlog` for logging which has the same API as slog.
# llama.cpp Backend
The llama.cpp backend (`backend/cpp/llama-cpp/grpc-server.cpp`) is a gRPC adaptation of the upstream HTTP server (`llama.cpp/tools/server/server.cpp`). It uses the same underlying server infrastructure from `llama.cpp/tools/server/server-context.cpp`.
## Building and Testing
- Test llama.cpp backend compilation: `make backends/llama-cpp`
- The backend is built as part of the main build process
- Check `backend/cpp/llama-cpp/Makefile` for build configuration
## Architecture
- **grpc-server.cpp**: gRPC server implementation, adapts HTTP server patterns to gRPC
- Uses shared server infrastructure: `server-context.cpp`, `server-task.cpp`, `server-queue.cpp`, `server-common.cpp`
- The gRPC server mirrors the HTTP server's functionality but uses gRPC instead of HTTP
## Common Issues When Updating llama.cpp
When fixing compilation errors after upstream changes:
1. Check how `server.cpp` (HTTP server) handles the same change
2. Look for new public APIs or getter methods
3. Store copies of needed data instead of accessing private members
4. Update function calls to match new signatures
5. Test with `make backends/llama-cpp`
## Key Differences from HTTP Server
- gRPC uses `BackendServiceImpl` class with gRPC service methods
- HTTP server uses `server_routes` with HTTP handlers
- Both use the same `server_context` and task queue infrastructure
- gRPC methods: `LoadModel`, `Predict`, `PredictStream`, `Embedding`, `Rerank`, `TokenizeString`, `GetMetrics`, `Health`
## Tool Call Parsing Maintenance
When working on JSON/XML tool call parsing functionality, always check llama.cpp for reference implementation and updates:
### Checking for XML Parsing Changes
1. **Review XML Format Definitions**: Check `llama.cpp/common/chat-parser-xml-toolcall.h` for `xml_tool_call_format` struct changes
2. **Review Parsing Logic**: Check `llama.cpp/common/chat-parser-xml-toolcall.cpp` for parsing algorithm updates
3. **Review Format Presets**: Check `llama.cpp/common/chat-parser.cpp` for new XML format presets (search for `xml_tool_call_format form`)
4. **Review Model Lists**: Check `llama.cpp/common/chat.h` for `COMMON_CHAT_FORMAT_*` enum values that use XML parsing:
- `COMMON_CHAT_FORMAT_GLM_4_5`
- `COMMON_CHAT_FORMAT_MINIMAX_M2`
- `COMMON_CHAT_FORMAT_KIMI_K2`
- `COMMON_CHAT_FORMAT_QWEN3_CODER_XML`
- `COMMON_CHAT_FORMAT_APRIEL_1_5`
- `COMMON_CHAT_FORMAT_XIAOMI_MIMO`
- Any new formats added
### Model Configuration Options
Always check `llama.cpp` for new model configuration options that should be supported in LocalAI:
1. **Check Server Context**: Review `llama.cpp/tools/server/server-context.cpp` for new parameters
2. **Check Chat Params**: Review `llama.cpp/common/chat.h` for `common_chat_params` struct changes
3. **Check Server Options**: Review `llama.cpp/tools/server/server.cpp` for command-line argument changes
4. **Examples of options to check**:
- `ctx_shift` - Context shifting support
- `parallel_tool_calls` - Parallel tool calling
- `reasoning_format` - Reasoning format options
- Any new flags or parameters
### Implementation Guidelines
1. **Feature Parity**: Always aim for feature parity with llama.cpp's implementation
2. **Test Coverage**: Add tests for new features matching llama.cpp's behavior
3. **Documentation**: Update relevant documentation when adding new formats or options
4. **Backward Compatibility**: Ensure changes don't break existing functionality
### Files to Monitor
- `llama.cpp/common/chat-parser-xml-toolcall.h` - Format definitions
- `llama.cpp/common/chat-parser-xml-toolcall.cpp` - Parsing logic
- `llama.cpp/common/chat-parser.cpp` - Format presets and model-specific handlers
- `llama.cpp/common/chat.h` - Format enums and parameter structures
- `llama.cpp/tools/server/server-context.cpp` - Server configuration options
# Documentation
The project documentation is located in `docs/content`. When adding new features or changing existing functionality, it is crucial to update the documentation to reflect these changes. This helps users understand how to use the new capabilities and ensures the documentation stays relevant.
- **Feature Documentation**: If you add a new feature (like a new backend or API endpoint), create a new markdown file in `docs/content/features/` explaining what it is, how to configure it, and how to use it.
- **Configuration**: If you modify configuration options, update the relevant sections in `docs/content/`.
- **Examples**: providing concrete examples (like YAML configuration blocks) is highly encouraged to help users get started quickly.

View File

@@ -78,20 +78,6 @@ LOCALAI_IMAGE_TAG=test LOCALAI_IMAGE=local-ai-aio make run-e2e-aio
We are welcome the contribution of the documents, please open new PR or create a new issue. The documentation is available under `docs/` https://github.com/mudler/LocalAI/tree/master/docs
### Gallery YAML Schema
LocalAI provides a JSON Schema for gallery model YAML files at:
`core/schema/gallery-model.schema.json`
This schema mirrors the internal gallery model configuration and can be used by editors (such as VS Code) to enable autocomplete, validation, and inline documentation when creating or modifying gallery files.
To use it with the YAML language server, add the following comment at the top of a gallery YAML file:
```yaml
# yaml-language-server: $schema=../core/schema/gallery-model.schema.json
```
## Community and Communication
- You can reach out via the Github issue tracker.

View File

@@ -1,7 +1,6 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG INTEL_BASE_IMAGE=${BASE_IMAGE}
ARG UBUNTU_CODENAME=noble
FROM ${BASE_IMAGE} AS requirements
@@ -10,7 +9,7 @@ ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates curl wget espeak-ng libgomp1 \
ffmpeg libopenblas0 libopenblas-dev sox && \
ffmpeg libopenblas-base libopenblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -24,7 +23,6 @@ ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG TARGETVARIANT
ENV BUILD_TYPE=${BUILD_TYPE}
ARG UBUNTU_VERSION=2404
RUN mkdir -p /run/localai
RUN echo "default" > /run/localai/capability
@@ -35,45 +33,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils mesa-vulkan-drivers
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
echo "vulkan" > /run/localai/capability
@@ -82,19 +46,15 @@ EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
@@ -105,34 +65,26 @@ RUN <<EOT bash
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
echo "nvidia-cuda-${CUDA_MAJOR_VERSION}" > /run/localai/capability
echo "nvidia" > /run/localai/capability
fi
EOT
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
echo "nvidia-l4t-cuda-${CUDA_MAJOR_VERSION}" > /run/localai/capability
echo "nvidia-l4t" > /run/localai/capability
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu2204-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu2204-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu2204-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss
fi
EOT
@@ -176,12 +128,13 @@ ENV PATH=/opt/rocm/bin:${PATH}
# The requirements-core target is common to all images. It should not be placed in requirements-core unless every single build will use it.
FROM requirements-drivers AS build-requirements
ARG GO_VERSION=1.25.4
ARG CMAKE_VERSION=3.31.10
ARG GO_VERSION=1.22.6
ARG CMAKE_VERSION=3.26.4
ARG CMAKE_FROM_SOURCE=false
ARG TARGETARCH
ARG TARGETVARIANT
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
@@ -218,6 +171,14 @@ RUN go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2 && \
COPY --chmod=644 custom-ca-certs/* /usr/local/share/ca-certificates/
RUN update-ca-certificates
# OpenBLAS requirements and stable diffusion
RUN apt-get update && \
apt-get install -y --no-install-recommends \
libopenblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN test -n "$TARGETARCH" \
|| (echo 'warn: missing $TARGETARCH, either set this `ARG` manually, or run using `docker buildkit`')
@@ -238,10 +199,9 @@ WORKDIR /build
# https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/APT-Repository-not-working-signatures-invalid/m-p/1599436/highlight/true#M36143
# This is a temporary workaround until Intel fixes their repository
FROM ${INTEL_BASE_IMAGE} AS intel
ARG UBUNTU_CODENAME=noble
RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu ${UBUNTU_CODENAME}/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
RUN apt-get update && \
apt-get install -y --no-install-recommends \
intel-oneapi-runtime-libs && \
@@ -372,6 +332,6 @@ RUN mkdir -p /models /backends
HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
CMD curl -f ${HEALTHCHECK_ENDPOINT} || exit 1
VOLUME /models /backends /configuration
VOLUME /models /backends
EXPOSE 8080
ENTRYPOINT [ "/entrypoint.sh" ]

