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Author SHA1 Message Date
copilot-swe-agent[bot]
805654fa23 Initial plan 2026-02-02 21:38:30 +00:00
601 changed files with 9880 additions and 65625 deletions

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# 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 x 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`

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# 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.

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# 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.
## 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.

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# 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

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@@ -1,120 +0,0 @@
# Testing MCP Apps (Interactive Tool UIs)
MCP Apps is an extension to MCP where tools declare interactive HTML UIs via `_meta.ui.resourceUri`. When the LLM calls such a tool, the UI renders the app in a sandboxed iframe inline in the chat. The app communicates bidirectionally with the host via `postMessage` (JSON-RPC) and can call server tools, send messages, and update model context.
Spec: https://modelcontextprotocol.io/extensions/apps/overview
## Quick Start: Run a Test MCP App Server
The `@modelcontextprotocol/server-basic-react` npm package is a ready-to-use test server that exposes a `get-time` tool with an interactive React clock UI. It requires Node >= 20, so run it in Docker:
```bash
docker run -d --name mcp-app-test -p 3001:3001 node:22-slim \
sh -c 'npx -y @modelcontextprotocol/server-basic-react'
```
Wait ~10 seconds for it to start, then verify:
```bash
# Check it's running
docker logs mcp-app-test
# Expected: "MCP server listening on http://localhost:3001/mcp"
# Verify MCP protocol works
curl -s -X POST http://localhost:3001/mcp \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}'
# List tools — should show get-time with _meta.ui.resourceUri
curl -s -X POST http://localhost:3001/mcp \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}'
```
The `tools/list` response should contain:
```json
{
"name": "get-time",
"_meta": {
"ui": { "resourceUri": "ui://get-time/mcp-app.html" }
}
}
```
## Testing in LocalAI's UI
1. Make sure LocalAI is running (e.g. `http://localhost:8080`)
2. Build the React UI: `cd core/http/react-ui && npm install && npm run build`
3. Open the Chat page in your browser
4. Click **"Client MCP"** in the chat header
5. Add a new client MCP server:
- **URL**: `http://localhost:3001/mcp`
- **Use CORS proxy**: enabled (default) — required because the browser can't hit `localhost:3001` directly due to CORS; LocalAI's proxy at `/api/cors-proxy` handles it
6. The server should connect and discover the `get-time` tool
7. Select a model and send: **"What time is it?"**
8. The LLM should call the `get-time` tool
9. The tool result should render the interactive React clock app in an iframe as a standalone chat message (not inside the collapsed activity group)
## What to Verify
- [ ] Tool appears in the connected tools list (not filtered — `get-time` is callable by the LLM)
- [ ] The iframe renders as a standalone chat message with a puzzle-piece icon
- [ ] The app loads and is interactive (clock UI, buttons work)
- [ ] No "Reconnect to MCP server" overlay (connection is live)
- [ ] Console logs show bidirectional communication:
- `tools/call` messages from app to host (app calling server tools)
- `ui/message` notifications (app sending messages)
- [ ] After the app renders, the LLM continues and produces a text response with the time
- [ ] Non-UI tools continue to work normally (text-only results)
- [ ] Page reload shows the HTML statically with a reconnect overlay until you reconnect
## Console Log Patterns
Healthy bidirectional communication looks like:
```
Parsed message { jsonrpc: "2.0", id: N, result: {...} } // Bridge init
get-time result: { content: [...] } // Tool result received
Calling get-time tool... // App calls tool
Sending message { method: "tools/call", ... } // App -> host -> server
Parsed message { jsonrpc: "2.0", id: N, result: {...} } // Server response
Sending message text to Host: ... // App sends message
Sending message { method: "ui/message", ... } // Message notification
Message accepted // Host acknowledged
```
Benign warnings to ignore:
- `Source map error: ... about:srcdoc` — browser devtools can't find source maps for srcdoc iframes
- `Ignoring message from unknown source` — duplicate postMessage from iframe navigation
- `notifications/cancelled` — app cleaning up previous requests
## Architecture Notes
- **No server-side changes needed** — the MCP App protocol runs entirely in the browser
- `PostMessageTransport` wraps `window.postMessage` between host and `srcdoc` iframe
- `AppBridge` (from `@modelcontextprotocol/ext-apps`) auto-forwards `tools/call`, `resources/read`, `resources/list` from the app to the MCP server via the host's `Client`
- The iframe uses `sandbox="allow-scripts allow-forms"` (no `allow-same-origin`) — opaque origin, no access to host cookies/DOM/localStorage
- App-only tools (`_meta.ui.visibility: "app-only"`) are filtered from the LLM's tool list but remain callable by the app iframe
## Key Files
- `core/http/react-ui/src/components/MCPAppFrame.jsx` — iframe + AppBridge component
- `core/http/react-ui/src/hooks/useMCPClient.js` — MCP client hook with app UI helpers (`hasAppUI`, `getAppResource`, `getClientForTool`, `getToolDefinition`)
- `core/http/react-ui/src/hooks/useChat.js` — agentic loop, attaches `appUI` to tool_result messages
- `core/http/react-ui/src/pages/Chat.jsx` — renders MCPAppFrame as standalone chat messages
## Other Test Servers
The `@modelcontextprotocol/ext-apps` repo has many example servers:
- `@modelcontextprotocol/server-basic-react` — simple clock (React)
- More examples at https://github.com/modelcontextprotocol/ext-apps/tree/main/examples
All examples support both stdio and HTTP transport. Run without `--stdio` for HTTP mode on port 3001.
## Cleanup
```bash
docker rm -f mcp-app-test
```

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@@ -10,8 +10,7 @@ services:
- 8080:8080
volumes:
- localai_workspace:/workspace
- models:/host-models
- backends:/host-backends
- ../models:/host-models
- ./customization:/devcontainer-customization
command: /bin/sh -c "while sleep 1000; do :; done"
cap_add:
@@ -40,9 +39,6 @@ services:
- GF_SECURITY_ADMIN_PASSWORD=grafana
volumes:
- ./grafana:/etc/grafana/provisioning/datasources
volumes:
prom_data:
localai_workspace:
models:
backends:
localai_workspace:

3
.env
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@@ -26,9 +26,6 @@
## Disables COMPEL (Diffusers)
# COMPEL=0
## Disables SD_EMBED (Diffusers)
# SD_EMBED=0
## Enable/Disable single backend (useful if only one GPU is available)
# LOCALAI_SINGLE_ACTIVE_BACKEND=true

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@@ -13,8 +13,8 @@ import (
"github.com/ghodss/yaml"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
"github.com/mudler/cogito"
"github.com/mudler/cogito/clients"
cogito "github.com/mudler/cogito"
"github.com/mudler/cogito/structures"
"github.com/sashabaranov/go-openai/jsonschema"
)
@@ -25,7 +25,7 @@ var (
openAIBaseURL = os.Getenv("OPENAI_BASE_URL")
galleryIndexPath = os.Getenv("GALLERY_INDEX_PATH")
//defaultclient
llm = clients.NewOpenAILLM(openAIModel, openAIKey, openAIBaseURL)
llm = cogito.NewOpenAILLM(openAIModel, openAIKey, openAIBaseURL)
)
// cleanTextContent removes trailing spaces, tabs, and normalizes line endings
@@ -141,12 +141,12 @@ func getRealReadme(ctx context.Context, repository string) (string, error) {
result = result.AddMessage("user", "Describe the model in a clear and concise way that can be shared in a model gallery.")
// Get a response
_, err = llm.Ask(ctx, result)
newFragment, err := llm.Ask(ctx, result)
if err != nil {
return "", err
}
content := result.LastMessage().Content
content := newFragment.LastMessage().Content
return cleanTextContent(content), nil
}

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@@ -13,16 +13,16 @@ type HFReadmeTool struct {
client *hfapi.Client
}
func (s *HFReadmeTool) Execute(args map[string]any) (string, any, error) {
func (s *HFReadmeTool) Execute(args map[string]any) (string, error) {
q, ok := args["repository"].(string)
if !ok {
return "", nil, fmt.Errorf("no query")
return "", fmt.Errorf("no query")
}
readme, err := s.client.GetReadmeContent(q, "README.md")
if err != nil {
return "", nil, err
return "", err
}
return readme, nil, nil
return readme, nil
}
func (s *HFReadmeTool) Tool() openai.Tool {

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@@ -149,7 +149,7 @@ jobs:
- name: Docker meta
id: meta
if: github.event_name != 'pull_request'
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/local-ai-backends
@@ -165,7 +165,7 @@ jobs:
- name: Docker meta for PR
id: meta_pull_request
if: github.event_name == 'pull_request'
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/ci-tests
@@ -188,21 +188,21 @@ jobs:
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
username: ${{ secrets.dockerUsername }}
password: ${{ secrets.dockerPassword }}
- name: Login to Quay.io
if: ${{ env.quay_username != '' }}
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
registry: quay.io
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Build and push
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
if: github.event_name != 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}
@@ -223,7 +223,7 @@ jobs:
labels: ${{ steps.meta.outputs.labels }}
- name: Build and push (PR)
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
if: github.event_name == 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}