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE}

318
Makefile
View File

@@ -1,20 +1,12 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/moonshine backends/pocket-tts backends/qwen-tts backends/qwen-asr backends/voxcpm backends/whisperx
GOCMD=go
GOTEST=$(GOCMD) test
GOVET=$(GOCMD) vet
BINARY_NAME=local-ai
LAUNCHER_BINARY_NAME=local-ai-launcher
UBUNTU_VERSION?=2404
UBUNTU_CODENAME?=noble
GORELEASER?=
export BUILD_TYPE?=
export CUDA_MAJOR_VERSION?=13
export CUDA_MINOR_VERSION?=0
GO_TAGS?=
BUILD_ID?=
@@ -160,17 +152,7 @@ test: test-models/testmodel.ggml protogen-go
########################################################
docker-build-aio:
docker build \
--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
docker build --build-arg MAKEFLAGS="--jobs=5 --output-sync=target" -t local-ai:tests -f Dockerfile .
BASE_IMAGE=local-ai:tests DOCKER_AIO_IMAGE=local-ai-aio:test $(MAKE) docker-aio
e2e-aio:
@@ -189,29 +171,20 @@ run-e2e-aio: protogen-go
########################################################
prepare-e2e:
docker build \
--build-arg IMAGE_TYPE=core \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-t localai-tests .
mkdir -p $(TEST_DIR)
cp -rfv $(abspath ./tests/e2e-fixtures)/gpu.yaml $(TEST_DIR)/gpu.yaml
test -e $(TEST_DIR)/ggllm-test-model.bin || wget -q https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q2_K.gguf -O $(TEST_DIR)/ggllm-test-model.bin
docker build --build-arg IMAGE_TYPE=core --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg CUDA_MAJOR_VERSION=12 --build-arg CUDA_MINOR_VERSION=0 -t localai-tests .
run-e2e-image:
docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --name e2e-tests-$(RANDOM) localai-tests
ls -liah $(abspath ./tests/e2e-fixtures)
docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --gpus all --name e2e-tests-$(RANDOM) localai-tests
test-e2e: build-mock-backend prepare-e2e run-e2e-image
test-e2e:
@echo 'Running e2e tests'
BUILD_TYPE=$(BUILD_TYPE) \
LOCALAI_API=http://$(E2E_BRIDGE_IP):5390 \
LOCALAI_API=http://$(E2E_BRIDGE_IP):5390/v1 \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
$(MAKE) clean-mock-backend
$(MAKE) teardown-e2e
docker rmi localai-tests
teardown-e2e:
rm -rf $(TEST_DIR) || true
@@ -292,7 +265,7 @@ protoc:
echo "Unsupported OS: $$OS_NAME"; exit 1; \
fi; \
URL=https://github.com/protocolbuffers/protobuf/releases/download/v31.1/$$FILE; \
curl -L $$URL -o protoc.zip && \
curl -L -s $$URL -o protoc.zip && \
unzip -j -d $(CURDIR) protoc.zip bin/protoc && rm protoc.zip
.PHONY: protogen-go
@@ -311,33 +284,17 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/diffusers
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
$(MAKE) -C backend/python/qwen-tts
$(MAKE) -C backend/python/qwen-asr
$(MAKE) -C backend/python/voxcpm
$(MAKE) -C backend/python/whisperx
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
$(MAKE) -C backend/python/diffusers test
$(MAKE) -C backend/python/chatterbox test
$(MAKE) -C backend/python/vllm test
$(MAKE) -C backend/python/vllm-omni test
$(MAKE) -C backend/python/vibevoice test
$(MAKE) -C backend/python/moonshine test
$(MAKE) -C backend/python/pocket-tts test
$(MAKE) -C backend/python/qwen-tts test
$(MAKE) -C backend/python/qwen-asr test
$(MAKE) -C backend/python/voxcpm test
$(MAKE) -C backend/python/whisperx test
DOCKER_IMAGE?=local-ai
DOCKER_AIO_IMAGE?=local-ai-aio
IMAGE_TYPE?=core
BASE_IMAGE?=ubuntu:24.04
BASE_IMAGE?=ubuntu:22.04
docker:
docker build \
@@ -346,34 +303,24 @@ docker:
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
docker-cuda12:
docker-cuda11:
docker build \
--build-arg CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
--build-arg CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
--build-arg CUDA_MAJOR_VERSION=11 \
--build-arg CUDA_MINOR_VERSION=8 \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE)-cuda-12 .
-t $(DOCKER_IMAGE)-cuda-11 .
docker-aio:
@echo "Building AIO image with base $(BASE_IMAGE) as $(DOCKER_AIO_IMAGE)"
docker build \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_AIO_IMAGE) -f Dockerfile.aio .
docker-aio-all:
@@ -382,31 +329,60 @@ docker-aio-all:
docker-image-intel:
docker build \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
--build-arg BASE_IMAGE=quay.io/go-skynet/intel-oneapi-base:latest \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=intel \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
--build-arg BUILD_TYPE=intel -t $(DOCKER_IMAGE) .
########################################################
## Backends
########################################################
# Pattern rule for standard backends (docker-based)
# This matches all backends that use docker-build-* and docker-save-*
backends/%: docker-build-% docker-save-% build
./local-ai backends install "ocifile://$(abspath ./backend-images/$*.tar)"
# Darwin-specific backends (keep as explicit targets since they have special build logic)
backends/diffusers: docker-build-diffusers docker-save-diffusers build
./local-ai backends install "ocifile://$(abspath ./backend-images/diffusers.tar)"
backends/llama-cpp: docker-build-llama-cpp docker-save-llama-cpp build
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/piper: docker-build-piper docker-save-piper build
./local-ai backends install "ocifile://$(abspath ./backend-images/piper.tar)"
backends/stablediffusion-ggml: docker-build-stablediffusion-ggml docker-save-stablediffusion-ggml build
./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
backends/whisper: docker-build-whisper docker-save-whisper build
./local-ai backends install "ocifile://$(abspath ./backend-images/whisper.tar)"
backends/silero-vad: docker-build-silero-vad docker-save-silero-vad build
./local-ai backends install "ocifile://$(abspath ./backend-images/silero-vad.tar)"
backends/local-store: docker-build-local-store docker-save-local-store build
./local-ai backends install "ocifile://$(abspath ./backend-images/local-store.tar)"
backends/huggingface: docker-build-huggingface docker-save-huggingface build
./local-ai backends install "ocifile://$(abspath ./backend-images/huggingface.tar)"
backends/rfdetr: docker-build-rfdetr docker-save-rfdetr build
./local-ai backends install "ocifile://$(abspath ./backend-images/rfdetr.tar)"
backends/kitten-tts: docker-build-kitten-tts docker-save-kitten-tts build
./local-ai backends install "ocifile://$(abspath ./backend-images/kitten-tts.tar)"
backends/kokoro: docker-build-kokoro docker-save-kokoro build
./local-ai backends install "ocifile://$(abspath ./backend-images/kokoro.tar)"
backends/chatterbox: docker-build-chatterbox docker-save-chatterbox build
./local-ai backends install "ocifile://$(abspath ./backend-images/chatterbox.tar)"
backends/llama-cpp-darwin: build
bash ./scripts/build/llama-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/neutts: docker-build-neutts docker-save-neutts build
./local-ai backends install "ocifile://$(abspath ./backend-images/neutts.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
@@ -436,102 +412,112 @@ backends/stablediffusion-ggml-darwin:
backend-images:
mkdir -p backend-images
# Backend metadata: BACKEND_NAME | DOCKERFILE_TYPE | BUILD_CONTEXT | PROGRESS_FLAG | NEEDS_BACKEND_ARG
# llama-cpp is special - uses llama-cpp Dockerfile and doesn't need BACKEND arg
BACKEND_LLAMA_CPP = llama-cpp|llama-cpp|.|false|false
docker-build-llama-cpp:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:llama-cpp -f backend/Dockerfile.llama-cpp .
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
BACKEND_LOCAL_STORE = local-store|golang|.|false|true
BACKEND_HUGGINGFACE = huggingface|golang|.|false|true
BACKEND_SILERO_VAD = silero-vad|golang|.|false|true
BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|true
BACKEND_WHISPER = whisper|golang|.|false|true
docker-build-bark-cpp:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark-cpp -f backend/Dockerfile.golang --build-arg BACKEND=bark-cpp .
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
BACKEND_TRANSFORMERS = transformers|python|.|false|true
BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
BACKEND_COQUI = coqui|python|.|false|true
BACKEND_RFDETR = rfdetr|python|.|false|true
BACKEND_KITTEN_TTS = kitten-tts|python|.|false|true
BACKEND_NEUTTS = neutts|python|.|false|true
BACKEND_KOKORO = kokoro|python|.|false|true
BACKEND_VLLM = vllm|python|.|false|true
BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
BACKEND_CHATTERBOX = chatterbox|python|.|false|true
BACKEND_VIBEVOICE = vibevoice|python|.|--progress=plain|true
BACKEND_MOONSHINE = moonshine|python|.|false|true
BACKEND_POCKET_TTS = pocket-tts|python|.|false|true
BACKEND_QWEN_TTS = qwen-tts|python|.|false|true
BACKEND_QWEN_ASR = qwen-asr|python|.|false|true
BACKEND_VOXCPM = voxcpm|python|.|false|true
BACKEND_WHISPERX = whisperx|python|.|false|true
docker-build-piper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:piper -f backend/Dockerfile.golang --build-arg BACKEND=piper .
# Helper function to build docker image for a backend
# Usage: $(call docker-build-backend,BACKEND_NAME,DOCKERFILE_TYPE,BUILD_CONTEXT,PROGRESS_FLAG,NEEDS_BACKEND_ARG)
define docker-build-backend
docker build $(if $(filter-out false,$(4)),$(4)) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
$(if $(filter true,$(5)),--build-arg BACKEND=$(1)) \
-t local-ai-backend:$(1) -f backend/Dockerfile.$(2) $(3)
endef
docker-build-local-store:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:local-store -f backend/Dockerfile.golang --build-arg BACKEND=local-store .
# Generate docker-build targets from backend definitions
define generate-docker-build-target
docker-build-$(word 1,$(subst |, ,$(1))):
$$(call docker-build-backend,$(word 1,$(subst |, ,$(1))),$(word 2,$(subst |, ,$(1))),$(word 3,$(subst |, ,$(1))),$(word 4,$(subst |, ,$(1))),$(word 5,$(subst |, ,$(1))))
endef
docker-build-huggingface:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:huggingface -f backend/Dockerfile.golang --build-arg BACKEND=huggingface .
# Generate all docker-build targets
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_SILERO_VAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRANSFORMERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_COQUI)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR)))
$(eval $(call generate-docker-build-target,$(BACKEND_KITTEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOONSHINE)))
$(eval $(call generate-docker-build-target,$(BACKEND_POCKET_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_ASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
docker-build-rfdetr:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rfdetr -f backend/Dockerfile.python --build-arg BACKEND=rfdetr ./backend
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-kitten-tts:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kitten-tts -f backend/Dockerfile.python --build-arg BACKEND=kitten-tts ./backend
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-qwen-asr docker-build-voxcpm docker-build-whisperx
docker-save-kitten-tts: backend-images
docker save local-ai-backend:kitten-tts -o backend-images/kitten-tts.tar
########################################################
### Mock Backend for E2E Tests
########################################################
docker-save-chatterbox: backend-images
docker save local-ai-backend:chatterbox -o backend-images/chatterbox.tar
build-mock-backend: protogen-go
$(GOCMD) build -o tests/e2e/mock-backend/mock-backend ./tests/e2e/mock-backend
docker-build-neutts:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:neutts -f backend/Dockerfile.python --build-arg BACKEND=neutts ./backend
clean-mock-backend:
rm -f tests/e2e/mock-backend/mock-backend
docker-save-neutts: backend-images
docker save local-ai-backend:neutts -o backend-images/neutts.tar
docker-build-kokoro:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kokoro -f backend/Dockerfile.python --build-arg BACKEND=kokoro ./backend
docker-save-kokoro: backend-images
docker save local-ai-backend:kokoro -o backend-images/kokoro.tar
docker-save-rfdetr: backend-images
docker save local-ai-backend:rfdetr -o backend-images/rfdetr.tar
docker-save-huggingface: backend-images
docker save local-ai-backend:huggingface -o backend-images/huggingface.tar
docker-save-local-store: backend-images
docker save local-ai-backend:local-store -o backend-images/local-store.tar
docker-build-silero-vad:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:silero-vad -f backend/Dockerfile.golang --build-arg BACKEND=silero-vad .
docker-save-silero-vad: backend-images
docker save local-ai-backend:silero-vad -o backend-images/silero-vad.tar
docker-save-piper: backend-images
docker save local-ai-backend:piper -o backend-images/piper.tar
docker-save-llama-cpp: backend-images
docker save local-ai-backend:llama-cpp -o backend-images/llama-cpp.tar
docker-save-bark-cpp: backend-images
docker save local-ai-backend:bark-cpp -o backend-images/bark-cpp.tar
docker-build-stablediffusion-ggml:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:stablediffusion-ggml -f backend/Dockerfile.golang --build-arg BACKEND=stablediffusion-ggml .
docker-save-stablediffusion-ggml: backend-images
docker save local-ai-backend:stablediffusion-ggml -o backend-images/stablediffusion-ggml.tar
docker-build-rerankers:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rerankers -f backend/Dockerfile.python --build-arg BACKEND=rerankers .
docker-build-vllm:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:vllm -f backend/Dockerfile.python --build-arg BACKEND=vllm .
docker-build-transformers:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:transformers -f backend/Dockerfile.python --build-arg BACKEND=transformers .
docker-build-diffusers:
docker build --progress=plain --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:diffusers -f backend/Dockerfile.python --build-arg BACKEND=diffusers ./backend
docker-save-diffusers: backend-images
docker save local-ai-backend:diffusers -o backend-images/diffusers.tar
docker-build-whisper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:whisper -f backend/Dockerfile.golang --build-arg BACKEND=whisper .
docker-save-whisper: backend-images
docker save local-ai-backend:whisper -o backend-images/whisper.tar
docker-build-faster-whisper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:faster-whisper -f backend/Dockerfile.python --build-arg BACKEND=faster-whisper .
docker-build-coqui:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:coqui -f backend/Dockerfile.python --build-arg BACKEND=coqui .
docker-build-bark:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark -f backend/Dockerfile.python --build-arg BACKEND=bark .
docker-build-chatterbox:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:chatterbox -f backend/Dockerfile.python --build-arg BACKEND=chatterbox ./backend
docker-build-exllama2:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:exllama2 -f backend/Dockerfile.python --build-arg BACKEND=exllama2 .
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-bark docker-build-chatterbox docker-build-exllama2
########################################################
### END Backends

138
README.md
View File

@@ -33,7 +33,7 @@
<img src="https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge&logo=X&logoColor=white&label=LocalAI_API" alt="Follow LocalAI_API"/>
</a>
<a href="https://discord.gg/uJAeKSAGDy" target="blank">
<img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Discord&style=for-the-badge&query=approximate_member_count&url=https%3A%2F%2Fdiscordapp.com%2Fapi%2Finvites%2FuJAeKSAGDy%3Fwith_counts%3Dtrue&logo=discord" alt="Join LocalAI Discord Community"/>
<img src="https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted" alt="Join LocalAI Discord Community"/>
</a>
</p>
@@ -51,29 +51,37 @@
**LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI (Elevenlabs, Anthropic... ) API specifications for local AI inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU. It is created and maintained by [Ettore Di Giacinto](https://github.com/mudler).
## Local Stack Family
## 📚🆕 Local Stack Family
Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tools, you might also like:
🆕 LocalAI is now part of a comprehensive suite of AI tools designed to work together:
- **[LocalAGI](https://github.com/mudler/LocalAGI)** - AI agent orchestration platform with OpenAI Responses API compatibility and advanced agentic capabilities
- **[LocalRecall](https://github.com/mudler/LocalRecall)** - MCP/REST API knowledge base system providing persistent memory and storage for AI agents
- 🆕 **[Cogito](https://github.com/mudler/cogito)** - Go library for building intelligent, co-operative agentic software and LLM-powered workflows, focusing on improving results for small, open source language models that scales to any LLM. Powers LocalAGI and LocalAI MCP/Agentic capabilities
- 🆕 **[Wiz](https://github.com/mudler/wiz)** - Terminal-based AI agent accessible via Ctrl+Space keybinding. Portable, local-LLM friendly shell assistant with TUI/CLI modes, tool execution with approval, MCP protocol support, and multi-shell compatibility (zsh, bash, fish)
- 🆕 **[SkillServer](https://github.com/mudler/skillserver)** - Simple, centralized skills database for AI agents via MCP. Manages skills as Markdown files with MCP server integration, web UI for editing, Git synchronization, and full-text search capabilities
<table>
<tr>
<td width="50%" valign="top">
<a href="https://github.com/mudler/LocalAGI">
<img src="https://raw.githubusercontent.com/mudler/LocalAGI/refs/heads/main/webui/react-ui/public/logo_2.png" width="300" alt="LocalAGI Logo">
</a>
</td>
<td width="50%" valign="top">
<h3><a href="https://github.com/mudler/LocalAGI">LocalAGI</a></h3>
<p>A powerful Local AI agent management platform that serves as a drop-in replacement for OpenAI's Responses API, enhanced with advanced agentic capabilities.</p>
</td>
</tr>
<tr>
<td width="50%" valign="top">
<a href="https://github.com/mudler/LocalRecall">
<img src="https://raw.githubusercontent.com/mudler/LocalRecall/refs/heads/main/static/localrecall_horizontal.png" width="300" alt="LocalRecall Logo">
</a>
</td>
<td width="50%" valign="top">
<h3><a href="https://github.com/mudler/LocalRecall">LocalRecall</a></h3>
<p>A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.</p>
</td>
</tr>
</table>
## Screenshots
## Screenshots / Video
### Youtube video
<h1 align="center">
<br>
<a href="https://www.youtube.com/watch?v=PDqYhB9nNHA" target="_blank"> <img width="300" src="https://img.youtube.com/vi/PDqYhB9nNHA/0.jpg"> </a><br>
<br>
</h1>
### Screenshots
| Talk Interface | Generate Audio |
| --- | --- |
@@ -93,8 +101,6 @@ Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tool
## 💻 Quickstart
> ⚠️ **Note:** The `install.sh` script is currently experiencing issues due to the heavy changes currently undergoing in LocalAI and may produce broken or misconfigured installations. Please use Docker installation (see below) or manual binary installation until [issue #8032](https://github.com/mudler/LocalAI/issues/8032) is resolved.
Run the installer script:
```bash
@@ -102,7 +108,7 @@ Run the installer script:
curl https://localai.io/install.sh | sh
```
For more installation options, see [Installer Options](https://localai.io/installation/).
For more installation options, see [Installer Options](https://localai.io/docs/advanced/installer/).
### macOS Download:
@@ -112,7 +118,7 @@ For more installation options, see [Installer Options](https://localai.io/instal
> Note: the DMGs are not signed by Apple as quarantined. See https://github.com/mudler/LocalAI/issues/6268 for a workaround, fix is tracked here: https://github.com/mudler/LocalAI/issues/6244
### Containers (Docker, podman, ...)
Or run with docker:
> **💡 Docker Run vs Docker Start**
>
@@ -121,59 +127,55 @@ For more installation options, see [Installer Options](https://localai.io/instal
>
> If you've already run LocalAI before and want to start it again, use: `docker start -i local-ai`
#### CPU only image:
### CPU only image:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
```
#### NVIDIA GPU Images:
### NVIDIA GPU Images:
```bash
# CUDA 13.0
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13
# CUDA 12.0
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
# NVIDIA Jetson (L4T) ARM64
# CUDA 12 (for Nvidia AGX Orin and similar platforms)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
# CUDA 11.7
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-11
# CUDA 13 (for Nvidia DGX Spark)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13
# NVIDIA Jetson (L4T) ARM64
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
```
#### AMD GPU Images (ROCm):
### AMD GPU Images (ROCm):
```bash
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
```
#### Intel GPU Images (oneAPI):
### Intel GPU Images (oneAPI):
```bash
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
```
#### Vulkan GPU Images:
### Vulkan GPU Images:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
```
#### AIO Images (pre-downloaded models):
### AIO Images (pre-downloaded models):
```bash
# CPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-cpu
# NVIDIA CUDA 13 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-13
# NVIDIA CUDA 12 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-12
# NVIDIA CUDA 11 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-11
# Intel GPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel
@@ -204,8 +206,6 @@ For more information, see [💻 Getting started](https://localai.io/basics/getti
## 📰 Latest project news
- December 2025: [Dynamic Memory Resource reclaimer](https://github.com/mudler/LocalAI/pull/7583), [Automatic fitting of models to multiple GPUS(llama.cpp)](https://github.com/mudler/LocalAI/pull/7584), [Added Vibevoice backend](https://github.com/mudler/LocalAI/pull/7494)
- November 2025: Major improvements to the UX. Among these: [Import models via URL](https://github.com/mudler/LocalAI/pull/7245) and [Multiple chats and history](https://github.com/mudler/LocalAI/pull/7325)
- October 2025: 🔌 [Model Context Protocol (MCP)](https://localai.io/docs/features/mcp/) support added for agentic capabilities with external tools
- September 2025: New Launcher application for MacOS and Linux, extended support to many backends for Mac and Nvidia L4T devices. Models: Added MLX-Audio, WAN 2.2. WebUI improvements and Python-based backends now ships portable python environments.
- August 2025: MLX, MLX-VLM, Diffusers and llama.cpp are now supported on Mac M1/M2/M3+ chips ( with `development` suffix in the gallery ): https://github.com/mudler/LocalAI/pull/6049 https://github.com/mudler/LocalAI/pull/6119 https://github.com/mudler/LocalAI/pull/6121 https://github.com/mudler/LocalAI/pull/6060
@@ -239,7 +239,6 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
- 🔈 [Audio to Text](https://localai.io/features/audio-to-text/) (Audio transcription with `whisper.cpp`)
- 🎨 [Image generation](https://localai.io/features/image-generation)
- 🔥 [OpenAI-alike tools API](https://localai.io/features/openai-functions/)
- ⚡ [Realtime API](https://localai.io/features/openai-realtime/) (Speech-to-speech)
- 🧠 [Embeddings generation for vector databases](https://localai.io/features/embeddings/)
- ✍️ [Constrained grammars](https://localai.io/features/constrained_grammars/)
- 🖼️ [Download Models directly from Huggingface ](https://localai.io/models/)
@@ -258,39 +257,39 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
### Text Generation & Language Models
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **llama.cpp** | LLM inference in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, CPU |
| **vLLM** | Fast LLM inference with PagedAttention | CUDA 12/13, ROCm, Intel |
| **transformers** | HuggingFace transformers framework | CUDA 12/13, ROCm, Intel, CPU |
| **llama.cpp** | LLM inference in C/C++ | CUDA 11/12, ROCm, Intel SYCL, Vulkan, Metal, CPU |
| **vLLM** | Fast LLM inference with PagedAttention | CUDA 12, ROCm, Intel |
| **transformers** | HuggingFace transformers framework | CUDA 11/12, ROCm, Intel, CPU |
| **exllama2** | GPTQ inference library | CUDA 12 |
| **MLX** | Apple Silicon LLM inference | Metal (M1/M2/M3+) |
| **MLX-VLM** | Apple Silicon Vision-Language Models | Metal (M1/M2/M3+) |
### Audio & Speech Processing
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **whisper.cpp** | OpenAI Whisper in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, CPU |
| **faster-whisper** | Fast Whisper with CTranslate2 | CUDA 12/13, ROCm, Intel, CPU |
| **coqui** | Advanced TTS with 1100+ languages | CUDA 12/13, ROCm, Intel, CPU |
| **kokoro** | Lightweight TTS model | CUDA 12/13, ROCm, Intel, CPU |
| **chatterbox** | Production-grade TTS | CUDA 12/13, CPU |
| **whisper.cpp** | OpenAI Whisper in C/C++ | CUDA 12, ROCm, Intel SYCL, Vulkan, CPU |
| **faster-whisper** | Fast Whisper with CTranslate2 | CUDA 12, ROCm, Intel, CPU |
| **bark** | Text-to-audio generation | CUDA 12, ROCm, Intel |
| **bark-cpp** | C++ implementation of Bark | CUDA, Metal, CPU |
| **coqui** | Advanced TTS with 1100+ languages | CUDA 12, ROCm, Intel, CPU |
| **kokoro** | Lightweight TTS model | CUDA 12, ROCm, Intel, CPU |
| **chatterbox** | Production-grade TTS | CUDA 11/12, CPU |
| **piper** | Fast neural TTS system | CPU |
| **kitten-tts** | Kitten TTS models | CPU |
| **silero-vad** | Voice Activity Detection | CPU |
| **neutts** | Text-to-speech with voice cloning | CUDA 12/13, ROCm, CPU |
| **vibevoice** | Real-time TTS with voice cloning | CUDA 12/13, ROCm, Intel, CPU |
| **pocket-tts** | Lightweight CPU-based TTS | CUDA 12/13, ROCm, Intel, CPU |
| **qwen-tts** | High-quality TTS with custom voice, voice design, and voice cloning | CUDA 12/13, ROCm, Intel, CPU |
| **neutts** | Text-to-speech with voice cloning | CUDA 12, ROCm, CPU |
### Image & Video Generation
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **stablediffusion.cpp** | Stable Diffusion in C/C++ | CUDA 12/13, Intel SYCL, Vulkan, CPU |
| **diffusers** | HuggingFace diffusion models | CUDA 12/13, ROCm, Intel, Metal, CPU |
| **stablediffusion.cpp** | Stable Diffusion in C/C++ | CUDA 12, Intel SYCL, Vulkan, CPU |
| **diffusers** | HuggingFace diffusion models | CUDA 11/12, ROCm, Intel, Metal, CPU |
### Specialized AI Tasks
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **rfdetr** | Real-time object detection | CUDA 12/13, Intel, CPU |
| **rerankers** | Document reranking API | CUDA 12/13, ROCm, Intel, CPU |
| **rfdetr** | Real-time object detection | CUDA 12, Intel, CPU |
| **rerankers** | Document reranking API | CUDA 11/12, ROCm, Intel, CPU |
| **local-store** | Vector database | CPU |
| **huggingface** | HuggingFace API integration | API-based |
@@ -298,14 +297,13 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
| Acceleration Type | Supported Backends | Hardware Support |
|-------------------|-------------------|------------------|
| **NVIDIA CUDA 11** | llama.cpp, whisper, stablediffusion, diffusers, rerankers, bark, chatterbox | Nvidia hardware |
| **NVIDIA CUDA 12** | All CUDA-compatible backends | Nvidia hardware |
| **NVIDIA CUDA 13** | All CUDA-compatible backends | Nvidia hardware |
| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, neutts, vibevoice, pocket-tts, qwen-tts | AMD Graphics |
| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, coqui, kokoro, vibevoice, pocket-tts, qwen-tts | Intel Arc, Intel iGPUs |
| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM | Apple M1/M2/M3+ |
| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, bark, neutts | AMD Graphics |
| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, exllama2, coqui, kokoro, bark | Intel Arc, Intel iGPUs |
| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM, bark-cpp | Apple M1/M2/M3+ |
| **Vulkan** | llama.cpp, whisper, stablediffusion | Cross-platform GPUs |
| **NVIDIA Jetson (CUDA 12)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (AGX Orin, etc.) |
| **NVIDIA Jetson (CUDA 13)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (DGX Spark) |
| **NVIDIA Jetson** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI |
| **CPU Optimized** | All backends | AVX/AVX2/AVX512, quantization support |
### 🔗 Community and integrations
@@ -324,10 +322,6 @@ Agentic Libraries:
MCPs:
- https://github.com/mudler/MCPs
OS Assistant:
- https://github.com/mudler/Keygeist - Keygeist is an AI-powered keyboard operator that listens for key combinations and responds with AI-generated text typed directly into your Linux box.
Model galleries
- https://github.com/go-skynet/model-gallery
@@ -402,10 +396,6 @@ A huge thank you to our generous sponsors who support this project covering CI e
</a>
</p>
### Individual sponsors
A special thanks to individual sponsors that contributed to the project, a full list is in [Github](https://github.com/sponsors/mudler) and [buymeacoffee](https://buymeacoffee.com/mudler), a special shout out goes to [drikster80](https://github.com/drikster80) for being generous. Thank you everyone!
## 🌟 Star history
[![LocalAI Star history Chart](https://api.star-history.com/svg?repos=go-skynet/LocalAI&type=Date)](https://star-history.com/#go-skynet/LocalAI&Date)

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -12,15 +12,14 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG GO_VERSION=1.22.6