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@@ -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@v7
uses: actions/upload-artifact@v6
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@v8
uses: actions/download-artifact@v7
with:
name: ${{ inputs.backend }}-tar
path: .
@@ -105,7 +105,7 @@ jobs:
- name: Docker meta
id: meta
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
localai/localai-backends
@@ -119,7 +119,7 @@ jobs:
- name: Docker meta
id: quaymeta
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/local-ai-backends

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@@ -37,7 +37,7 @@ jobs:
make build-launcher-darwin
ls -liah dist
- name: Upload macOS launcher artifacts
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: launcher-macos
path: dist/
@@ -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@v7
uses: actions/upload-artifact@v6
with:
name: launcher-linux
path: local-ai-launcher-linux.tar.xz

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@@ -5,7 +5,6 @@ on:
workflow_dispatch:
jobs:
bump-backends:
if: github.repository == 'mudler/LocalAI'
strategy:
fail-fast: false
matrix:
@@ -18,6 +17,10 @@ jobs:
variable: "WHISPER_CPP_VERSION"
branch: "master"
file: "backend/go/whisper/Makefile"
- repository: "PABannier/bark.cpp"
variable: "BARKCPP_VERSION"
branch: "main"
file: "Makefile"
- repository: "leejet/stable-diffusion.cpp"
variable: "STABLEDIFFUSION_GGML_VERSION"
branch: "master"
@@ -26,14 +29,6 @@ jobs:
variable: "PIPER_VERSION"
branch: "master"
file: "backend/go/piper/Makefile"
- repository: "antirez/voxtral.c"
variable: "VOXTRAL_VERSION"
branch: "main"
file: "backend/go/voxtral/Makefile"
- repository: "ace-step/acestep.cpp"
variable: "ACESTEP_CPP_VERSION"
branch: "master"
file: "backend/go/acestep-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6

View File

@@ -5,7 +5,6 @@ on:
workflow_dispatch:
jobs:
bump-docs:
if: github.repository == 'mudler/LocalAI'
strategy:
fail-fast: false
matrix:

View File

@@ -5,7 +5,6 @@ on:
workflow_dispatch:
jobs:
checksum_check:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- name: Force Install GIT latest

View File

@@ -9,8 +9,8 @@ permissions:
jobs:
dependabot:
if: github.repository == 'mudler/LocalAI' && github.actor == 'dependabot[bot]'
runs-on: ubuntu-latest
if: ${{ github.actor == 'dependabot[bot]' }}
steps:
- name: Dependabot metadata
id: metadata

View File

@@ -12,7 +12,6 @@ concurrency:
jobs:
build-linux:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- name: Clone
@@ -34,7 +33,7 @@ jobs:
run: |
CGO_ENABLED=0 make build
- name: rm
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.4
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}
@@ -54,7 +53,7 @@ jobs:
rm: true
target: ./local-ai
- name: restarting
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.4
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}

View File

@@ -27,7 +27,6 @@ on:
type: string
jobs:
gallery-agent:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- name: Checkout repository
@@ -50,12 +49,12 @@ jobs:
PATH="$PATH:$HOME/go/bin" make protogen-go
- uses: mudler/localai-github-action@v1.1
with:
model: 'https://huggingface.co/unsloth/Qwen3.5-2B-GGUF'
model: 'https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF'
- name: Run gallery agent
env:
#OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
OPENAI_MODE: Qwen3.5-2B-GGUF
OPENAI_MODE: Qwen_Qwen3-1.7B-GGUF
OPENAI_BASE_URL: "http://localhost:8080"
OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
#OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}

View File

@@ -13,7 +13,6 @@ concurrency:
jobs:
generate_caches:
if: github.repository == 'mudler/LocalAI'
strategy:
matrix:
include:
@@ -77,7 +76,7 @@ jobs:
uses: actions/checkout@v6
- name: Cache GRPC
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
with:
builder: ${{ steps.buildx.outputs.name }}
# The build-args MUST be an EXACT match between the image cache and other workflow steps that want to use that cache.

View File

@@ -12,7 +12,6 @@ concurrency:
jobs:
generate_caches:
if: github.repository == 'mudler/LocalAI'
strategy:
matrix:
include:
@@ -27,14 +26,14 @@ jobs:
platforms: all
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_PASSWORD }}
- name: Login to quay
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
registry: quay.io
username: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
@@ -47,7 +46,7 @@ jobs:
uses: actions/checkout@v6
- name: Cache Intel images
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
with:
builder: ${{ steps.buildx.outputs.name }}
build-args: |

View File

@@ -14,7 +14,6 @@
jobs:
hipblas-jobs:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -26,6 +25,7 @@
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 }}
@@ -45,11 +45,11 @@
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:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -59,6 +59,7 @@
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 }}
@@ -80,6 +81,7 @@
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'
@@ -94,6 +96,7 @@
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'
@@ -106,6 +109,7 @@
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'
@@ -116,6 +120,7 @@
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'
@@ -126,11 +131,11 @@
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:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
@@ -140,6 +145,7 @@
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 }}

View File

@@ -51,6 +51,11 @@ on:
required: false
default: '--jobs=4 --output-sync=target'
type: string
aio:
description: 'AIO Image Name'
required: false
default: ''
type: string
ubuntu-version:
description: 'Ubuntu version'
required: false
@@ -146,7 +151,7 @@ jobs:
- name: Docker meta
id: meta
if: github.event_name != 'pull_request'
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/local-ai
@@ -161,7 +166,7 @@ jobs:
- name: Docker meta for PR
id: meta_pull_request
if: github.event_name == 'pull_request'
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/ci-tests
@@ -172,6 +177,34 @@ jobs:
flavor: |
latest=${{ inputs.tag-latest }}
suffix=${{ inputs.tag-suffix }}
- name: Docker meta AIO (quay.io)
if: inputs.aio != ''
id: meta_aio
uses: docker/metadata-action@v5
with:
images: |
quay.io/go-skynet/local-ai
tags: |
type=ref,event=branch
type=semver,pattern={{raw}}
flavor: |
latest=${{ inputs.tag-latest }}
suffix=${{ inputs.aio }},onlatest=true
- name: Docker meta AIO (dockerhub)
if: inputs.aio != ''
id: meta_aio_dockerhub
uses: docker/metadata-action@v5
with:
images: |
localai/localai
tags: |
type=ref,event=branch
type=semver,pattern={{raw}}
flavor: |
latest=${{ inputs.tag-latest }}
suffix=${{ inputs.aio }},onlatest=true
- name: Set up QEMU
uses: docker/setup-qemu-action@master
with:
@@ -183,21 +216,21 @@ jobs:
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
username: ${{ secrets.dockerUsername }}
password: ${{ secrets.dockerPassword }}
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
registry: quay.io
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Build and push
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
if: github.event_name != 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}
@@ -226,7 +259,7 @@ jobs:
labels: ${{ steps.meta.outputs.labels }}
### Start testing image
- name: Build and push
uses: docker/build-push-action@v7
uses: docker/build-push-action@v6
if: github.event_name == 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}
@@ -254,6 +287,41 @@ jobs:
tags: ${{ steps.meta_pull_request.outputs.tags }}
labels: ${{ steps.meta_pull_request.outputs.labels }}
## End testing image
- name: Build and push AIO image
if: inputs.aio != ''
uses: docker/build-push-action@v6
with:
builder: ${{ steps.buildx.outputs.name }}
build-args: |
BASE_IMAGE=quay.io/go-skynet/local-ai:${{ steps.meta.outputs.version }}
MAKEFLAGS=${{ inputs.makeflags }}
context: .
file: ./Dockerfile.aio
platforms: ${{ inputs.platforms }}
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta_aio.outputs.tags }}
labels: ${{ steps.meta_aio.outputs.labels }}
- name: Build and push AIO image (dockerhub)
if: inputs.aio != ''
uses: docker/build-push-action@v6
with:
builder: ${{ steps.buildx.outputs.name }}
build-args: |
BASE_IMAGE=localai/localai:${{ steps.meta.outputs.version }}
MAKEFLAGS=${{ inputs.makeflags }}
context: .
file: ./Dockerfile.aio
platforms: ${{ inputs.platforms }}
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta_aio_dockerhub.outputs.tags }}
labels: ${{ steps.meta_aio_dockerhub.outputs.labels }}
- name: job summary
run: |
echo "Built image: ${{ steps.meta.outputs.labels }}" >> $GITHUB_STEP_SUMMARY
- name: job summary(AIO)
if: inputs.aio != ''
run: |
echo "Built image: ${{ steps.meta_aio.outputs.labels }}" >> $GITHUB_STEP_SUMMARY

View File

@@ -10,8 +10,8 @@ permissions:
actions: write # to dispatch publish workflow
jobs:
dependabot:
if: github.repository == 'mudler/LocalAI' && github.actor == 'localai-bot' && contains(github.event.pull_request.title, 'chore:')
runs-on: ubuntu-latest
if: ${{ github.actor == 'localai-bot' && !contains(github.event.pull_request.title, 'chore(model gallery):') }}
steps:
- name: Checkout repository
uses: actions/checkout@v6

View File

@@ -10,7 +10,7 @@ permissions:
jobs:
notify-discord:
if: github.repository == 'mudler/LocalAI' && (github.event.pull_request.merged == true) && (contains(github.event.pull_request.labels.*.name, 'area/ai-model'))
if: ${{ (github.event.pull_request.merged == true) && (contains(github.event.pull_request.labels.*.name, 'area/ai-model')) }}
env:
MODEL_NAME: gemma-3-12b-it-qat
runs-on: ubuntu-latest
@@ -90,7 +90,7 @@ jobs:
connect-timeout-seconds: 180
limit-access-to-actor: true
notify-twitter:
if: github.repository == 'mudler/LocalAI' && (github.event.pull_request.merged == true) && (contains(github.event.pull_request.labels.*.name, 'area/ai-model'))
if: ${{ (github.event.pull_request.merged == true) && (contains(github.event.pull_request.labels.*.name, 'area/ai-model')) }}
env:
MODEL_NAME: gemma-3-12b-it-qat
runs-on: ubuntu-latest

View File

@@ -6,7 +6,6 @@ on:
jobs:
notify-discord:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
env:
RELEASE_BODY: ${{ github.event.release.body }}

View File

@@ -18,7 +18,7 @@ jobs:
with:
go-version: 1.23
- name: Run GoReleaser
uses: goreleaser/goreleaser-action@v7
uses: goreleaser/goreleaser-action@v6
with:
version: v2.11.0
args: release --clean

View File

@@ -8,10 +8,9 @@ on:
jobs:
stale:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v9
- uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # 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

@@ -304,28 +304,6 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/qwen-tts
make --jobs=5 --output-sync=target -C backend/python/qwen-tts test
# TODO: s2-pro model is too large to load on CPU-only CI runners — re-enable
# when we have GPU runners or a smaller test model.
# tests-fish-speech:
# runs-on: ubuntu-latest
# timeout-minutes: 45
# 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 portaudio19-dev
# 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 fish-speech
# run: |
# make --jobs=5 --output-sync=target -C backend/python/fish-speech
# make --jobs=5 --output-sync=target -C backend/python/fish-speech test
tests-qwen-asr:
runs-on: ubuntu-latest
steps:
@@ -345,25 +323,6 @@ jobs:
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-nemo:
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 nemo
run: |
make --jobs=5 --output-sync=target -C backend/python/nemo
make --jobs=5 --output-sync=target -C backend/python/nemo test
tests-voxcpm:
runs-on: ubuntu-latest
steps:
@@ -383,64 +342,3 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/voxcpm
make --jobs=5 --output-sync=target -C backend/python/voxcpm test
tests-acestep-cpp:
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 cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- 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: Build acestep-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/acestep-cpp
- name: Test acestep-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/acestep-cpp test
tests-voxtral:
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 cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
# You can test your matrix by printing the current Go version
- 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: Build voxtral
run: |
make --jobs=5 --output-sync=target -C backend/go/voxtral
- name: Test voxtral
run: |
make --jobs=5 --output-sync=target -C backend/go/voxtral test

View File

@@ -93,21 +93,30 @@ jobs:
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install curl ffmpeg libopus-dev
- name: Setup Node.js
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI
run: make react-ui
- name: Build backends
run: |
make backends/transformers
mkdir external && mv backends/transformers external/transformers
make backends/llama-cpp backends/local-store backends/silero-vad backends/piper backends/whisper backends/stablediffusion-ggml
sudo apt-get install build-essential ccache upx-ucl curl ffmpeg
sudo apt-get install -y libgmock-dev clang
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
sudo apt-get install -y ca-certificates cmake patch python3-pip unzip
sudo apt-get install -y libopencv-dev
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
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get install -y cuda-nvcc-${CUDA_VERSION} libcublas-dev-${CUDA_VERSION}
export CUDACXX=/usr/local/cuda/bin/nvcc
make -C backend/python/transformers
make backends/huggingface backends/llama-cpp backends/local-store backends/silero-vad backends/piper backends/whisper backends/stablediffusion-ggml
env:
CUDA_VERSION: 12-4
- name: Test
run: |
TRANSFORMER_BACKEND=$PWD/external/transformers/run.sh PATH="$PATH:/root/go/bin" GO_TAGS="tts" make --jobs 5 --output-sync=target test
PATH="$PATH:/root/go/bin" GO_TAGS="tts" make --jobs 5 --output-sync=target test
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
@@ -116,7 +125,7 @@ jobs:
connect-timeout-seconds: 180
limit-access-to-actor: true
tests-e2e-container:
tests-aio-container:
runs-on: ubuntu-latest
steps:
- name: Release space from worker
@@ -166,7 +175,7 @@ jobs:
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Test
run: |
PATH="$PATH:$HOME/go/bin" make backends/local-store backends/silero-vad backends/llama-cpp backends/whisper backends/piper backends/stablediffusion-ggml docker-build-e2e e2e-aio
PATH="$PATH:$HOME/go/bin" make backends/local-store backends/silero-vad backends/llama-cpp backends/whisper backends/piper backends/stablediffusion-ggml docker-build-aio e2e-aio
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
@@ -195,14 +204,8 @@ jobs:
run: go version
- name: Dependencies
run: |
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm opus
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
pip install --user --no-cache-dir grpcio-tools grpcio
- name: Setup Node.js
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI
run: make react-ui
- name: Build llama-cpp-darwin
run: |
make protogen-go

View File

@@ -43,13 +43,7 @@ jobs:
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential libopus-dev
- name: Setup Node.js
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build React UI
run: make react-ui
sudo apt-get install -y build-essential
- name: Test Backend E2E
run: |
PATH="$PATH:$HOME/go/bin" make build-mock-backend test-e2e

View File

@@ -5,7 +5,6 @@ on:
workflow_dispatch:
jobs:
swagger:
if: github.repository == 'mudler/LocalAI'
strategy:
fail-fast: false
runs-on: ubuntu-latest

9
.gitignore vendored
View File

@@ -37,7 +37,7 @@ models/*
test-models/
test-dir/
tests/e2e-aio/backends
mock-backend
tests/e2e-aio/models
release/
@@ -65,10 +65,3 @@ docs/static/gallery.html
# per-developer customization files for the development container
.devcontainer/customization/*
# React UI build artifacts (keep placeholder dist/index.html)
core/http/react-ui/node_modules/
core/http/react-ui/dist
# Extracted backend binaries for container-based testing
local-backends/

View File

@@ -2,7 +2,6 @@ version: 2
before:
hooks:
- make protogen-go
- make react-ui
- go mod tidy
dist: release
source:

302
AGENTS.md
View File

@@ -1,22 +1,290 @@
# LocalAI Agent Instructions
# Build and testing
This file is an index to detailed topic guides in the `.agents/` directory. Read the relevant file(s) for the task at hand — you don't need to load all of them.
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.
## Topics
## Building a specified backend
| File | When to read |
|------|-------------|
| [.agents/building-and-testing.md](.agents/building-and-testing.md) | Building the project, running tests, Docker builds for specific platforms |
| [.agents/adding-backends.md](.agents/adding-backends.md) | Adding a new backend (Python, Go, or C++) — full step-by-step checklist |
| [.agents/coding-style.md](.agents/coding-style.md) | Code style, editorconfig, logging, documentation conventions |
| [.agents/llama-cpp-backend.md](.agents/llama-cpp-backend.md) | Working on the llama.cpp backend — architecture, updating, tool call parsing |
| [.agents/testing-mcp-apps.md](.agents/testing-mcp-apps.md) | Testing MCP Apps (interactive tool UIs) in the React UI |
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
## Quick Reference
- 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.
- **Logging**: Use `github.com/mudler/xlog` (same API as slog)
- **Go style**: Prefer `any` over `interface{}`
- **Comments**: Explain *why*, not *what*
- **Docs**: Update `docs/content/` when adding features or changing config
- **Build**: Inspect `Makefile` and `.github/workflows/` — ask the user before running long builds
- **UI**: The active UI is the React app in `core/http/react-ui/`. The older Alpine.js/HTML UI in `core/http/static/` is pending deprecation — all new UI work goes in the React UI
## 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