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \
ca-certificates \
make cmake wget \
make cmake \
curl unzip \
libssl-dev && \
apt-get clean && \
@@ -33,52 +32,17 @@ ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -86,19 +50,15 @@ EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
@@ -109,31 +69,12 @@ RUN <<EOT bash
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
@@ -182,8 +123,6 @@ EOT
COPY . /LocalAI
RUN git config --global --add safe.directory /LocalAI
RUN cd /LocalAI && make protogen-go && make -C /LocalAI/backend/go/${BACKEND} build
FROM scratch

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
@@ -10,8 +10,7 @@ FROM ${GRPC_BASE_IMAGE} AS grpc
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG CMAKE_VERSION=3.26.4
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
@@ -21,13 +20,13 @@ RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
git && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
@@ -51,13 +50,6 @@ RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shall
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
@@ -69,8 +61,7 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG GO_VERSION=1.22.6
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -78,9 +69,8 @@ RUN apt-get update && \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
libssl-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -90,52 +80,17 @@ ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -143,19 +98,15 @@ EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
@@ -166,31 +117,12 @@ RUN <<EOT bash
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
@@ -232,7 +164,7 @@ EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
@@ -248,30 +180,19 @@ COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
fi
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
else
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-avx
make llama-cpp-avx2
make llama-cpp-avx512
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
fi
## Otherwise just run the normal build
RUN <<EOT bash
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then \
cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-fallback && \
make llama-cpp-grpc && make llama-cpp-rpc-server; \
else \
cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-avx && \
make llama-cpp-avx2 && \
make llama-cpp-avx512 && \
make llama-cpp-fallback && \
make llama-cpp-grpc && \
make llama-cpp-rpc-server; \
fi
EOT

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -12,7 +12,6 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -22,7 +21,7 @@ RUN apt-get update && \
espeak-ng \
curl \
libssl-dev \
git wget \
git \
git-lfs \
unzip clang \
upx-ucl \
@@ -31,15 +30,8 @@ RUN apt-get update && \
python3-dev llvm \
python3-venv make cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user --upgrade pip
else
pip install --upgrade pip
fi
EOT
rm -rf /var/lib/apt/lists/* && \
pip install --upgrade pip
# Cuda
@@ -54,45 +46,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -100,19 +58,15 @@ EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
@@ -123,31 +77,12 @@ RUN <<EOT bash
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
@@ -168,40 +103,21 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ]; then \
ln -s /opt/rocm-**/lib/llvm/lib/libomp.so /usr/lib/libomp.so \
; fi
# Install uv as a system package
RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
ENV PATH="/root/.cargo/bin:${PATH}"
# Increase timeout for uv installs behind slow networks
ENV UV_HTTP_TIMEOUT=180
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
# Install grpcio-tools (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user grpcio-tools==1.71.0 grpcio==1.71.0
else
pip install grpcio-tools==1.71.0 grpcio==1.71.0
fi
EOT
RUN pip install --user grpcio-tools==1.71.0 grpcio==1.71.0
COPY backend/python/${BACKEND} /${BACKEND}
COPY backend/backend.proto /${BACKEND}/backend.proto
COPY backend/python/common/ /${BACKEND}/common
COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
COPY python/${BACKEND} /${BACKEND}
COPY backend.proto /${BACKEND}/backend.proto
COPY python/common/ /${BACKEND}/common
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory
RUN mkdir -p /${BACKEND}/lib && \
TARGET_LIB_DIR="/${BACKEND}/lib" BUILD_TYPE="${BUILD_TYPE}" CUDA_MAJOR_VERSION="${CUDA_MAJOR_VERSION}" \
bash /package-gpu-libs.sh "/${BACKEND}/lib"
FROM scratch
ARG BACKEND=rerankers
COPY --from=builder /${BACKEND}/ /

View File

@@ -46,7 +46,7 @@ The backend system provides language-specific Dockerfiles that handle the build
- **vllm**: High-performance LLM inference
- **mlx**: Apple Silicon optimization
- **diffusers**: Stable Diffusion models
- **Audio**: coqui, faster-whisper, kitten-tts
- **Audio**: bark, coqui, faster-whisper, kitten-tts
- **Vision**: mlx-vlm, rfdetr
- **Specialized**: rerankers, chatterbox, kokoro
@@ -55,6 +55,7 @@ The backend system provides language-specific Dockerfiles that handle the build
- **stablediffusion-ggml**: Stable Diffusion in Go with GGML Cpp backend
- **huggingface**: Hugging Face model integration
- **piper**: Text-to-speech synthesis Golang with C bindings using rhaspy/piper
- **bark-cpp**: Bark TTS models Golang with Cpp bindings
- **local-store**: Vector storage backend
#### C++ Backends (`cpp/`)
@@ -64,7 +65,7 @@ The backend system provides language-specific Dockerfiles that handle the build
## Hardware Acceleration Support
### CUDA (NVIDIA)
- **Versions**: CUDA 12.x, 13.x
- **Versions**: CUDA 11.x, 12.x
- **Features**: cuBLAS, cuDNN, TensorRT optimization
- **Targets**: x86_64, ARM64 (Jetson)
@@ -131,7 +132,8 @@ For ARM64/Mac builds, docker can't be used, and the makefile in the respective b
### Build Types
- **`cpu`**: CPU-only optimization
- **`cublas12`**, **`cublas13`**: CUDA 12.x, 13.x with cuBLAS
- **`cublas11`**: CUDA 11.x with cuBLAS
- **`cublas12`**: CUDA 12.x with cuBLAS
- **`hipblas`**: ROCm with rocBLAS
- **`intel`**: Intel oneAPI optimization
- **`vulkan`**: Vulkan-based acceleration
@@ -208,4 +210,4 @@ When contributing to the backend system:
2. **Add Tests**: Include comprehensive test coverage
3. **Document**: Provide clear usage examples
4. **Optimize**: Consider performance and resource usage
5. **Validate**: Test across different hardware targets
5. **Validate**: Test across different hardware targets

View File

@@ -17,7 +17,6 @@ service Backend {
rpc GenerateVideo(GenerateVideoRequest) returns (Result) {}
rpc AudioTranscription(TranscriptRequest) returns (TranscriptResult) {}
rpc TTS(TTSRequest) returns (Result) {}
rpc TTSStream(TTSRequest) returns (stream Reply) {}
rpc SoundGeneration(SoundGenerationRequest) returns (Result) {}
rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
rpc Status(HealthMessage) returns (StatusResponse) {}
@@ -33,8 +32,6 @@ service Backend {
rpc GetMetrics(MetricsRequest) returns (MetricsResponse);
rpc VAD(VADRequest) returns (VADResponse) {}
rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
}
// Define the empty request
@@ -285,7 +282,6 @@ message TranscriptRequest {
uint32 threads = 4;
bool translate = 5;
bool diarize = 6;
string prompt = 7;
}
message TranscriptResult {
@@ -299,12 +295,12 @@ message TranscriptSegment {
int64 end = 3;
string text = 4;
repeated int32 tokens = 5;
string speaker = 6;
}
message GenerateImageRequest {
int32 height = 1;
int32 width = 2;
int32 mode = 3;
int32 step = 4;
int32 seed = 5;
string positive_prompt = 6;
@@ -414,8 +410,3 @@ message Detection {
message DetectResponse {
repeated Detection Detections = 1;
}
message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)
}

View File

@@ -57,7 +57,7 @@ add_library(hw_grpc_proto
${hw_proto_srcs}
${hw_proto_hdrs} )
add_executable(${TARGET} grpc-server.cpp json.hpp httplib.h)
add_executable(${TARGET} grpc-server.cpp utils.hpp json.hpp httplib.h)
target_include_directories(${TARGET} PRIVATE ../llava)
target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
@@ -70,4 +70,4 @@ target_link_libraries(${TARGET} PRIVATE common llama mtmd ${CMAKE_THREAD_LIBS_IN
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()
endif()

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=2634ed207a17db1a54bd8df0555bd8499a6ab691
LLAMA_VERSION?=10e9780154365b191fb43ca4830659ef12def80f
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -7,8 +7,7 @@ BUILD_TYPE?=
NATIVE?=false
ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
JOBS?=$(shell nproc)
# Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=OFF
@@ -107,21 +106,21 @@ llama-cpp-avx: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build purge
$(info ${GREEN}I llama-cpp build info:avx${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build/grpc-server llama-cpp-avx
llama-cpp-fallback: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build purge
$(info ${GREEN}I llama-cpp build info:fallback${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build/grpc-server llama-cpp-fallback
llama-cpp-grpc: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build purge
$(info ${GREEN}I llama-cpp build info:grpc${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/grpc-server llama-cpp-grpc
llama-cpp-rpc-server: llama-cpp-grpc

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File diff suppressed because it is too large Load Diff

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -38,15 +37,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

@@ -0,0 +1,13 @@
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 3cd0d2fa..6c5e811a 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -2608,7 +2608,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
- patches_data[i] = i + 1;
+ patches_data[i] = i;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);

View File

@@ -1,25 +1,21 @@
#!/bin/bash
## Patches
## Apply patches from the `patches` directory
if [ -d "patches" ]; then
for patch in $(ls patches); do
echo "Applying patch $patch"
patch -d llama.cpp/ -p1 < patches/$patch
done
fi
for patch in $(ls patches); do
echo "Applying patch $patch"
patch -d llama.cpp/ -p1 < patches/$patch
done
set -e
for file in $(ls llama.cpp/tools/server/); do
cp -rfv llama.cpp/tools/server/$file llama.cpp/tools/grpc-server/
done
cp -r CMakeLists.txt llama.cpp/tools/grpc-server/
cp -r grpc-server.cpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/vendor/nlohmann/json.hpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/tools/server/utils.hpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/vendor/cpp-httplib/httplib.h llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/tools/server/server-http.cpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/tools/server/server-http.h llama.cpp/tools/grpc-server/
set +e
if grep -q "grpc-server" llama.cpp/tools/CMakeLists.txt; then
@@ -29,3 +25,30 @@ else
fi
set -e
# Now to keep maximum compatibility with the original server.cpp, we need to remove the index.html.gz.hpp and loading.html.hpp includes
# and remove the main function
# TODO: upstream this to the original server.cpp by extracting the upstream main function to a separate file
awk '
/int[ \t]+main[ \t]*\(/ { # If the line starts the main function
in_main=1; # Set a flag
open_braces=0; # Track number of open braces
}
in_main {
open_braces += gsub(/\{/, "{"); # Count opening braces
open_braces -= gsub(/\}/, "}"); # Count closing braces
if (open_braces == 0) { # If all braces are closed
in_main=0; # End skipping
}
next; # Skip lines inside main
}
!in_main # Print lines not inside main
' "llama.cpp/tools/server/server.cpp" > llama.cpp/tools/grpc-server/server.cpp
# remove index.html.gz.hpp and loading.html.hpp includes
if [[ "$OSTYPE" == "darwin"* ]]; then
# macOS
sed -i '' '/#include "index\.html\.gz\.hpp"/d; /#include "loading\.html\.hpp"/d' llama.cpp/tools/grpc-server/server.cpp
else
# Linux and others
sed -i '/#include "index\.html\.gz\.hpp"/d; /#include "loading\.html\.hpp"/d' llama.cpp/tools/grpc-server/server.cpp
fi

View File

@@ -0,0 +1,51 @@
INCLUDE_PATH := $(abspath ./)
LIBRARY_PATH := $(abspath ./)
AR?=ar
CMAKE_ARGS?=-DGGML_NATIVE=OFF
BUILD_TYPE?=
GOCMD=go
# keep standard at C11 and C++11
CXXFLAGS = -I. -I$(INCLUDE_PATH)/sources/bark.cpp/examples -I$(INCLUDE_PATH)/sources/bark.cpp/encodec.cpp/ggml/include -I$(INCLUDE_PATH)/sources/bark.cpp/spm-headers -I$(INCLUDE_PATH)/sources/bark.cpp -O3 -DNDEBUG -std=c++17 -fPIC
LDFLAGS = -L$(LIBRARY_PATH) -L$(LIBRARY_PATH)/sources/bark.cpp/build/examples -lbark -lstdc++ -lm
# bark.cpp
BARKCPP_REPO?=https://github.com/PABannier/bark.cpp.git
BARKCPP_VERSION?=5d5be84f089ab9ea53b7a793f088d3fbf7247495
# warnings
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
## bark.cpp
sources/bark.cpp:
git clone --recursive $(BARKCPP_REPO) sources/bark.cpp && \
cd sources/bark.cpp && \
git checkout $(BARKCPP_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
sources/bark.cpp/build/libbark.a: sources/bark.cpp
cd sources/bark.cpp && \
mkdir -p build && \
cd build && \
cmake $(CMAKE_ARGS) .. && \
cmake --build . --config Release
gobark.o:
$(CXX) $(CXXFLAGS) gobark.cpp -o gobark.o -c $(LDFLAGS)
libbark.a: sources/bark.cpp/build/libbark.a gobark.o
cp $(INCLUDE_PATH)/sources/bark.cpp/build/libbark.a ./
$(AR) rcs libbark.a gobark.o
bark-cpp: libbark.a
CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH="$(CURDIR)" LIBRARY_PATH=$(CURDIR) \
$(GOCMD) build -v -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o bark-cpp ./
package:
bash package.sh
build: bark-cpp package
clean:
rm -f gobark.o libbark.a

View File

@@ -0,0 +1,85 @@
#include <iostream>
#include <tuple>
#include "bark.h"
#include "gobark.h"
#include "common.h"
#include "ggml.h"
struct bark_context *c;
void bark_print_progress_callback(struct bark_context *bctx, enum bark_encoding_step step, int progress, void *user_data) {
if (step == bark_encoding_step::SEMANTIC) {
printf("\rGenerating semantic tokens... %d%%", progress);
} else if (step == bark_encoding_step::COARSE) {
printf("\rGenerating coarse tokens... %d%%", progress);
} else if (step == bark_encoding_step::FINE) {
printf("\rGenerating fine tokens... %d%%", progress);
}
fflush(stdout);
}
int load_model(char *model) {
// initialize bark context
struct bark_context_params ctx_params = bark_context_default_params();
bark_params params;
params.model_path = model;
// ctx_params.verbosity = verbosity;
ctx_params.progress_callback = bark_print_progress_callback;
ctx_params.progress_callback_user_data = nullptr;
struct bark_context *bctx = bark_load_model(params.model_path.c_str(), ctx_params, params.seed);
if (!bctx) {
fprintf(stderr, "%s: Could not load model\n", __func__);
return 1;
}
c = bctx;
return 0;
}
int tts(char *text,int threads, char *dst ) {
ggml_time_init();
const int64_t t_main_start_us = ggml_time_us();
// generate audio
if (!bark_generate_audio(c, text, threads)) {
fprintf(stderr, "%s: An error occurred. If the problem persists, feel free to open an issue to report it.\n", __func__);
return 1;
}
const float *audio_data = bark_get_audio_data(c);
if (audio_data == NULL) {
fprintf(stderr, "%s: Could not get audio data\n", __func__);
return 1;
}
const int audio_arr_size = bark_get_audio_data_size(c);
std::vector<float> audio_arr(audio_data, audio_data + audio_arr_size);
write_wav_on_disk(audio_arr, dst);
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
const int64_t t_load_us = bark_get_load_time(c);
const int64_t t_eval_us = bark_get_eval_time(c);
printf("\n\n");
printf("%s: load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
printf("%s: eval time = %8.2f ms\n", __func__, t_eval_us / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
}
return 0;
}
int unload() {
bark_free(c);
}

View File

@@ -0,0 +1,52 @@
package main
// #cgo CXXFLAGS: -I${SRCDIR}/sources/bark.cpp/ -I${SRCDIR}/sources/bark.cpp/encodec.cpp -I${SRCDIR}/sources/bark.cpp/encodec.cpp/ggml/include -I${SRCDIR}/sources/bark.cpp/examples -I${SRCDIR}/sources/bark.cpp/spm-headers
// #cgo LDFLAGS: -L${SRCDIR}/ -L${SRCDIR}/sources/bark.cpp/build/examples -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ggml/src/ -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ -lbark -lencodec -lcommon -lggml -lgomp
// #include <gobark.h>
// #include <stdlib.h>
import "C"
import (
"fmt"
"unsafe"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
type Bark struct {
base.SingleThread
threads int
}
func (sd *Bark) Load(opts *pb.ModelOptions) error {
sd.threads = int(opts.Threads)
modelFile := C.CString(opts.ModelFile)
defer C.free(unsafe.Pointer(modelFile))
ret := C.load_model(modelFile)
if ret != 0 {
return fmt.Errorf("inference failed")
}
return nil
}
func (sd *Bark) TTS(opts *pb.TTSRequest) error {
t := C.CString(opts.Text)
defer C.free(unsafe.Pointer(t))
dst := C.CString(opts.Dst)
defer C.free(unsafe.Pointer(dst))
threads := C.int(sd.threads)
ret := C.tts(t, threads, dst)
if ret != 0 {
return fmt.Errorf("inference failed")
}
return nil
}

View File

@@ -0,0 +1,8 @@
#ifdef __cplusplus
extern "C" {
#endif
int load_model(char *model);
int tts(char *text,int threads, char *dst );
#ifdef __cplusplus
}
#endif

View File

@@ -0,0 +1,20 @@
package main
// Note: this is started internally by LocalAI and a server is allocated for each model
import (
"flag"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
func main() {
flag.Parse()
if err := grpc.StartServer(*addr, &Bark{}); err != nil {
panic(err)
}
}

41
backend/go/bark-cpp/package.sh Executable file
View File

@@ -0,0 +1,41 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/bark-cpp $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
# ARM64 architecture
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
else
echo "Error: Could not detect architecture"
exit 1
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

13
backend/go/bark-cpp/run.sh Executable file
View File

@@ -0,0 +1,13 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath $0)")
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
exec $CURDIR/lib/ld.so $CURDIR/bark-cpp "$@"
fi
exec $CURDIR/bark-cpp "$@"

View File

@@ -4,11 +4,11 @@
package main
import (
"github.com/mudler/xlog"
"github.com/rs/zerolog/log"
)
func assert(cond bool, msg string) {
if !cond {
xlog.Fatal().Stack().Msg(msg)
log.Fatal().Stack().Msg(msg)
}
}

View File

@@ -7,7 +7,8 @@ import (
"os"
grpc "github.com/mudler/LocalAI/pkg/grpc"
"github.com/mudler/xlog"
"github.com/rs/zerolog"
"github.com/rs/zerolog/log"
)
var (
@@ -15,7 +16,7 @@ var (
)
func main() {
xlog.SetLogger(xlog.NewLogger(xlog.LogLevel(os.Getenv("LOCALAI_LOG_LEVEL")), os.Getenv("LOCALAI_LOG_FORMAT")))
log.Logger = log.Output(zerolog.ConsoleWriter{Out: os.Stderr})
flag.Parse()

View File

@@ -12,7 +12,7 @@ import (
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/xlog"
"github.com/rs/zerolog/log"
)
type Store struct {
@@ -135,7 +135,7 @@ func (s *Store) StoresSet(opts *pb.StoresSetOptions) error {
} else {
sample = k.Floats
}
xlog.Debug("Key is not normalized", "sample", sample)
log.Debug().Msgf("Key is not normalized: %v", sample)
}
kvs[i] = Pair{
@@ -238,7 +238,7 @@ func (s *Store) StoresDelete(opts *pb.StoresDeleteOptions) error {
assert(!hasKey(s.keys, k), fmt.Sprintf("Key exists, but was not found: t=%d, %v", len(tail_ks), k))
}
xlog.Debug("Delete", "found", found, "tailLen", len(tail_ks), "j", j, "mergeKeysLen", len(merge_ks), "mergeValuesLen", len(merge_vs))
log.Debug().Msgf("Delete: found = %v, t = %d, j = %d, len(merge_ks) = %d, len(merge_vs) = %d", found, len(tail_ks), j, len(merge_ks), len(merge_vs))
}
merge_ks = append(merge_ks, tail_ks...)
@@ -261,7 +261,7 @@ func (s *Store) StoresDelete(opts *pb.StoresDeleteOptions) error {
}(), "Keys to delete still present")
if len(s.keys) != l {
xlog.Debug("Delete: Some keys not found", "keysLen", len(s.keys), "expectedLen", l)
log.Debug().Msgf("Delete: Some keys not found: len(s.keys) = %d, l = %d", len(s.keys), l)
}
return nil
@@ -273,7 +273,7 @@ func (s *Store) StoresGet(opts *pb.StoresGetOptions) (pb.StoresGetResult, error)
ks := sortIntoKeySlicese(opts.Keys)
if len(s.keys) == 0 {
xlog.Debug("Get: No keys in store")
log.Debug().Msgf("Get: No keys in store")
}
if s.keyLen == -1 {
@@ -305,7 +305,7 @@ func (s *Store) StoresGet(opts *pb.StoresGetOptions) (pb.StoresGetResult, error)
}
if len(pbKeys) != len(opts.Keys) {
xlog.Debug("Get: Some keys not found", "pbKeysLen", len(pbKeys), "optsKeysLen", len(opts.Keys), "storeKeysLen", len(s.keys))
log.Debug().Msgf("Get: Some keys not found: len(pbKeys) = %d, len(opts.Keys) = %d, len(s.Keys) = %d", len(pbKeys), len(opts.Keys), len(s.keys))
}
return pb.StoresGetResult{
@@ -507,7 +507,7 @@ func (s *Store) StoresFind(opts *pb.StoresFindOptions) (pb.StoresFindResult, err
} else {
sample = tk
}
xlog.Debug("Trying to compare non-normalized key with normalized keys", "sample", sample)
log.Debug().Msgf("Trying to compare non-normalized key with normalized keys: %v", sample)
}
return s.StoresFindFallback(opts)

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=e411520407663e1ddf8ff2e5ed4ff3a116fbbc97
STABLEDIFFUSION_GGML_VERSION?=0ebe6fe118f125665939b27c89f34ed38716bff8
CMAKE_ARGS+=-DGGML_MAX_NAME=128
@@ -28,12 +28,7 @@ else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
# If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++
else ifeq ($(BUILD_TYPE),hipblas)
ROCM_HOME ?= /opt/rocm
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DSD_VULKAN=ON -DGGML_VULKAN=ON
else ifeq ($(OS),Darwin)

View File

@@ -1,5 +1,3 @@
#include "stable-diffusion.h"
#include <cmath>
#include <cstdint>
#define GGML_MAX_NAME 128
@@ -8,9 +6,7 @@
#include <time.h>
#include <string>
#include <vector>
#include <map>
#include <filesystem>
#include <algorithm>
#include "gosd.h"
#define STB_IMAGE_IMPLEMENTATION
@@ -24,13 +20,11 @@
#define STB_IMAGE_RESIZE_IMPLEMENTATION
#define STB_IMAGE_RESIZE_STATIC
#include "stb_image_resize.h"
#include <stdlib.h>
#include <regex>
// Names of the sampler method, same order as enum sample_method in stable-diffusion.h
const char* sample_method_str[] = {
"default",
"euler",
"euler_a",
"heun",
"dpm2",
"dpm++2s_a",
@@ -41,384 +35,29 @@ const char* sample_method_str[] = {
"lcm",
"ddim_trailing",
"tcd",
"euler_a",
};
static_assert(std::size(sample_method_str) == SAMPLE_METHOD_COUNT, "sample method mismatch");
// Names of the sigma schedule overrides, same order as sample_schedule in stable-diffusion.h
const char* schedulers[] = {
"default",
"discrete",
"karras",
"exponential",
"ays",
"gits",
"sgm_uniform",
"simple",
"smoothstep",
"kl_optimal",
"lcm",
};
static_assert(std::size(schedulers) == SCHEDULER_COUNT, "schedulers mismatch");
static_assert(std::size(schedulers) == SCHEDULE_COUNT, "schedulers mismatch");
// New enum string arrays
const char* rng_type_str[] = {
"std_default",
"cuda",
"cpu",
};
static_assert(std::size(rng_type_str) == RNG_TYPE_COUNT, "rng type mismatch");
const char* prediction_str[] = {
"epsilon",
"v",
"edm_v",
"flow",
"flux_flow",
"flux2_flow",
};
static_assert(std::size(prediction_str) == PREDICTION_COUNT, "prediction mismatch");
const char* lora_apply_mode_str[] = {
"auto",
"immediately",
"at_runtime",
};
static_assert(std::size(lora_apply_mode_str) == LORA_APPLY_MODE_COUNT, "lora apply mode mismatch");
constexpr const char* sd_type_str[] = {
"f32", // 0
"f16", // 1
"q4_0", // 2
"q4_1", // 3
nullptr, // 4
nullptr, // 5
"q5_0", // 6
"q5_1", // 7
"q8_0", // 8
"q8_1", // 9
"q2_k", // 10
"q3_k", // 11
"q4_k", // 12
"q5_k", // 13
"q6_k", // 14
"q8_k", // 15
"iq2_xxs", // 16
"iq2_xs", // 17
"iq3_xxs", // 18
"iq1_s", // 19
"iq4_nl", // 20
"iq3_s", // 21
"iq2_s", // 22
"iq4_xs", // 23
"i8", // 24
"i16", // 25
"i32", // 26
"i64", // 27
"f64", // 28
"iq1_m", // 29
"bf16", // 30
nullptr, nullptr, nullptr, nullptr, // 31-34
"tq1_0", // 35
"tq2_0", // 36
nullptr, nullptr, // 37-38
"mxfp4" // 39
};
static_assert(std::size(sd_type_str) == SD_TYPE_COUNT, "sd type mismatch");
sd_ctx_params_t ctx_params;
sd_ctx_t* sd_c;
// Moved from the context (load time) to generation time params
scheduler_t scheduler = SCHEDULER_COUNT;
sample_method_t sample_method = SAMPLE_METHOD_COUNT;
scheduler_t scheduler = scheduler_t::DEFAULT;
// Storage for embeddings (needs to persist for the lifetime of ctx_params)
static std::vector<sd_embedding_t> embedding_vec;
// Storage for embedding strings (needs to persist as long as embedding_vec references them)
static std::vector<std::string> embedding_strings;
// Storage for LoRAs (needs to persist for the lifetime of generation params)
static std::vector<sd_lora_t> lora_vec;
// Storage for LoRA strings (needs to persist as long as lora_vec references them)
static std::vector<std::string> lora_strings;
// Storage for lora_dir path
static std::string lora_dir_path;
// Build embeddings vector from directory, similar to upstream CLI
static void build_embedding_vec(const char* embedding_dir) {
embedding_vec.clear();
embedding_strings.clear();
if (!embedding_dir || strlen(embedding_dir) == 0) {
return;
}
if (!std::filesystem::exists(embedding_dir) || !std::filesystem::is_directory(embedding_dir)) {
fprintf(stderr, "Embedding directory does not exist or is not a directory: %s\n", embedding_dir);
return;
}
static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
for (const auto& entry : std::filesystem::directory_iterator(embedding_dir)) {
if (!entry.is_regular_file()) {
continue;
}
auto path = entry.path();
std::string ext = path.extension().string();
bool valid = false;
for (const auto& e : valid_ext) {
if (ext == e) {
valid = true;
break;
}
}
if (!valid) {
continue;
}
std::string name = path.stem().string();
std::string full_path = path.string();
// Store strings in persistent storage
embedding_strings.push_back(name);
embedding_strings.push_back(full_path);
sd_embedding_t item;
item.name = embedding_strings[embedding_strings.size() - 2].c_str();
item.path = embedding_strings[embedding_strings.size() - 1].c_str();
embedding_vec.push_back(item);
fprintf(stderr, "Found embedding: %s -> %s\n", item.name, item.path);
}
fprintf(stderr, "Loaded %zu embeddings from %s\n", embedding_vec.size(), embedding_dir);
}
// Discover LoRA files in directory and build a map of name -> path
static std::map<std::string, std::string> discover_lora_files(const char* lora_dir) {
std::map<std::string, std::string> lora_map;
if (!lora_dir || strlen(lora_dir) == 0) {
fprintf(stderr, "LoRA directory not specified\n");
return lora_map;
}
if (!std::filesystem::exists(lora_dir) || !std::filesystem::is_directory(lora_dir)) {
fprintf(stderr, "LoRA directory does not exist or is not a directory: %s\n", lora_dir);
return lora_map;
}
static const std::vector<std::string> valid_ext = {".safetensors", ".ckpt", ".pt", ".gguf"};
fprintf(stderr, "Discovering LoRA files in: %s\n", lora_dir);
for (const auto& entry : std::filesystem::directory_iterator(lora_dir)) {
if (!entry.is_regular_file()) {
continue;
}
auto path = entry.path();
std::string ext = path.extension().string();
bool valid = false;
for (const auto& e : valid_ext) {
if (ext == e) {
valid = true;
break;
}
}
if (!valid) {
continue;
}
std::string name = path.stem().string(); // stem() already removes extension
std::string full_path = path.string();
// Store the name (without extension) -> full path mapping
// This allows users to specify just the name in <lora:name:strength>
lora_map[name] = full_path;
fprintf(stderr, "Found LoRA file: %s -> %s\n", name.c_str(), full_path.c_str());
}
fprintf(stderr, "Discovered %zu LoRA files in %s\n", lora_map.size(), lora_dir);
return lora_map;
}
// Helper function to check if a path is absolute (matches upstream)
static bool is_absolute_path(const std::string& p) {
#ifdef _WIN32
// Windows: C:/path or C:\path
return p.size() > 1 && std::isalpha(static_cast<unsigned char>(p[0])) && p[1] == ':';
#else
// Unix: /path
return !p.empty() && p[0] == '/';
#endif
}
// Parse LoRAs from prompt string (e.g., "<lora:name:1.0>" or "<lora:name>")
// Returns a vector of LoRA info and the cleaned prompt with LoRA tags removed
// Matches upstream implementation more closely
static std::pair<std::vector<sd_lora_t>, std::string> parse_loras_from_prompt(const std::string& prompt, const char* lora_dir) {
std::vector<sd_lora_t> loras;
std::string cleaned_prompt = prompt;
if (!lora_dir || strlen(lora_dir) == 0) {
fprintf(stderr, "LoRA directory not set, cannot parse LoRAs from prompt\n");
return {loras, cleaned_prompt};
}
// Discover LoRA files for name-based lookup
std::map<std::string, std::string> discovered_lora_map = discover_lora_files(lora_dir);
// Map to accumulate multipliers for the same LoRA (matches upstream)