@@ -1 +0,0 @@
AGENTS.md

View File

@@ -7,10 +7,8 @@ Thank you for your interest in contributing to LocalAI! We appreciate your time
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Setting up the Development Environment](#setting-up-the-development-environment)
- [Environment Variables](#environment-variables)
- [Contributing](#contributing)
- [Submitting an Issue](#submitting-an-issue)
- [Development Workflow](#development-workflow)
- [Creating a Pull Request (PR)](#creating-a-pull-request-pr)
- [Coding Guidelines](#coding-guidelines)
- [Testing](#testing)
@@ -21,122 +19,18 @@ Thank you for your interest in contributing to LocalAI! We appreciate your time
### Prerequisites
- **Go 1.21+** (the project currently uses Go 1.26 in `go.mod`, but 1.21 is the minimum supported version)
- [Download Go](https://go.dev/dl/) or install via your package manager
- macOS: `brew install go`
- Ubuntu/Debian: follow the [official instructions](https://go.dev/doc/install) (the `apt` version is often outdated)
- Verify: `go version`
- **Git**
- **GNU Make**
- **GCC / C/C++ toolchain** (required for CGo and native backends)
- **Protocol Buffers compiler** (`protoc`) — needed for gRPC code generation
- Golang [1.21]
- Git
- macOS/Linux
#### System dependencies by platform
### Setting up the Development Environment and running localAI in the local environment
<details>
<summary><strong>Ubuntu / Debian</strong></summary>
```bash
sudo apt-get update
sudo apt-get install -y build-essential gcc g++ cmake git wget \
protobuf-compiler libprotobuf-dev pkg-config \
libopencv-dev libgrpc-dev
```
</details>
<details>
<summary><strong>CentOS / RHEL / Fedora</strong></summary>
```bash
sudo dnf groupinstall -y "Development Tools"
sudo dnf install -y cmake git wget protobuf-compiler protobuf-devel \
opencv-devel grpc-devel
```
</details>
<details>
<summary><strong>macOS</strong></summary>
```bash
xcode-select --install
brew install cmake git protobuf grpc opencv wget
```
</details>
<details>
<summary><strong>Windows</strong></summary>
Use [WSL 2](https://learn.microsoft.com/en-us/windows/wsl/install) with an Ubuntu distribution, then follow the Ubuntu instructions above.
</details>
### Setting up the Development Environment
1. **Clone the repository:**
```bash
git clone https://github.com/mudler/LocalAI.git
cd LocalAI
```
2. **Build LocalAI:**
```bash
make build
```
This runs protobuf generation, installs Go tools, builds the React UI, and compiles the `local-ai` binary. Key build variables you can set:
| Variable | Description | Example |
|---|---|---|
| `BUILD_TYPE` | GPU/accelerator type (`cublas`, `hipblas`, `intel`, ``) | `BUILD_TYPE=cublas make build` |
| `GO_TAGS` | Additional Go build tags | `GO_TAGS=debug make build` |
| `CUDA_MAJOR_VERSION` | CUDA major version (default: `13`) | `CUDA_MAJOR_VERSION=12` |
3. **Run LocalAI:**
```bash
./local-ai
```
4. **Development mode with live reload:**
```bash
make build-dev
```
This installs [`air`](https://github.com/air-verse/air) automatically and watches for file changes, rebuilding and restarting the server on each save.
5. **Containerized build** (no local toolchain needed):
```bash
make docker
```
For GPU-specific Docker builds, see the `docker-build-*` targets in the Makefile and refer to [CLAUDE.md](CLAUDE.md) for detailed backend build instructions.
### Environment Variables
LocalAI is configured primarily through environment variables (or equivalent CLI flags). The most useful ones for development are:
| Variable | Description | Default |
|---|---|---|
| `LOCALAI_DEBUG` | Enable debug mode | `false` |
| `LOCALAI_LOG_LEVEL` | Log verbosity (`error`, `warn`, `info`, `debug`, `trace`) | — |
| `LOCALAI_LOG_FORMAT` | Log format (`default`, `text`, `json`) | `default` |
| `LOCALAI_MODELS_PATH` | Path to model files | `./models` |
| `LOCALAI_BACKENDS_PATH` | Path to backend binaries | `./backends` |
| `LOCALAI_CONFIG_DIR` | Directory for dynamic config files (API keys, external backends) | `./configuration` |
| `LOCALAI_THREADS` | Number of threads for inference | — |
| `LOCALAI_ADDRESS` | Bind address for the API server | `:8080` |
| `LOCALAI_API_KEY` | API key(s) for authentication | — |
| `LOCALAI_CORS` | Enable CORS | `false` |
| `LOCALAI_DISABLE_WEBUI` | Disable the web UI | `false` |
See `core/cli/run.go` for the full list of supported environment variables.
1. Clone the repository: `git clone https://github.com/go-skynet/LocalAI.git`
2. Navigate to the project directory: `cd LocalAI`
3. Install the required dependencies ( see https://localai.io/basics/build/#build-localai-locally )
4. Build LocalAI: `make build`
5. Run LocalAI: `./local-ai`
6. To Build and live reload: `make build-dev`
## Contributing
@@ -146,128 +40,43 @@ We welcome contributions from everyone! To get started, follow these steps:
If you find a bug, have a feature request, or encounter any issues, please check the [issue tracker](https://github.com/go-skynet/LocalAI/issues) to see if a similar issue has already been reported. If not, feel free to [create a new issue](https://github.com/go-skynet/LocalAI/issues/new) and provide as much detail as possible.
### Development Workflow
#### Branch naming conventions
Use a descriptive branch name that indicates the type and scope of the change:
- `feature/<short-description>` — new functionality
- `fix/<short-description>` — bug fixes
- `docs/<short-description>` — documentation changes
- `refactor/<short-description>` — code refactoring
#### Commit messages
- Use a short, imperative subject line (e.g., "feat: add whisper backend support", not "Added whisper backend support")
- Keep the subject under 72 characters
- Use the body to explain **why** the change was made when the subject alone is not sufficient
- Use [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/)
#### Creating a Pull Request (PR)
Before jumping into a PR for a massive feature or big change, it is preferred to discuss it first via an issue.
### Creating a Pull Request (PR)
1. Fork the repository.
2. Create a new branch: `git checkout -b feature/my-change`
3. Make your changes, keeping commits focused and atomic.
4. Run tests locally before pushing (see [Testing](#testing) below).
5. Push to your fork: `git push origin feature/my-change`
6. Open a pull request against the `master` branch.
7. Fill in the PR description with:
- What the change does and why
- How it was tested
- Any breaking changes or migration steps
8. Respond to review feedback promptly. Push follow-up commits rather than force-pushing amended commits so reviewers can see incremental changes.
9. Once approved, a maintainer will merge your PR.
2. Create a new branch with a descriptive name: `git checkout -b [branch name]`
3. Make your changes and commit them.
4. Push the changes to your fork: `git push origin [branch name]`
5. Create a new pull request from your branch to the main project's `main` or `master` branch.
6. Provide a clear description of your changes in the pull request.
7. Make any requested changes during the review process.
8. Once your PR is approved, it will be merged into the main project.
## Coding Guidelines
This project uses an [`.editorconfig`](.editorconfig) file to define formatting standards (indentation, line endings, charset, etc.). Please configure your editor to respect it.
For AI-assisted development, see [`CLAUDE.md`](CLAUDE.md) for agent-specific guidelines including build instructions and backend architecture details.
### General Principles
- Write code that can be tested. All new features and bug fixes should include test coverage.
- Use comments sparingly to explain **why** code does something, not **what** it does. Comments should add context that would be difficult to deduce from reading the code alone.
- Keep changes focused. Avoid unrelated refactors, formatting changes, or feature additions in the same PR.
### Go Code
- Prefer modern Go idioms — for example, use `any` instead of `interface{}`.
- Use [`golangci-lint`](https://golangci-lint.run) to catch common issues before submitting a PR.
- Use [`github.com/mudler/xlog`](https://github.com/mudler/xlog) for logging (same API as `slog`). Do not use `fmt.Println` or the standard `log` package for operational logging.
- Use tab indentation for Go files (as defined in `.editorconfig`).
### Python Code
- Use 4-space indentation (as defined in `.editorconfig`).
- Include a `requirements.txt` for any new dependencies.
### Code Review
- All contributions go through code review via pull requests.
- Reviewers will check for correctness, test coverage, adherence to these guidelines, and clarity of intent.
- Be responsive to review feedback and keep discussions constructive.
- No specific coding guidelines at the moment. Please make sure the code can be tested. The most popular lint tools like [`golangci-lint`](https://golangci-lint.run) can help you here.
## Testing
All new features and bug fixes should include test coverage. The project uses [Ginkgo](https://onsi.github.io/ginkgo/) as its test framework.
`make test` cannot handle all the model now. Please be sure to add a test case for the new features or the part was changed.
### Running unit tests
### Running AIO tests
```bash
make test
```
This downloads test model fixtures, runs protobuf generation, and executes the full test suite including llama-gguf, TTS, and stable-diffusion tests. Note: some tests require model files to be downloaded, so the first run may take longer.
To run tests for a specific package:
```bash
go test ./core/config/...
go test ./pkg/model/...
```
To run a specific test by name using Ginkgo's `--focus` flag:
```bash
go run github.com/onsi/ginkgo/v2/ginkgo --focus="should load a model" -v -r ./core/
```
### Running end-to-end tests
The e2e tests run LocalAI in a Docker container and exercise the API:
```bash
make test-e2e
```
### Running E2E container tests
These tests build a standard LocalAI Docker image and run it with pre-configured model configs to verify that most endpoints work correctly:
All-In-One images has a set of tests that automatically verifies that most of the endpoints works correctly, a flow can be :
```bash
# Build the LocalAI docker image
make docker-build-e2e
make DOCKER_IMAGE=local-ai docker
# Run the e2e tests (uses model configs from tests/e2e-aio/models/)
make e2e-aio
```
# Build the corresponding AIO image
BASE_IMAGE=local-ai DOCKER_AIO_IMAGE=local-ai-aio:test make docker-aio
### Testing backends
To prepare and test extra (Python) backends:
```bash
make prepare-test-extra # build Python backends for testing
make test-extra # run backend-specific tests
# Run the AIO e2e tests
LOCALAI_IMAGE_TAG=test LOCALAI_IMAGE=local-ai-aio make run-e2e-aio
```
## Documentation
We welcome contributions to the documentation. Please open a new PR or create a new issue. The documentation is available under `docs/` https://github.com/mudler/LocalAI/tree/master/docs
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

View File

@@ -10,7 +10,7 @@ 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 libopus0 sox && \
ffmpeg libopenblas0 libopenblas-dev sox && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -190,7 +190,6 @@ RUN apt-get update && \
curl libssl-dev \
git \
git-lfs \
libopus-dev pkg-config \
unzip upx-ucl python3 python-is-python3 && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -292,17 +291,6 @@ EOT
###################################
###################################
# Build React UI
FROM node:25-slim AS react-ui-builder
WORKDIR /app
COPY core/http/react-ui/package*.json ./
RUN npm install
COPY core/http/react-ui/ ./
RUN npm run build
###################################
###################################
# Compile backends first in a separate stage
FROM builder-base AS builder-backends
ARG TARGETARCH
@@ -332,9 +320,6 @@ WORKDIR /build
COPY . .
# Copy pre-built React UI
COPY --from=react-ui-builder /app/dist ./core/http/react-ui/dist
## Build the binary
## If we're on arm64 AND using cublas/hipblas, skip some of the llama-compat backends to save space
## Otherwise just run the normal build
@@ -379,17 +364,14 @@ COPY ./entrypoint.sh .
# Copy the binary
COPY --from=builder /build/local-ai ./
# Copy the opus shim if it was built
RUN --mount=from=builder,src=/build/,dst=/mnt/build \
if [ -f /mnt/build/libopusshim.so ]; then cp /mnt/build/libopusshim.so ./; fi
# Make sure the models directory exists
RUN mkdir -p /models /backends /data
RUN mkdir -p /models /backends
# Define the health check command
HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
CMD curl -f ${HEALTHCHECK_ENDPOINT} || exit 1
VOLUME /models /backends /configuration /data
VOLUME /models /backends /configuration
EXPOSE 8080
ENTRYPOINT [ "/entrypoint.sh" ]

8
Dockerfile.aio Normal file
View File

@@ -0,0 +1,8 @@
ARG BASE_IMAGE=ubuntu:24.04
FROM ${BASE_IMAGE}
RUN apt-get update && apt-get install -y pciutils && apt-get clean
COPY aio/ /aio
ENTRYPOINT [ "/aio/entrypoint.sh" ]