std::map<std::string, float> lora_map;
std::map<std::string, float> high_noise_lora_map;
static const std::regex re(R"(<lora:([^:>]+):([^>]+)>)");
static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
std::smatch m;
std::string tmp = prompt;
fprintf(stderr, "Parsing LoRAs from prompt: %s\n", prompt.c_str());
while (std::regex_search(tmp, m, re)) {
std::string raw_path = m[1].str();
const std::string raw_mul = m[2].str();
float mul = 0.f;
try {
mul = std::stof(raw_mul);
} catch (...) {
tmp = m.suffix().str();
cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
fprintf(stderr, "Invalid LoRA multiplier '%s', skipping\n", raw_mul.c_str());
continue;
}
bool is_high_noise = false;
static const std::string prefix = "|high_noise|";
if (raw_path.rfind(prefix, 0) == 0) {
raw_path.erase(0, prefix.size());
is_high_noise = true;
}
std::filesystem::path final_path;
if (is_absolute_path(raw_path)) {
final_path = raw_path;
} else {
// Try name-based lookup first
auto it = discovered_lora_map.find(raw_path);
if (it != discovered_lora_map.end()) {
final_path = it->second;
} else {
// Try case-insensitive lookup
bool found = false;
for (const auto& pair : discovered_lora_map) {
std::string lower_name = raw_path;
std::string lower_key = pair.first;
std::transform(lower_name.begin(), lower_name.end(), lower_name.begin(), ::tolower);
std::transform(lower_key.begin(), lower_key.end(), lower_key.begin(), ::tolower);
if (lower_name == lower_key) {
final_path = pair.second;
found = true;
break;
}
}
if (!found) {
// Try as relative path in lora_dir
final_path = std::filesystem::path(lora_dir) / raw_path;
}
}
}
// Try adding extensions if file doesn't exist
if (!std::filesystem::exists(final_path)) {
bool found = false;
for (const auto& ext : valid_ext) {
std::filesystem::path try_path = final_path;
try_path += ext;
if (std::filesystem::exists(try_path)) {
final_path = try_path;
found = true;
break;
}
}
if (!found) {
fprintf(stderr, "WARNING: LoRA file not found: %s\n", final_path.lexically_normal().string().c_str());
tmp = m.suffix().str();
cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
continue;
}
}
// Normalize path (matches upstream)
const std::string key = final_path.lexically_normal().string();
// Accumulate multiplier if same LoRA appears multiple times (matches upstream)
if (is_high_noise) {
high_noise_lora_map[key] += mul;
} else {
lora_map[key] += mul;
}
fprintf(stderr, "Parsed LoRA: path='%s', multiplier=%.2f, is_high_noise=%s\n",
key.c_str(), mul, is_high_noise ? "true" : "false");
cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
tmp = m.suffix().str();
}
// Build final LoRA vector from accumulated maps (matches upstream)
// Store all path strings first to ensure they persist
for (const auto& kv : lora_map) {
lora_strings.push_back(kv.first);
}
for (const auto& kv : high_noise_lora_map) {
lora_strings.push_back(kv.first);
}
// Now build the LoRA vector with pointers to the stored strings
size_t string_idx = 0;
for (const auto& kv : lora_map) {
sd_lora_t item;
item.is_high_noise = false;
item.path = lora_strings[string_idx].c_str();
item.multiplier = kv.second;
loras.push_back(item);
string_idx++;
}
for (const auto& kv : high_noise_lora_map) {
sd_lora_t item;
item.is_high_noise = true;
item.path = lora_strings[string_idx].c_str();
item.multiplier = kv.second;
loras.push_back(item);
string_idx++;
}
// Clean up extra spaces
std::regex space_regex(R"(\s+)");
cleaned_prompt = std::regex_replace(cleaned_prompt, space_regex, " ");
// Trim leading/trailing spaces
size_t first = cleaned_prompt.find_first_not_of(" \t");
if (first != std::string::npos) {
cleaned_prompt.erase(0, first);
}
size_t last = cleaned_prompt.find_last_not_of(" \t");
if (last != std::string::npos) {
cleaned_prompt.erase(last + 1);
}
fprintf(stderr, "Parsed %zu LoRA(s) from prompt. Cleaned prompt: %s\n", loras.size(), cleaned_prompt.c_str());
return {loras, cleaned_prompt};
}
sample_method_t sample_method;
// Copied from the upstream CLI
static void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
@@ -459,7 +98,7 @@ int load_model(const char *model, char *model_path, char* options[], int threads
const char *stableDiffusionModel = "";
if (diff == 1 ) {
stableDiffusionModel = strdup(model);
stableDiffusionModel = model;
model = "";
}
@@ -470,38 +109,8 @@ int load_model(const char *model, char *model_path, char* options[], int threads
const char *vae_path = "";
const char *scheduler_str = "";
const char *sampler = "";
const char *clip_vision_path = "";
const char *llm_path = "";
const char *llm_vision_path = "";
const char *diffusion_model_path = stableDiffusionModel;
const char *high_noise_diffusion_model_path = "";
const char *taesd_path = "";
const char *control_net_path = "";
const char *embedding_dir = "";
const char *photo_maker_path = "";
const char *tensor_type_rules = "";
char *lora_dir = model_path;
bool vae_decode_only = true;
int n_threads = threads;
enum sd_type_t wtype = SD_TYPE_COUNT;
enum rng_type_t rng_type = CUDA_RNG;
enum rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
enum prediction_t prediction = PREDICTION_COUNT;
enum lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
bool offload_params_to_cpu = false;
bool keep_clip_on_cpu = false;
bool keep_control_net_on_cpu = false;
bool keep_vae_on_cpu = false;
bool diffusion_flash_attn = false;
bool tae_preview_only = false;
bool diffusion_conv_direct = false;
bool vae_conv_direct = false;
bool force_sdxl_vae_conv_scale = false;
bool chroma_use_dit_mask = true;
bool chroma_use_t5_mask = false;
int chroma_t5_mask_pad = 1;
float flow_shift = INFINITY;
bool lora_dir_allocated = false;
fprintf(stderr, "parsing options: %p\n", options);
@@ -514,16 +123,16 @@ int load_model(const char *model, char *model_path, char* options[], int threads
}
if (!strcmp(optname, "clip_l_path")) {
clip_l_path = strdup(optval);
clip_l_path = optval;
}
if (!strcmp(optname, "clip_g_path")) {
clip_g_path = strdup(optval);
clip_g_path = optval;
}
if (!strcmp(optname, "t5xxl_path")) {
t5xxl_path = strdup(optval);
t5xxl_path = optval;
}
if (!strcmp(optname, "vae_path")) {
vae_path = strdup(optval);
vae_path = optval;
}
if (!strcmp(optname, "scheduler")) {
scheduler_str = optval;
@@ -538,201 +147,18 @@ int load_model(const char *model, char *model_path, char* options[], int threads
std::filesystem::path lora_path(optval);
std::filesystem::path full_lora_path = model_path_str / lora_path;
lora_dir = strdup(full_lora_path.string().c_str());
lora_dir_path = full_lora_path.string();
fprintf(stderr, "LoRA dir resolved to: %s\n", lora_dir);
lora_dir_allocated = true;
fprintf(stderr, "Lora dir resolved to: %s\n", lora_dir);
} else {
lora_dir = strdup(optval);
lora_dir_path = std::string(optval);
lora_dir_allocated = true;
fprintf(stderr, "No model path provided, using lora dir as-is: %s\n", lora_dir);
}
// Discover LoRAs immediately when directory is set
if (lora_dir && strlen(lora_dir) > 0) {
discover_lora_files(lora_dir);
}
}
// New parsing
if (!strcmp(optname, "clip_vision_path")) clip_vision_path = strdup(optval);
if (!strcmp(optname, "llm_path")) llm_path = strdup(optval);
if (!strcmp(optname, "llm_vision_path")) llm_vision_path = strdup(optval);
if (!strcmp(optname, "diffusion_model_path")) diffusion_model_path = strdup(optval);
if (!strcmp(optname, "high_noise_diffusion_model_path")) high_noise_diffusion_model_path = strdup(optval);
if (!strcmp(optname, "taesd_path")) taesd_path = strdup(optval);
if (!strcmp(optname, "control_net_path")) control_net_path = strdup(optval);
if (!strcmp(optname, "embedding_dir")) {
// Path join with model dir
if (model_path && strlen(model_path) > 0) {
std::filesystem::path model_path_str(model_path);
std::filesystem::path embedding_path(optval);
std::filesystem::path full_embedding_path = model_path_str / embedding_path;
embedding_dir = strdup(full_embedding_path.string().c_str());
fprintf(stderr, "Embedding dir resolved to: %s\n", embedding_dir);
} else {
embedding_dir = strdup(optval);
fprintf(stderr, "No model path provided, using embedding dir as-is: %s\n", embedding_dir);
}
}
if (!strcmp(optname, "photo_maker_path")) photo_maker_path = strdup(optval);
if (!strcmp(optname, "tensor_type_rules")) tensor_type_rules = strdup(optval);
if (!strcmp(optname, "vae_decode_only")) vae_decode_only = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "offload_params_to_cpu")) offload_params_to_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "keep_clip_on_cpu")) keep_clip_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "keep_control_net_on_cpu")) keep_control_net_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "keep_vae_on_cpu")) keep_vae_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "diffusion_flash_attn")) diffusion_flash_attn = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "tae_preview_only")) tae_preview_only = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "diffusion_conv_direct")) diffusion_conv_direct = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "vae_conv_direct")) vae_conv_direct = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "force_sdxl_vae_conv_scale")) force_sdxl_vae_conv_scale = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "chroma_use_dit_mask")) chroma_use_dit_mask = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "chroma_use_t5_mask")) chroma_use_t5_mask = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
if (!strcmp(optname, "n_threads")) n_threads = atoi(optval);
if (!strcmp(optname, "chroma_t5_mask_pad")) chroma_t5_mask_pad = atoi(optval);
if (!strcmp(optname, "flow_shift")) flow_shift = atof(optval);
if (!strcmp(optname, "rng_type")) {
int found = -1;
for (int m = 0; m < RNG_TYPE_COUNT; m++) {
if (!strcmp(optval, rng_type_str[m])) {
found = m;
break;
}
}
if (found != -1) {
rng_type = (rng_type_t)found;
fprintf(stderr, "Found rng_type: %s\n", optval);
} else {
fprintf(stderr, "Invalid rng_type: %s, using default\n", optval);
}
}
if (!strcmp(optname, "sampler_rng_type")) {
int found = -1;
for (int m = 0; m < RNG_TYPE_COUNT; m++) {
if (!strcmp(optval, rng_type_str[m])) {
found = m;
break;
}
}
if (found != -1) {
sampler_rng_type = (rng_type_t)found;
fprintf(stderr, "Found sampler_rng_type: %s\n", optval);
} else {
fprintf(stderr, "Invalid sampler_rng_type: %s, using default\n", optval);
}
}
if (!strcmp(optname, "prediction")) {
int found = -1;
for (int m = 0; m < PREDICTION_COUNT; m++) {
if (!strcmp(optval, prediction_str[m])) {
found = m;
break;
}
}
if (found != -1) {
prediction = (prediction_t)found;
fprintf(stderr, "Found prediction: %s\n", optval);
} else {
fprintf(stderr, "Invalid prediction: %s, using default\n", optval);
}
}
if (!strcmp(optname, "lora_apply_mode")) {
int found = -1;
for (int m = 0; m < LORA_APPLY_MODE_COUNT; m++) {
if (!strcmp(optval, lora_apply_mode_str[m])) {
found = m;
break;
}
}
if (found != -1) {
lora_apply_mode = (lora_apply_mode_t)found;
fprintf(stderr, "Found lora_apply_mode: %s\n", optval);
} else {
fprintf(stderr, "Invalid lora_apply_mode: %s, using default\n", optval);
}
}
if (!strcmp(optname, "wtype")) {
int found = -1;
for (int m = 0; m < SD_TYPE_COUNT; m++) {
if (sd_type_str[m] && !strcmp(optval, sd_type_str[m])) {
found = m;
break;
}
}
if (found != -1) {
wtype = (sd_type_t)found;
fprintf(stderr, "Found wtype: %s\n", optval);
} else {
fprintf(stderr, "Invalid wtype: %s, using default\n", optval);
}
}
}
fprintf(stderr, "parsed options\n");
// Build embeddings vector from directory if provided
build_embedding_vec(embedding_dir);
fprintf (stderr, "Creating context\n");
sd_ctx_params_init(&ctx_params);
ctx_params.model_path = model;
ctx_params.clip_l_path = clip_l_path;
ctx_params.clip_g_path = clip_g_path;
ctx_params.clip_vision_path = clip_vision_path;
ctx_params.t5xxl_path = t5xxl_path;
ctx_params.llm_path = llm_path;
ctx_params.llm_vision_path = llm_vision_path;
ctx_params.diffusion_model_path = diffusion_model_path;
ctx_params.high_noise_diffusion_model_path = high_noise_diffusion_model_path;
ctx_params.vae_path = vae_path;
ctx_params.taesd_path = taesd_path;
ctx_params.control_net_path = control_net_path;
if (lora_dir && strlen(lora_dir) > 0) {
lora_dir_path = std::string(lora_dir);
fprintf(stderr, "LoRA model directory set to: %s\n", lora_dir);
// Discover LoRAs at load time for logging
discover_lora_files(lora_dir);
} else {
fprintf(stderr, "WARNING: LoRA model directory not set. LoRAs in prompts will not be loaded.\n");
}
// Set embeddings array and count
ctx_params.embeddings = embedding_vec.empty() ? NULL : embedding_vec.data();
ctx_params.embedding_count = static_cast<uint32_t>(embedding_vec.size());
ctx_params.photo_maker_path = photo_maker_path;
ctx_params.tensor_type_rules = tensor_type_rules;
ctx_params.vae_decode_only = vae_decode_only;
// XXX: Setting to true causes a segfault on the second run
ctx_params.free_params_immediately = false;
ctx_params.n_threads = n_threads;
ctx_params.rng_type = rng_type;
ctx_params.keep_clip_on_cpu = keep_clip_on_cpu;
if (wtype != SD_TYPE_COUNT) ctx_params.wtype = wtype;
if (sampler_rng_type != RNG_TYPE_COUNT) ctx_params.sampler_rng_type = sampler_rng_type;
if (prediction != PREDICTION_COUNT) ctx_params.prediction = prediction;
if (lora_apply_mode != LORA_APPLY_MODE_COUNT) ctx_params.lora_apply_mode = lora_apply_mode;
ctx_params.offload_params_to_cpu = offload_params_to_cpu;
ctx_params.keep_control_net_on_cpu = keep_control_net_on_cpu;
ctx_params.keep_vae_on_cpu = keep_vae_on_cpu;
ctx_params.diffusion_flash_attn = diffusion_flash_attn;
ctx_params.tae_preview_only = tae_preview_only;
ctx_params.diffusion_conv_direct = diffusion_conv_direct;
ctx_params.vae_conv_direct = vae_conv_direct;
ctx_params.force_sdxl_vae_conv_scale = force_sdxl_vae_conv_scale;
ctx_params.chroma_use_dit_mask = chroma_use_dit_mask;
ctx_params.chroma_use_t5_mask = chroma_use_t5_mask;
ctx_params.chroma_t5_mask_pad = chroma_t5_mask_pad;
ctx_params.flow_shift = flow_shift;
sd_ctx_t* sd_ctx = new_sd_ctx(&ctx_params);
if (sd_ctx == NULL) {
fprintf (stderr, "failed loading model (generic error)\n");
// TODO: Clean up allocated memory
return 1;
}
fprintf (stderr, "Created context: OK\n");
int sample_method_found = -1;
for (int m = 0; m < SAMPLE_METHOD_COUNT; m++) {
if (!strcmp(sampler, sample_method_str[m])) {