164
Makefile
View File

@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/outetts 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/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus
.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=$(GOCMD) test
@@ -91,23 +91,8 @@ install-go-tools:
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
## React UI:
react-ui:
ifneq ($(wildcard core/http/react-ui/dist),)
@echo "react-ui dist already exists, skipping build"
else
cd core/http/react-ui && npm install && npm run build
endif
react-ui-docker:
docker run --entrypoint /bin/bash -v $(CURDIR):/app:z oven/bun:1 \
-c "cd /app/core/http/react-ui && bun install && bun run build"
core/http/react-ui/dist: react-ui
## Build:
build: protogen-go install-go-tools core/http/react-ui/dist ## Build the project
build: protogen-go install-go-tools ## Build the project
$(info ${GREEN}I local-ai build info:${RESET})
$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
@@ -164,7 +149,6 @@ test: test-models/testmodel.ggml protogen-go
@echo 'Running tests'
export GO_TAGS="debug"
$(MAKE) prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
HUGGINGFACE_GRPC=$(abspath ./)/backend/python/transformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="!llama-gguf" --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
$(MAKE) test-llama-gguf
@@ -172,10 +156,10 @@ test: test-models/testmodel.ggml protogen-go
$(MAKE) test-stablediffusion
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
## AIO tests
########################################################
docker-build-e2e:
docker-build-aio:
docker build \
--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
@@ -187,12 +171,13 @@ docker-build-e2e:
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
BASE_IMAGE=local-ai:tests DOCKER_AIO_IMAGE=local-ai-aio:test $(MAKE) docker-aio
e2e-aio:
LOCALAI_BACKEND_DIR=$(abspath ./backends) \
LOCALAI_MODELS_DIR=$(abspath ./tests/e2e-aio/models) \
LOCALAI_IMAGE_TAG=tests \
LOCALAI_IMAGE=local-ai \
LOCALAI_MODELS_DIR=$(abspath ./models) \
LOCALAI_IMAGE_TAG=test \
LOCALAI_IMAGE=local-ai-aio \
$(MAKE) run-e2e-aio
run-e2e-aio: protogen-go
@@ -251,88 +236,6 @@ test-stablediffusion: prepare-test
test-stores:
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stores" --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-opus:
@echo 'Running opus backend tests'
$(MAKE) -C backend/go/opus libopusshim.so
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./backend/go/opus/...
test-opus-docker:
@echo 'Running opus backend tests in Docker'
docker build --target builder \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg BACKEND=opus \
-t localai-opus-test -f backend/Dockerfile.golang .
docker run --rm localai-opus-test \
bash -c 'cd /LocalAI && go run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./backend/go/opus/...'
test-realtime: build-mock-backend
@echo 'Running realtime e2e tests (mock backend)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime && !real-models" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# Real-model realtime tests. Set REALTIME_TEST_MODEL to use your own pipeline,
# or leave unset to auto-build one from the component env vars below.
REALTIME_VAD?=silero-vad-ggml
REALTIME_STT?=whisper-1
REALTIME_LLM?=qwen3-0.6b
REALTIME_TTS?=tts-1
REALTIME_BACKENDS_PATH?=$(abspath ./)/backends
test-realtime-models: build-mock-backend
@echo 'Running realtime e2e tests (real models)'
REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
REALTIME_VAD=$(REALTIME_VAD) \
REALTIME_STT=$(REALTIME_STT) \
REALTIME_LLM=$(REALTIME_LLM) \
REALTIME_TTS=$(REALTIME_TTS) \
REALTIME_BACKENDS_PATH=$(REALTIME_BACKENDS_PATH) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# --- Container-based real-model testing ---
REALTIME_BACKEND_NAMES ?= silero-vad whisper llama-cpp kokoro
REALTIME_MODELS_DIR ?= $(abspath ./models)
REALTIME_BACKENDS_DIR ?= $(abspath ./local-backends)
REALTIME_DOCKER_FLAGS ?= --gpus all
local-backends:
mkdir -p local-backends
extract-backend-%: docker-build-% local-backends
@echo "Extracting backend $*..."
@CID=$$(docker create local-ai-backend:$*) && \
rm -rf local-backends/$* && mkdir -p local-backends/$* && \
docker cp $$CID:/ - | tar -xf - -C local-backends/$* && \
docker rm $$CID > /dev/null
extract-realtime-backends: $(addprefix extract-backend-,$(REALTIME_BACKEND_NAMES))
test-realtime-models-docker: build-mock-backend
docker build --target build-requirements \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),cublas) \
--build-arg CUDA_MAJOR_VERSION=$(or $(CUDA_MAJOR_VERSION),13) \
--build-arg CUDA_MINOR_VERSION=$(or $(CUDA_MINOR_VERSION),0) \
-t localai-test-runner .
docker run --rm \
$(REALTIME_DOCKER_FLAGS) \
-v $(abspath ./):/build \
-v $(REALTIME_MODELS_DIR):/models:ro \
-v $(REALTIME_BACKENDS_DIR):/backends \
-v localai-go-cache:/root/go/pkg/mod \
-v localai-go-build-cache:/root/.cache/go-build \
-e REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
-e REALTIME_VAD=$(REALTIME_VAD) \
-e REALTIME_STT=$(REALTIME_STT) \
-e REALTIME_LLM=$(REALTIME_LLM) \
-e REALTIME_TTS=$(REALTIME_TTS) \
-e REALTIME_BACKENDS_PATH=/backends \
-e REALTIME_MODELS_PATH=/models \
-w /build \
localai-test-runner \
bash -c 'git config --global --add safe.directory /build && \
make protogen-go && make build-mock-backend && \
go run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e'
test-container:
docker build --target requirements -t local-ai-test-container .
docker run -ti --rm --entrypoint /bin/bash -ti -v $(abspath ./):/build local-ai-test-container
@@ -405,7 +308,6 @@ protogen-go-clean:
prepare-test-extra: protogen-python
$(MAKE) -C backend/python/transformers
$(MAKE) -C backend/python/outetts
$(MAKE) -C backend/python/diffusers
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
@@ -414,17 +316,12 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
$(MAKE) -C backend/python/qwen-tts
$(MAKE) -C backend/python/fish-speech
$(MAKE) -C backend/python/faster-qwen3-tts
$(MAKE) -C backend/python/qwen-asr
$(MAKE) -C backend/python/nemo
$(MAKE) -C backend/python/voxcpm
$(MAKE) -C backend/python/whisperx
$(MAKE) -C backend/python/ace-step
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
$(MAKE) -C backend/python/outetts test
$(MAKE) -C backend/python/diffusers test
$(MAKE) -C backend/python/chatterbox test
$(MAKE) -C backend/python/vllm test
@@ -433,15 +330,12 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/moonshine test
$(MAKE) -C backend/python/pocket-tts test
$(MAKE) -C backend/python/qwen-tts test
$(MAKE) -C backend/python/fish-speech test
$(MAKE) -C backend/python/faster-qwen3-tts test
$(MAKE) -C backend/python/qwen-asr test
$(MAKE) -C backend/python/nemo test
$(MAKE) -C backend/python/voxcpm test
$(MAKE) -C backend/python/whisperx test
$(MAKE) -C backend/python/ace-step test
DOCKER_IMAGE?=local-ai
DOCKER_AIO_IMAGE?=local-ai-aio
IMAGE_TYPE?=core
BASE_IMAGE?=ubuntu:24.04
@@ -471,6 +365,21 @@ docker-cuda12:
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE)-cuda-12 .
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:
$(MAKE) docker-aio DOCKER_AIO_SIZE=cpu
$(MAKE) docker-aio DOCKER_AIO_SIZE=cpu
docker-image-intel:
docker build \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
@@ -520,10 +429,6 @@ backends/mlx-audio:
BACKEND=mlx-audio $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx-audio.tar)"
backends/mlx-distributed:
BACKEND=mlx-distributed $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx-distributed.tar)"
backends/stablediffusion-ggml-darwin:
BACKEND=stablediffusion-ggml BUILD_TYPE=metal $(MAKE) build-darwin-go-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
@@ -542,14 +447,10 @@ 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
BACKEND_VOXTRAL = voxtral|golang|.|false|true
BACKEND_ACESTEP_CPP = acestep-cpp|golang|.|false|true
BACKEND_OPUS = opus|golang|.|false|true
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
BACKEND_TRANSFORMERS = transformers|python|.|false|true
BACKEND_OUTETTS = outetts|python|.|false|true
BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
BACKEND_COQUI = coqui|python|.|false|true
BACKEND_RFDETR = rfdetr|python|.|false|true
@@ -564,14 +465,9 @@ 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_FISH_SPEECH = fish-speech|python|.|false|true
BACKEND_FASTER_QWEN3_TTS = faster-qwen3-tts|python|.|false|true
BACKEND_QWEN_ASR = qwen-asr|python|.|false|true
BACKEND_NEMO = nemo|python|.|false|true
BACKEND_VOXCPM = voxcpm|python|.|false|true
BACKEND_WHISPERX = whisperx|python|.|false|true
BACKEND_ACE_STEP = ace-step|python|.|false|true
BACKEND_MLX_DISTRIBUTED = mlx-distributed|python|./|false|true
# 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)
@@ -601,11 +497,8 @@ $(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_VOXTRAL)))
$(eval $(call generate-docker-build-target,$(BACKEND_OPUS)))
$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRANSFORMERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_OUTETTS)))
$(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)))
@@ -620,21 +513,15 @@ $(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_FISH_SPEECH)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_QWEN3_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_ASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEMO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACE_STEP)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACESTEP_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_DISTRIBUTED)))
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-transformers docker-build-outetts 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-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed
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
########################################################
### Mock Backend for E2E Tests
@@ -654,7 +541,6 @@ clean-mock-backend:
swagger:
swag init -g core/http/app.go --output swagger
# DEPRECATED: gen-assets is for the legacy Alpine.js UI. Remove when legacy UI is removed.
.PHONY: gen-assets
gen-assets:
$(GOCMD) run core/dependencies_manager/manager.go webui_static.yaml core/http/static/assets