@@ -741,24 +167,54 @@ int load_model(const char *model, char *model_path, char* options[], int threads
}
}
if (sample_method_found == -1) {
sample_method_found = sd_get_default_sample_method(sd_ctx);
fprintf(stderr, "Invalid sample method, using default: %s\n", sample_method_str[sample_method_found]);
fprintf(stderr, "Invalid sample method, default to EULER_A!\n");
sample_method_found = sample_method_t::SAMPLE_METHOD_DEFAULT;
}
sample_method = (sample_method_t)sample_method_found;
for (int d = 0; d < SCHEDULER_COUNT; d++) {
for (int d = 0; d < SCHEDULE_COUNT; d++) {
if (!strcmp(scheduler_str, schedulers[d])) {
scheduler = (scheduler_t)d;
fprintf (stderr, "Found scheduler: %s\n", scheduler_str);
}
}
if (scheduler == SCHEDULER_COUNT) {
scheduler = sd_get_default_scheduler(sd_ctx, sample_method);
fprintf(stderr, "Invalid scheduler, using default: %s\n", schedulers[scheduler]);
fprintf (stderr, "Creating context\n");
sd_ctx_params_t ctx_params;
sd_ctx_params_init(&ctx_params);
ctx_params.model_path = model;
ctx_params.clip_l_path = clip_l_path;
ctx_params.clip_g_path = clip_g_path;
ctx_params.t5xxl_path = t5xxl_path;
ctx_params.diffusion_model_path = stableDiffusionModel;
ctx_params.vae_path = vae_path;
ctx_params.taesd_path = "";
ctx_params.control_net_path = "";
ctx_params.lora_model_dir = lora_dir;
ctx_params.embedding_dir = "";
ctx_params.vae_decode_only = false;
ctx_params.free_params_immediately = false;
ctx_params.n_threads = threads;
ctx_params.rng_type = STD_DEFAULT_RNG;
sd_ctx_t* sd_ctx = new_sd_ctx(&ctx_params);
if (sd_ctx == NULL) {
fprintf (stderr, "failed loading model (generic error)\n");
// Clean up allocated memory
if (lora_dir_allocated && lora_dir) {
free(lora_dir);
}
return 1;
}
fprintf (stderr, "Created context: OK\n");
sd_c = sd_ctx;
// Clean up allocated memory
if (lora_dir_allocated && lora_dir) {
free(lora_dir);
}
return 0;
}
@@ -787,66 +243,12 @@ sd_tiling_params_t* sd_img_gen_params_get_vae_tiling_params(sd_img_gen_params_t
sd_img_gen_params_t* sd_img_gen_params_new(void) {
sd_img_gen_params_t *params = (sd_img_gen_params_t *)std::malloc(sizeof(sd_img_gen_params_t));
sd_img_gen_params_init(params);
sd_sample_params_init(&params->sample_params);
sd_cache_params_init(&params->cache);
params->control_strength = 0.9f;
return params;
}
// Storage for cleaned prompt strings (needs to persist)
static std::string cleaned_prompt_storage;
static std::string cleaned_negative_prompt_storage;
void sd_img_gen_params_set_prompts(sd_img_gen_params_t *params, const char *prompt, const char *negative_prompt) {
// Clear previous LoRA data
lora_vec.clear();
lora_strings.clear();
// Parse LoRAs from prompt
std::string prompt_str = prompt ? prompt : "";
std::string negative_prompt_str = negative_prompt ? negative_prompt : "";
// Get lora_dir from ctx_params if available, otherwise use stored path
const char* lora_dir_to_use = lora_dir_path.empty() ? nullptr : lora_dir_path.c_str();
auto [loras, cleaned_prompt] = parse_loras_from_prompt(prompt_str, lora_dir_to_use);
lora_vec = loras;
cleaned_prompt_storage = cleaned_prompt;
// Also check negative prompt for LoRAs (though this is less common)
auto [neg_loras, cleaned_negative] = parse_loras_from_prompt(negative_prompt_str, lora_dir_to_use);
// Merge negative prompt LoRAs (though typically not used)
if (!neg_loras.empty()) {
fprintf(stderr, "Note: Found %zu LoRAs in negative prompt (may not be supported)\n", neg_loras.size());
}
cleaned_negative_prompt_storage = cleaned_negative;
// Set the cleaned prompts
params->prompt = cleaned_prompt_storage.c_str();
params->negative_prompt = cleaned_negative_prompt_storage.c_str();
// Set LoRAs in params
params->loras = lora_vec.empty() ? nullptr : lora_vec.data();
params->lora_count = static_cast<uint32_t>(lora_vec.size());
fprintf(stderr, "Set prompts with %zu LoRAs. Original prompt: %s\n", lora_vec.size(), prompt ? prompt : "(null)");
fprintf(stderr, "Cleaned prompt: %s\n", cleaned_prompt_storage.c_str());
// Debug: Verify LoRAs are set correctly
if (params->loras && params->lora_count > 0) {
fprintf(stderr, "DEBUG: LoRAs set in params structure:\n");
for (uint32_t i = 0; i < params->lora_count; i++) {
fprintf(stderr, " params->loras[%u]: path='%s' (ptr=%p), multiplier=%.2f, is_high_noise=%s\n",
i,
params->loras[i].path ? params->loras[i].path : "(null)",
(void*)params->loras[i].path,
params->loras[i].multiplier,
params->loras[i].is_high_noise ? "true" : "false");
}
} else {
fprintf(stderr, "DEBUG: No LoRAs set in params structure (loras=%p, lora_count=%u)\n",
(void*)params->loras, params->lora_count);
}
params->prompt = prompt;
params->negative_prompt = negative_prompt;
}
void sd_img_gen_params_set_dimensions(sd_img_gen_params_t *params, int width, int height) {
@@ -858,7 +260,7 @@ void sd_img_gen_params_set_seed(sd_img_gen_params_t *params, int64_t seed) {
params->seed = seed;
}
int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char* ref_images[], int ref_images_count) {
int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char **ref_images, int ref_images_count) {
sd_image_t* results;
@@ -1038,24 +440,6 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
}
}
// Log LoRA information
if (p->loras && p->lora_count > 0) {
fprintf(stderr, "Using %u LoRA(s) in generation:\n", p->lora_count);
for (uint32_t i = 0; i < p->lora_count; i++) {
fprintf(stderr, " LoRA[%u]: path='%s', multiplier=%.2f, is_high_noise=%s\n",
i,
p->loras[i].path ? p->loras[i].path : "(null)",
p->loras[i].multiplier,
p->loras[i].is_high_noise ? "true" : "false");
}
} else {
fprintf(stderr, "No LoRAs specified for this generation\n");
}
fprintf(stderr, "Generating image with params: \nctx\n---\n%s\ngen\n---\n%s\n",
sd_ctx_params_to_str(&ctx_params),
sd_img_gen_params_to_str(p));
results = generate_image(sd_c, p);
std::free(p);
@@ -1088,12 +472,9 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
fprintf (stderr, "Channel: %d\n", results[0].channel);
fprintf (stderr, "Data: %p\n", results[0].data);
int ret = stbi_write_png(dst, results[0].width, results[0].height, results[0].channel,
results[0].data, 0, NULL);
if (ret)
fprintf (stderr, "Saved resulting image to '%s'\n", dst);
else
fprintf(stderr, "Failed to write image to '%s'\n", dst);
stbi_write_png(dst, results[0].width, results[0].height, results[0].channel,
results[0].data, 0, NULL);
fprintf (stderr, "Saved resulting image to '%s'\n", dst);
// Clean up
free(results[0].data);
@@ -1104,14 +485,12 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
for (auto buffer : ref_image_buffers) {
if (buffer) free(buffer);
}
fprintf (stderr, "gen_image is done: %s\n", dst);
fflush(stderr);
fprintf (stderr, "gen_image is done: %s", dst);
return !ret;
return 0;
}
int unload() {
free_sd_ctx(sd_c);
return 0;
}

View File

@@ -22,7 +22,7 @@ type SDGGML struct {
var (
LoadModel func(model, model_apth string, options []uintptr, threads int32, diff int) int
GenImage func(params uintptr, steps int, dst string, cfgScale float32, srcImage string, strength float32, maskImage string, refImages []uintptr, refImagesCount int) int
GenImage func(params uintptr, steps int, dst string, cfgScale float32, srcImage string, strength float32, maskImage string, refImages []string, refImagesCount int) int
TilingParamsSetEnabled func(params uintptr, enabled bool)
TilingParamsSetTileSizes func(params uintptr, tileSizeX int, tileSizeY int)
@@ -95,12 +95,12 @@ func (sd *SDGGML) Load(opts *pb.ModelOptions) error {
sd.cfgScale = opts.CFGScale
ret := LoadModel(modelFile, modelPathC, options, opts.Threads, diffusionModel)
runtime.KeepAlive(keepAlive)
fmt.Fprintf(os.Stderr, "LoadModel: %d\n", ret)
if ret != 0 {
return fmt.Errorf("could not load model")
}
runtime.KeepAlive(keepAlive)
return nil
}
@@ -123,15 +123,10 @@ func (sd *SDGGML) GenerateImage(opts *pb.GenerateImageRequest) error {
}
}
// At the time of writing Purego doesn't recurse into slices and convert Go strings to pointers so we need to do that
var keepAlive []any
refImagesCount := len(opts.RefImages)
refImages := make([]uintptr, refImagesCount, refImagesCount+1)
for i, ri := range opts.RefImages {
bytep := CString(ri)
refImages[i] = uintptr(unsafe.Pointer(bytep))
keepAlive = append(keepAlive, bytep)
}
refImages := make([]string, refImagesCount, refImagesCount+1)
copy(refImages, opts.RefImages)
*(*uintptr)(unsafe.Add(unsafe.Pointer(&refImages), refImagesCount)) = 0
// Default strength for img2img (0.75 is a good default)
strength := float32(0.75)
@@ -145,8 +140,6 @@ func (sd *SDGGML) GenerateImage(opts *pb.GenerateImageRequest) error {
TilingParamsSetEnabled(vaep, false)
ret := GenImage(p, int(opts.Step), dst, sd.cfgScale, srcImage, strength, maskImage, refImages, refImagesCount)
runtime.KeepAlive(keepAlive)
fmt.Fprintf(os.Stderr, "GenImage: %d\n", ret)
if ret != 0 {
return fmt.Errorf("inference failed")
}

View File

@@ -17,7 +17,7 @@ void sd_img_gen_params_set_dimensions(sd_img_gen_params_t *params, int width, in
void sd_img_gen_params_set_seed(sd_img_gen_params_t *params, int64_t seed);
int load_model(const char *model, char *model_path, char* options[], int threads, int diffusionModel);
int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char* ref_images[], int ref_images_count);
int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char **ref_images, int ref_images_count);
#ifdef __cplusplus
}
#endif

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -51,15 +50,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

@@ -3,5 +3,5 @@ sources/
build/
package/
whisper
*.so
compile_commands.json
libgowhisper.so

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=aa1bc0d1a6dfd70dbb9f60c11df12441e03a9075
WHISPER_CPP_VERSION?=b12abefa9be2abae39a73fa903322af135024a36
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

View File

@@ -107,7 +107,7 @@ int vad(float pcmf32[], size_t pcmf32_len, float **segs_out,
}
int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
float pcmf32[], size_t pcmf32_len, size_t *segs_out_len, char *prompt) {
float pcmf32[], size_t pcmf32_len, size_t *segs_out_len) {
whisper_full_params wparams =
whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
@@ -122,10 +122,8 @@ int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
wparams.debug_mode = true;
wparams.print_progress = true;
wparams.tdrz_enable = tdrz;
wparams.initial_prompt = prompt;
fprintf(stderr, "info: Enable tdrz: %d\n", tdrz);
fprintf(stderr, "info: Initial prompt: \"%s\"\n", prompt);
if (whisper_full(ctx, wparams, pcmf32, pcmf32_len)) {
fprintf(stderr, "error: transcription failed\n");

View File

@@ -17,7 +17,7 @@ var (
CppLoadModel func(modelPath string) int
CppLoadModelVAD func(modelPath string) int
CppVAD func(pcmf32 []float32, pcmf32Size uintptr, segsOut unsafe.Pointer, segsOutLen unsafe.Pointer) int
CppTranscribe func(threads uint32, lang string, translate bool, diarize bool, pcmf32 []float32, pcmf32Len uintptr, segsOutLen unsafe.Pointer, prompt string) int
CppTranscribe func(threads uint32, lang string, translate bool, diarize bool, pcmf32 []float32, pcmf32Len uintptr, segsOutLen unsafe.Pointer) int
CppGetSegmentText func(i int) string
CppGetSegmentStart func(i int) int64
CppGetSegmentEnd func(i int) int64
@@ -123,16 +123,15 @@ func (w *Whisper) AudioTranscription(opts *pb.TranscriptRequest) (pb.TranscriptR
segsLen := uintptr(0xdeadbeef)
segsLenPtr := unsafe.Pointer(&segsLen)
if ret := CppTranscribe(opts.Threads, opts.Language, opts.Translate, opts.Diarize, data, uintptr(len(data)), segsLenPtr, opts.Prompt); ret != 0 {
if ret := CppTranscribe(opts.Threads, opts.Language, opts.Translate, opts.Diarize, data, uintptr(len(data)), segsLenPtr); ret != 0 {
return pb.TranscriptResult{}, fmt.Errorf("Failed Transcribe")
}
segments := []*pb.TranscriptSegment{}
text := ""
for i := range int(segsLen) {
// segment start/end conversion factor taken from https://github.com/ggml-org/whisper.cpp/blob/master/examples/cli/cli.cpp#L895
s := CppGetSegmentStart(i) * (10000000)
t := CppGetSegmentEnd(i) * (10000000)
s := CppGetSegmentStart(i)
t := CppGetSegmentEnd(i)
txt := strings.Clone(CppGetSegmentText(i))
tokens := make([]int32, CppNTokens(i))

View File

@@ -7,8 +7,7 @@ int load_model_vad(const char *const model_path);
int vad(float pcmf32[], size_t pcmf32_size, float **segs_out,
size_t *segs_out_len);
int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
float pcmf32[], size_t pcmf32_len, size_t *segs_out_len,
char *prompt);
float pcmf32[], size_t pcmf32_len, size_t *segs_out_len);
const char *get_segment_text(int i);
int64_t get_segment_t0(int i);
int64_t get_segment_t1(int i);

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -51,15 +50,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

File diff suppressed because it is too large Load Diff

View File

@@ -16,8 +16,10 @@ The Python backends use a unified build system based on `libbackend.sh` that pro
- **transformers** - Hugging Face Transformers framework (PyTorch-based)
- **vllm** - High-performance LLM inference engine
- **mlx** - Apple Silicon optimized ML framework
- **exllama2** - ExLlama2 quantized models
### Audio & Speech
- **bark** - Text-to-speech synthesis
- **coqui** - Coqui TTS models
- **faster-whisper** - Fast Whisper speech recognition
- **kitten-tts** - Lightweight TTS
@@ -83,7 +85,7 @@ runUnittests
The build system automatically detects and configures for different hardware:
- **CPU** - Standard CPU-only builds
- **CUDA** - NVIDIA GPU acceleration (supports CUDA 12/13)
- **CUDA** - NVIDIA GPU acceleration (supports CUDA 11/12)
- **Intel** - Intel XPU/GPU optimization
- **MLX** - Apple Silicon (M1/M2/M3) optimization
- **HIP** - AMD GPU acceleration
@@ -93,8 +95,8 @@ The build system automatically detects and configures for different hardware:
Backends can specify hardware-specific dependencies:
- `requirements.txt` - Base requirements
- `requirements-cpu.txt` - CPU-specific packages
- `requirements-cublas11.txt` - CUDA 11 packages
- `requirements-cublas12.txt` - CUDA 12 packages
- `requirements-cublas13.txt` - CUDA 13 packages
- `requirements-intel.txt` - Intel-optimized packages
- `requirements-mps.txt` - Apple Silicon packages

View File

@@ -1,18 +1,18 @@
.PHONY: voxcpm
voxcpm:
.PHONY: ttsbark
ttsbark:
bash install.sh