119
README.md
View File

@@ -19,12 +19,6 @@
</a>
</p>
<p align="center">
<a href="LICENSE" target="blank">
<img src="https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge" alt="LocalAI License"/>
</a>
</p>
<p align="center">
<a href="https://hub.docker.com/r/localai/localai" target="blank">
<img src="https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker" alt="LocalAI Docker hub"/>
@@ -54,43 +48,8 @@
[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml)[![Build and Release](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml)[![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)[![Bump dependencies](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml)[![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/localai)](https://artifacthub.io/packages/search?repo=localai)
<p align="center">
<a href="https://github.com/mudler/LocalAI-examples" target="blank">
<img src="https://img.shields.io/badge/📦_Examples_Repository-Browse_Ready--to--Run_Examples-blue?style=for-the-badge" alt="LocalAI Examples Repository"/>
</a>
</p>
**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).
<details>
<summary><strong>Table of Contents</strong></summary>
- [Local Stack Family](#local-stack-family)
- [Screenshots / Video](#screenshots--video)
- [Quickstart](#-quickstart)
- [macOS Download](#macos-download)
- [Containers (Docker, podman, ...)](#containers-docker-podman-)
- [Latest project news](#-latest-project-news)
- [Features](#-features)
- [Supported Backends & Acceleration](#-supported-backends--acceleration)
- [Text Generation & Language Models](#text-generation--language-models)
- [Audio & Speech Processing](#audio--speech-processing)
- [Image & Video Generation](#image--video-generation)
- [Specialized AI Tasks](#specialized-ai-tasks)
- [Hardware Acceleration Matrix](#hardware-acceleration-matrix)
- [Community and integrations](#-community-and-integrations)
- [Resources](#-resources)
- [Media, Blogs, Social](#book--media-blogs-social)
- [Autonomous Development Team](#-autonomous-development-team)
- [Citation](#citation)
- [Sponsors](#-sponsors)
- [Individual sponsors](#individual-sponsors)
- [Star history](#-star-history)
- [License](#-license)
- [Acknowledgements](#-acknowledgements)
- [Contributors](#-contributors)
</details>
## Local Stack Family
@@ -118,23 +77,32 @@ Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tool
| Talk Interface | Generate Audio |
| --- | --- |
| ![LocalAI - Talk](./docs/assets/images/screenshots/screenshot_talk.png) | ![LocalAI - Generate Audio](./docs/assets/images/screenshots/screenshot_tts.png) |
| ![Screenshot 2025-03-31 at 12-01-36 LocalAI - Talk](./docs/assets/images/screenshots/screenshot_tts.png) | ![Screenshot 2025-03-31 at 12-01-29 LocalAI - Generate audio with voice-en-us-ryan-low](./docs/assets/images/screenshots/screenshot_tts.png) |
| Models Overview | Generate Images |
| --- | --- |
| ![LocalAI - Models](./docs/assets/images/screenshots/screenshot_gallery.png) | ![LocalAI - Generate Images](./docs/assets/images/screenshots/screenshot_image.png) |
| ![Screenshot 2025-03-31 at 12-01-20 LocalAI - Models](./docs/assets/images/screenshots/screenshot_gallery.png) | ![Screenshot 2025-03-31 at 12-31-41 LocalAI - Generate images with flux 1-dev](./docs/assets/images/screenshots/screenshot_image.png) |
| Chat Interface | Home |
| --- | --- |
| ![LocalAI - Chat](./docs/assets/images/screenshots/screenshot_chat.png) | ![LocalAI - Home](./docs/assets/images/screenshots/screenshot_home.png) |
| ![Screenshot 2025-03-31 at 11-57-44 LocalAI - Chat with localai-functioncall-qwen2 5-7b-v0 5](./docs/assets/images/screenshots/screenshot_chat.png) | ![Screenshot 2025-03-31 at 11-57-23 LocalAI API - c2a39e3 (c2a39e3639227cfd94ffffe9f5691239acc275a8)](./docs/assets/images/screenshots/screenshot_home.png) |
| Login | Swarm |
| --- | --- |
| ![LocalAI - Login](./docs/assets/images/screenshots/screenshot_login.png) | ![LocalAI - P2P Dashboard](./docs/assets/images/screenshots/screenshot_p2p.png) |
|![Screenshot 2025-03-31 at 12-09-59 ](./docs/assets/images/screenshots/screenshot_login.png) | ![Screenshot 2025-03-31 at 12-10-39 LocalAI - P2P dashboard](./docs/assets/images/screenshots/screenshot_p2p.png) |
## 💻 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
# Basic installation
curl https://localai.io/install.sh | sh
```
For more installation options, see [Installer Options](https://localai.io/installation/).
### macOS Download:
@@ -194,6 +162,27 @@ docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/de
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
```
#### 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
# Intel GPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel
# AMD GPU version
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-aio-gpu-hipblas
```
For more information about the AIO images and pre-downloaded models, see [Container Documentation](https://localai.io/basics/container/).
To load models:
```bash
@@ -214,9 +203,7 @@ local-ai run oci://localai/phi-2:latest
For more information, see [💻 Getting started](https://localai.io/basics/getting_started/index.html), if you are interested in our roadmap items and future enhancements, you can see the [Issues labeled as Roadmap here](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
## 📰 Latest project news
- March 2026: [Agent management](https://github.com/mudler/LocalAI/pull/8820), [New React UI](https://github.com/mudler/LocalAI/pull/8772), [WebRTC](https://github.com/mudler/LocalAI/pull/8790),[MLX-distributed via P2P and RDMA](https://github.com/mudler/LocalAI/pull/8801), [MCP Apps, MCP Client-side](https://github.com/mudler/LocalAI/pull/8947)
- February 2026: [Realtime API for audio-to-audio with tool calling](https://github.com/mudler/LocalAI/pull/6245), [ACE-Step 1.5 support](https://github.com/mudler/LocalAI/pull/8396)
- January 2026: **LocalAI 3.10.0** - Major release with Anthropic API support, Open Responses API for stateful agents, video & image generation suite (LTX-2), unified GPU backends, tool streaming & XML parsing, system-aware backend gallery, crash fixes for AVX-only CPUs and AMD VRAM reporting, request tracing, and new backends: **Moonshine** (ultra-fast transcription), **Pocket-TTS** (lightweight TTS). Vulkan arm64 builds now available. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v3.10.0).
- 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
@@ -229,7 +216,7 @@ For more information, see [💻 Getting started](https://localai.io/basics/getti
- May 2025: Important: image name changes [See release](https://github.com/mudler/LocalAI/releases/tag/v2.29.0)
- Apr 2025: Rebrand, WebUI enhancements
- Apr 2025: [LocalAGI](https://github.com/mudler/LocalAGI) and [LocalRecall](https://github.com/mudler/LocalRecall) join the LocalAI family stack.
- Apr 2025: WebUI overhaul
- Apr 2025: WebUI overhaul, AIO images updates
- Feb 2025: Backend cleanup, Breaking changes, new backends (kokoro, OutelTTS, faster-whisper), Nvidia L4T images
- Jan 2025: LocalAI model release: https://huggingface.co/mudler/LocalAI-functioncall-phi-4-v0.3, SANA support in diffusers: https://github.com/mudler/LocalAI/pull/4603
- Dec 2024: stablediffusion.cpp backend (ggml) added ( https://github.com/mudler/LocalAI/pull/4289 )
@@ -249,7 +236,7 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
- 🧩 [Backend Gallery](https://localai.io/backends/): Install/remove backends on the fly, powered by OCI images — fully customizable and API-driven.
- 📖 [Text generation with GPTs](https://localai.io/features/text-generation/) (`llama.cpp`, `transformers`, `vllm` ... [:book: and more](https://localai.io/model-compatibility/index.html#model-compatibility-table))
- 🗣 [Text to Audio](https://localai.io/features/text-to-audio/)
- 🔈 [Audio to Text](https://localai.io/features/audio-to-text/)
- 🔈 [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)
@@ -261,7 +248,6 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
- 📈 [Reranker API](https://localai.io/features/reranker/)
- 🆕🖧 [P2P Inferencing](https://localai.io/features/distribute/)
- 🆕🔌 [Model Context Protocol (MCP)](https://localai.io/docs/features/mcp/) - Agentic capabilities with external tools and [LocalAGI's Agentic capabilities](https://github.com/mudler/LocalAGI)
- 🆕🤖 [Built-in Agents](https://localai.io/features/agents/) - Autonomous AI agents with tool use, knowledge base (RAG), skills, SSE streaming, import/export, and [Agent Hub](https://agenthub.localai.io) — powered by [LocalAGI](https://github.com/mudler/LocalAGI)
- 🔊 Voice activity detection (Silero-VAD support)
- 🌍 Integrated WebUI!
@@ -277,14 +263,12 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
| **transformers** | HuggingFace transformers framework | CUDA 12/13, ROCm, Intel, CPU |
| **MLX** | Apple Silicon LLM inference | Metal (M1/M2/M3+) |
| **MLX-VLM** | Apple Silicon Vision-Language Models | Metal (M1/M2/M3+) |
| **vLLM Omni** | Multimodal vLLM with vision and audio | CUDA 12/13, ROCm, Intel |
### 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 |
| **moonshine** | Ultra-fast transcription engine for low-end devices | CUDA 12/13, Metal, 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 |
@@ -295,13 +279,6 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
| **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 |
| **nemo** | NVIDIA NeMo framework for speech models | CUDA 12/13, ROCm, Intel, CPU |
| **outetts** | OuteTTS with voice cloning | CUDA 12/13, CPU |
| **faster-qwen3-tts** | Faster Qwen3 TTS | CUDA 12/13, ROCm, Intel, CPU |
| **qwen-asr** | Qwen ASR speech recognition | CUDA 12/13, ROCm, Intel, CPU |
| **voxcpm** | VoxCPM speech understanding | CUDA 12/13, Metal, CPU |
| **whisperx** | Enhanced Whisper transcription | CUDA 12/13, ROCm, Intel, CPU |
| **ace-step** | Music generation from text descriptions, lyrics, or audio samples | CUDA 12/13, ROCm, Intel, Metal, CPU |
### Image & Video Generation
| Backend | Description | Acceleration Support |
@@ -323,11 +300,11 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
|-------------------|-------------------|------------------|
| **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, ace-step | AMD Graphics |
| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, coqui, kokoro, vibevoice, pocket-tts, qwen-tts, ace-step | Intel Arc, Intel iGPUs |
| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM, moonshine, ace-step | Apple M1/M2/M3+ |
| **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+ |
| **Vulkan** | llama.cpp, whisper, stablediffusion | Cross-platform GPUs |
| **NVIDIA Jetson (CUDA 12)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr, ace-step | ARM64 embedded AI (AGX Orin, etc.) |
| **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) |
| **CPU Optimized** | All backends | AVX/AVX2/AVX512, quantization support |
@@ -363,7 +340,7 @@ Other:
- Langchain: https://python.langchain.com/docs/integrations/providers/localai/
- Terminal utility https://github.com/djcopley/ShellOracle
- Local Smart assistant https://github.com/mudler/LocalAGI
- Home Assistant https://github.com/drndos/hass-openai-custom-conversation / https://github.com/valentinfrlch/ha-llmvision / https://github.com/loryanstrant/HA-LocalAI-Monitor
- Home Assistant https://github.com/sammcj/homeassistant-localai / https://github.com/drndos/hass-openai-custom-conversation / https://github.com/valentinfrlch/ha-gpt4vision
- Discord bot https://github.com/mudler/LocalAGI/tree/main/examples/discord
- Slack bot https://github.com/mudler/LocalAGI/tree/main/examples/slack
- Shell-Pilot(Interact with LLM using LocalAI models via pure shell scripts on your Linux or MacOS system) https://github.com/reid41/shell-pilot
@@ -385,8 +362,6 @@ Other:
## :book: 🎥 [Media, Blogs, Social](https://localai.io/basics/news/#media-blogs-social)
- 🆕 [LocalAI Autonomous Dev Team Blog Post](https://mudler.pm/posts/2026/02/28/a-call-to-open-source-maintainers-stop-babysitting-ai-how-i-built-a-100-local-autonomous-dev-team-to-maintain-localai-and-why-you-should-too/)
- [Run Visual studio code with LocalAI (SUSE)](https://www.suse.com/c/running-ai-locally/)
- 🆕 [Run LocalAI on Jetson Nano Devkit](https://mudler.pm/posts/local-ai-jetson-nano-devkit/)
- [Run LocalAI on AWS EKS with Pulumi](https://www.pulumi.com/blog/low-code-llm-apps-with-local-ai-flowise-and-pulumi/)
@@ -396,15 +371,6 @@ Other:
- [Question Answering on Documents locally with LangChain, LocalAI, Chroma, and GPT4All](https://mudler.pm/posts/localai-question-answering/)
- [Tutorial to use k8sgpt with LocalAI](https://medium.com/@tyler_97636/k8sgpt-localai-unlock-kubernetes-superpowers-for-free-584790de9b65)
## 🤖 Autonomous Development Team
LocalAI is now helped being maintained (for small tasks!) by a full team of autonomous AI agents led by an AI Scrum Master! This experiment demonstrates how open source projects can leverage AI agents for sustainable, long-term maintenance.
- **📊 Live Reports**: [Automatically generated reports](http://reports.localai.io)
- **📋 Project Board**: [Agent task tracking](https://github.com/users/mudler/projects/6)
- **📝 Blog Post**: [Learn about the autonomous dev team experiment](https://mudler.pm/posts/2026/02/28/a-call-to-open-source-maintainers-stop-babysitting-ai-how-i-built-a-100-local-autonomous-dev-team-to-maintain-localai-and-why-you-should-too/)
## Citation
If you utilize this repository, data in a downstream project, please consider citing it with:
@@ -461,7 +427,6 @@ LocalAI couldn't have been built without the help of great software already avai
- https://github.com/EdVince/Stable-Diffusion-NCNN
- https://github.com/ggerganov/whisper.cpp
- https://github.com/rhasspy/piper
- [exo](https://github.com/exo-explore/exo) for the MLX distributed auto-parallel sharding implementation
## 🤗 Contributors