.PHONY: run
run: voxcpm
@echo "Running voxcpm..."
run: ttsbark
@echo "Running bark..."
bash run.sh
@echo "voxcpm run."
@echo "bark run."
.PHONY: test
test: voxcpm
@echo "Testing voxcpm..."
test: ttsbark
@echo "Testing bark..."
bash test.sh
@echo "voxcpm tested."
@echo "bark tested."
.PHONY: protogen-clean
protogen-clean:
@@ -20,4 +20,4 @@ protogen-clean:
.PHONY: clean
clean: protogen-clean
rm -rf venv __pycache__
rm -rf venv __pycache__

View File

@@ -0,0 +1,16 @@
# Creating a separate environment for ttsbark project
```
make ttsbark
```
# Testing the gRPC server
```
<The path of your python interpreter> -m unittest test_ttsbark.py
```
For example
```
/opt/conda/envs/bark/bin/python -m unittest extra/grpc/bark/test_ttsbark.py
``````

View File

@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Moonshine transcription
This is an extra gRPC server of LocalAI for Bark TTS
"""
from concurrent import futures
import time
@@ -8,9 +8,11 @@ import argparse
import signal
import sys
import os
from scipy.io.wavfile import write as write_wav
import backend_pb2
import backend_pb2_grpc
import moonshine_onnx
from bark import SAMPLE_RATE, generate_audio, preload_models
import grpc
@@ -27,52 +29,36 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
"""
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
model_name = request.Model
try:
print("Preparing models, please wait", file=sys.stderr)
# Store the model name for use in transcription
# Model name format: e.g., "moonshine/tiny"
self.model_name = request.Model
print(f"Model name set to: {self.model_name}", file=sys.stderr)
# download and load all models
preload_models()
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service
# Replace this with your desired response
return backend_pb2.Result(message="Model loaded successfully", success=True)
def AudioTranscription(self, request, context):
resultSegments = []
text = ""
def TTS(self, request, context):
model = request.model
print(request, file=sys.stderr)
try:
# moonshine_onnx.transcribe returns a list of strings
transcriptions = moonshine_onnx.transcribe(request.dst, self.model_name)
# Combine all transcriptions into a single text
if isinstance(transcriptions, list):
text = " ".join(transcriptions)
# Create segments for each transcription in the list
for id, trans in enumerate(transcriptions):
# Since moonshine doesn't provide timing info, we'll create a single segment
# with id and text, using approximate timing
resultSegments.append(backend_pb2.TranscriptSegment(
id=id,
start=0,
end=0,
text=trans
))
audio_array = None
if model != "":
audio_array = generate_audio(request.text, history_prompt=model)
else:
# Handle case where it's not a list (shouldn't happen, but be safe)
text = str(transcriptions)
resultSegments.append(backend_pb2.TranscriptSegment(
id=0,
start=0,
end=0,
text=text
))
audio_array = generate_audio(request.text)
print("saving to", request.dst, file=sys.stderr)
# save audio to disk
write_wav(request.dst, SAMPLE_RATE, audio_array)
print("saved to", request.dst, file=sys.stderr)
print("tts for", file=sys.stderr)
print(request, file=sys.stderr)
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}", file=sys.stderr)
return backend_pb2.TranscriptResult(segments=[], text="")
return backend_pb2.TranscriptResult(segments=resultSegments, text=text)
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(success=True)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
@@ -110,4 +96,3 @@ if __name__ == "__main__":
args = parser.parse_args()
serve(args.addr)

View File

@@ -16,15 +16,4 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
fi
# Use python 3.12 for l4t
if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
PYTHON_VERSION="3.12"
PYTHON_PATCH="12"
PY_STANDALONE_TAG="20251120"
fi
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
installRequirements

View File

@@ -0,0 +1,4 @@
transformers
accelerate
torch==2.4.1
torchaudio==2.4.1

View File

@@ -0,0 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
torchaudio==2.4.1+cu118
transformers
accelerate

View File

@@ -0,0 +1,4 @@
torch==2.4.1
torchaudio==2.4.1
transformers
accelerate

View File

@@ -0,0 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.4.1+rocm6.0
torchaudio==2.4.1+rocm6.0
transformers
accelerate

View File

@@ -0,0 +1,9 @@
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.8.10+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
oneccl_bind_pt==2.3.100+xpu
optimum[openvino]
setuptools
transformers
accelerate

View File

@@ -0,0 +1,4 @@
bark==0.1.5
grpcio==1.76.0
protobuf
certifi

View File

@@ -1,3 +1,6 @@
"""
A test script to test the gRPC service
"""
import unittest
import subprocess
import time
@@ -6,21 +9,29 @@ import backend_pb2_grpc
import grpc
class TestBackendServicer(unittest.TestCase):
"""
TestBackendServicer is the class that tests the gRPC service.
This class contains methods to test the startup and shutdown of the gRPC service.
TestBackendServicer is the class that tests the gRPC service
"""
def setUp(self):
self.service = subprocess.Popen(["python", "backend.py", "--addr", "localhost:50051"])
"""
This method sets up the gRPC service by starting the server
"""
self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
time.sleep(10)
def tearDown(self) -> None:
"""
This method tears down the gRPC service by terminating the server
"""
self.service.terminate()
self.service.wait()
def test_server_startup(self):
"""
This method tests if the server starts up successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
@@ -41,8 +52,7 @@ class TestBackendServicer(unittest.TestCase):
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
# Use a small image generation model for testing
response = stub.LoadModel(backend_pb2.ModelOptions(Model="Tongyi-MAI/Z-Image-Turbo"))
response = stub.LoadModel(backend_pb2.ModelOptions(Model="v2/en_speaker_4"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
@@ -51,32 +61,21 @@ class TestBackendServicer(unittest.TestCase):
finally:
self.tearDown()
def test_generate_image(self):
def test_tts(self):
"""
This method tests if image generation works
This method tests if the embeddings are generated successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="Tongyi-MAI/Z-Image-Turbo"))
response = stub.LoadModel(backend_pb2.ModelOptions(Model="v2/en_speaker_4"))
self.assertTrue(response.success)
req = backend_pb2.GenerateImageRequest(
positive_prompt="a cup of coffee on the table",
dst="/tmp/test_output.png",
width=512,
height=512,
step=20,
seed=42additional_information
)
resp = stub.GenerateImage(req)
self.assertTrue(resp.success)
tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story")
tts_response = stub.TTS(tts_request)
self.assertIsNotNone(tts_response)
except Exception as err:
print(err)
self.fail("GenerateImage service failed")
self.fail("TTS service failed")
finally:
self.tearDown()
additional_information
if __name__ == "__main__":
unittest.main()
self.tearDown()

View File

@@ -17,9 +17,4 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
fi
EXTRA_PIP_INSTALL_FLAGS+=" --no-build-isolation"
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
installRequirements

View File

@@ -1,8 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/cu130
torch
torchaudio
transformers
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.6.0+cu118
torchaudio==2.6.0+cu118
transformers==4.46.3
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289
chatterbox-tts@git+https://git@github.com/mudler/chatterbox.git@faster
accelerate
accelerate

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.9.1+rocm6.4
torchaudio==2.9.1+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.6.0+rocm6.1
torchaudio==2.6.0+rocm6.1
transformers
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

View File

@@ -1,5 +0,0 @@
# Build dependencies needed for packages installed from source (e.g., git dependencies)
# When using --no-build-isolation, these must be installed in the venv first
wheel
setuptools
packaging

View File

@@ -1,6 +1,7 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch
torchaudio
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
transformers
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

View File

@@ -1,7 +0,0 @@
--extra-index-url https://download.pytorch.org/whl/cu130
torch
torchaudio
transformers
numpy>=1.24.0,<1.26.0
chatterbox-tts@git+https://git@github.com/mudler/chatterbox.git@faster
accelerate

View File

@@ -1,7 +1,7 @@
#!/usr/bin/env bash
set -euo pipefail
#
#
# use the library by adding the following line to a script:
# source $(dirname $0)/../common/libbackend.sh
#
@@ -206,12 +206,12 @@ function init() {
# getBuildProfile will inspect the system to determine which build profile is appropriate:
# returns one of the following:
# - cublas11
# - cublas12
# - cublas13
# - hipblas
# - intel
function getBuildProfile() {
if [ x"${BUILD_TYPE:-}" == "xcublas" ] || [ x"${BUILD_TYPE:-}" == "xl4t" ]; then
if [ x"${BUILD_TYPE:-}" == "xcublas" ]; then
if [ ! -z "${CUDA_MAJOR_VERSION:-}" ]; then
echo ${BUILD_TYPE}${CUDA_MAJOR_VERSION}
else
@@ -237,14 +237,7 @@ function getBuildProfile() {
# Make the venv relocatable:
# - rewrite venv/bin/python{,3} to relative symlinks into $(_portable_dir)
# - normalize entrypoint shebangs to /usr/bin/env python3
# - optionally update pyvenv.cfg to point to the portable Python directory (only at runtime)
# Usage: _makeVenvPortable [--update-pyvenv-cfg]
_makeVenvPortable() {
local update_pyvenv_cfg=false
if [ "${1:-}" = "--update-pyvenv-cfg" ]; then
update_pyvenv_cfg=true
fi
local venv_dir="${EDIR}/venv"
local vbin="${venv_dir}/bin"
@@ -262,39 +255,7 @@ _makeVenvPortable() {
ln -s "${rel_py}" "${vbin}/python3"
ln -s "python3" "${vbin}/python"
# 2) Update pyvenv.cfg to point to the portable Python directory (only at runtime)
# Use absolute path resolved at runtime so it works when the venv is copied
if [ "$update_pyvenv_cfg" = "true" ]; then
local pyvenv_cfg="${venv_dir}/pyvenv.cfg"
if [ -f "${pyvenv_cfg}" ]; then
local portable_dir="$(_portable_dir)"
# Resolve to absolute path - this ensures it works when the backend is copied
# Only resolve if the directory exists (it should if ensurePortablePython was called)
if [ -d "${portable_dir}" ]; then
portable_dir="$(cd "${portable_dir}" && pwd)"
else
# Fallback to relative path if directory doesn't exist yet
portable_dir="../python"
fi
local sed_i=(sed -i)
# macOS/BSD sed needs a backup suffix; GNU sed doesn't. Make it portable:
if sed --version >/dev/null 2>&1; then
sed_i=(sed -i)
else
sed_i=(sed -i '')
fi
# Update the home field in pyvenv.cfg
# Handle both absolute paths (starting with /) and relative paths
if grep -q "^home = " "${pyvenv_cfg}"; then
"${sed_i[@]}" "s|^home = .*|home = ${portable_dir}|" "${pyvenv_cfg}"
else
# If home field doesn't exist, add it
echo "home = ${portable_dir}" >> "${pyvenv_cfg}"
fi
fi
fi
# 3) Rewrite shebangs of entry points to use env, so the venv is relocatable
# 2) Rewrite shebangs of entry points to use env, so the venv is relocatable
# Only touch text files that start with #! and reference the current venv.
local ve_abs="${vbin}/python"
local sed_i=(sed -i)
@@ -355,7 +316,6 @@ function ensureVenv() {
fi
fi
if [ "x${PORTABLE_PYTHON}" == "xtrue" ]; then
# During install, only update symlinks and shebangs, not pyvenv.cfg
_makeVenvPortable
fi
fi
@@ -392,13 +352,13 @@ function runProtogen() {
# - requirements-${BUILD_TYPE}.txt
# - requirements-${BUILD_PROFILE}.txt
#
# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-12 or cuda-13
# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-11 or cuda-12
# it can also include some options that we do not have BUILD_TYPES for, ex: intel
#
# NOTE: for BUILD_PROFILE==intel, this function does NOT automatically use the Intel python package index.
# you may want to add the following line to a requirements-intel.txt if you use one:
#
# --index-url https://download.pytorch.org/whl/xpu
# --index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
#
# If you need to add extra flags into the pip install command you can do so by setting the variable EXTRA_PIP_INSTALL_FLAGS
# before calling installRequirements. For example:
@@ -460,19 +420,6 @@ function installRequirements() {
# - ${BACKEND_NAME}.py
function startBackend() {
ensureVenv
# Update pyvenv.cfg before running to ensure paths are correct for current location
# This is critical when the backend position is dynamic (e.g., copied from container)
if [ "x${PORTABLE_PYTHON}" == "xtrue" ] || [ -x "$(_portable_python)" ]; then
_makeVenvPortable --update-pyvenv-cfg
fi
# Set up GPU library paths if a lib directory exists
# This allows backends to include their own GPU libraries (CUDA, ROCm, etc.)
if [ -d "${EDIR}/lib" ]; then
export LD_LIBRARY_PATH="${EDIR}/lib:${LD_LIBRARY_PATH:-}"
echo "Added ${EDIR}/lib to LD_LIBRARY_PATH for GPU libraries"
fi
if [ ! -z "${BACKEND_FILE:-}" ]; then
exec "${EDIR}/venv/bin/python" "${BACKEND_FILE}" "$@"
elif [ -e "${MY_DIR}/server.py" ]; then

View File

@@ -1,2 +1,2 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch

View File

@@ -1,4 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/xpu
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.8.10+xpu
torch==2.8.0
oneccl_bind_pt==2.8.0+xpu
optimum[openvino]

View File

@@ -1,4 +1,4 @@
# Creating a separate environment for coqui project
# Creating a separate environment for ttsbark project
```
make coqui

View File

@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Coqui TTS
This is an extra gRPC server of LocalAI for Bark TTS
"""
from concurrent import futures
import time

View File

@@ -0,0 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
torchaudio==2.4.1+cu118
transformers==4.48.3
accelerate
coqui-tts

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.8.0+rocm6.4
torchaudio==2.8.0+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.4.1+rocm6.0
torchaudio==2.4.1+rocm6.0
transformers==4.48.3
accelerate
coqui-tts

View File

@@ -1,6 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch==2.8.0+xpu
torchaudio==2.8.0+xpu
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
oneccl_bind_pt==2.3.100+xpu
optimum[openvino]
setuptools
transformers==4.48.3

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