View File

@@ -8,24 +8,10 @@ At LocalAI, we take the security of our software seriously. We understand the im
We provide support and updates for certain versions of our software. The following table outlines which versions are currently supported with security updates:
| Version Series | Support Level | Details |
| -------------- | ------------- | ------- |
| 3.x | :white_check_mark: Actively supported | Full security updates and bug fixes for the latest minor versions. |
| 2.x | :warning: Security fixes only | Critical security patches only, until **December 31, 2025**. |
| 1.x | :x: End-of-life (EOL) | No longer supported as of **January 1, 2024**. No security fixes will be provided. |
### What each support level means
- **Actively supported (3.x):** Receives all security updates, bug fixes, and new features. Users should stay on the latest 3.x minor release for the best protection.
- **Security fixes only (2.x):** Receives only critical security patches (e.g., remote code execution, authentication bypass, data exposure). No bug fixes or new features. Support ends December 31, 2025.
- **End-of-life (1.x):** No updates of any kind. Users on 1.x are strongly encouraged to upgrade immediately, as known vulnerabilities will not be patched.
### Migrating from older versions
If you are running an unsupported or soon-to-be-unsupported version, we recommend upgrading as soon as possible:
- **From 1.x to 3.x:** Version 1.x reached end-of-life on January 1, 2024. Review the [release notes](https://github.com/mudler/LocalAI/releases) for breaking changes across major versions, and upgrade directly to the latest 3.x release.
- **From 2.x to 3.x:** While 2.x still receives critical security patches until December 31, 2025, we recommend planning your migration to 3.x to benefit from ongoing improvements and full support.
| Version | Supported |
| ------- | ------------------ |
| > 2.0 | :white_check_mark: |
| < 2.0 | :x: |
Please ensure that you are using a supported version to receive the latest security updates.

5
aio/cpu/README.md Normal file
View File

@@ -0,0 +1,5 @@
## AIO CPU size
Use this image with CPU-only.
Please keep using only C++ backends so the base image is as small as possible (without CUDA, cuDNN, python, etc).

13
aio/cpu/embeddings.yaml Normal file
View File

@@ -0,0 +1,13 @@
embeddings: true
name: text-embedding-ada-002
backend: llama-cpp
parameters:
model: huggingface://bartowski/granite-embedding-107m-multilingual-GGUF/granite-embedding-107m-multilingual-f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'

View File

@@ -12,3 +12,12 @@ download_files:
- filename: "stable-diffusion-v1-5-pruned-emaonly-Q4_0.gguf"
sha256: "b8944e9fe0b69b36ae1b5bb0185b3a7b8ef14347fe0fa9af6c64c4829022261f"
uri: "huggingface://second-state/stable-diffusion-v1-5-GGUF/stable-diffusion-v1-5-pruned-emaonly-Q4_0.gguf"
usage: |
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"step": 25,
"size": "512x512"
}'

33
aio/cpu/rerank.yaml Normal file
View File

@@ -0,0 +1,33 @@
name: jina-reranker-v1-base-en
reranking: true
f16: true
parameters:
model: jina-reranker-v1-tiny-en.f16.gguf
backend: llama-cpp
download_files:
- filename: jina-reranker-v1-tiny-en.f16.gguf
sha256: 5f696cf0d0f3d347c4a279eee8270e5918554cdac0ed1f632f2619e4e8341407
uri: huggingface://mradermacher/jina-reranker-v1-tiny-en-GGUF/jina-reranker-v1-tiny-en.f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/v1/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "jina-reranker-v1-base-en",
"query": "Organic skincare products for sensitive skin",
"documents": [
"Eco-friendly kitchenware for modern homes",
"Biodegradable cleaning supplies for eco-conscious consumers",
"Organic cotton baby clothes for sensitive skin",
"Natural organic skincare range for sensitive skin",
"Tech gadgets for smart homes: 2024 edition",
"Sustainable gardening tools and compost solutions",
"Sensitive skin-friendly facial cleansers and toners",
"Organic food wraps and storage solutions",
"All-natural pet food for dogs with allergies",
"Yoga mats made from recycled materials"
],
"top_n": 3
}'

View File

@@ -0,0 +1,18 @@
name: whisper-1
backend: whisper
parameters:
model: ggml-whisper-base.bin
usage: |
## example audio file
wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
## Send the example audio file to the transcriptions endpoint
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@$PWD/gb1.ogg" -F model="whisper-1"
download_files:
- filename: "ggml-whisper-base.bin"
sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"

View File

@@ -0,0 +1,15 @@
name: tts-1
download_files:
- filename: voice-en-us-amy-low.tar.gz
uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
backend: piper
parameters:
model: en-us-amy-low.onnx
usage: |
To test if this model works as expected, you can use the following curl command:
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
"model":"voice-en-us-amy-low",
"input": "Hi, this is a test."
}'

View File

@@ -55,4 +55,4 @@ template:
download_files:
- filename: Hermes-3-Llama-3.2-3B-Q4_K_M.gguf
sha256: 2e220a14ba4328fee38cf36c2c068261560f999fadb5725ce5c6d977cb5126b5
uri: huggingface://bartowski/Hermes-3-Llama-3.2-3B-GGUF/Hermes-3-Llama-3.2-3B-Q4_K_M.gguf
uri: huggingface://bartowski/Hermes-3-Llama-3.2-3B-GGUF/Hermes-3-Llama-3.2-3B-Q4_K_M.gguf

View File

@@ -1,8 +1,8 @@
backend: silero-vad
name: silero-vad
parameters:
model: silero-vad.onnx
download_files:
- filename: silero-vad.onnx
uri: https://huggingface.co/onnx-community/silero-vad/resolve/main/onnx/model.onnx
sha256: a4a068cd6cf1ea8355b84327595838ca748ec29a25bc91fc82e6c299ccdc5808
backend: silero-vad
name: silero-vad
parameters:
model: silero-vad.onnx
download_files:
- filename: silero-vad.onnx
uri: https://huggingface.co/onnx-community/silero-vad/resolve/main/onnx/model.onnx
sha256: a4a068cd6cf1ea8355b84327595838ca748ec29a25bc91fc82e6c299ccdc5808

View File

@@ -47,4 +47,4 @@ download_files:
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/ggml-model-Q4_K_M.gguf
- filename: minicpm-v-4_5-mmproj-f16.gguf
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/mmproj-model-f16.gguf
sha256: 7a7225a32e8d453aaa3d22d8c579b5bf833c253f784cdb05c99c9a76fd616df8
sha256: 7a7225a32e8d453aaa3d22d8c579b5bf833c253f784cdb05c99c9a76fd616df8

138
aio/entrypoint.sh Executable file
View File

@@ -0,0 +1,138 @@
#!/bin/bash
echo "===> LocalAI All-in-One (AIO) container starting..."
GPU_ACCELERATION=false
GPU_VENDOR=""
function check_intel() {
if lspci | grep -E 'VGA|3D' | grep -iq intel; then
echo "Intel GPU detected"
if [ -d /opt/intel ]; then
GPU_ACCELERATION=true
GPU_VENDOR=intel
else
echo "Intel GPU detected, but Intel GPU drivers are not installed. GPU acceleration will not be available."
fi
fi
}
function check_nvidia_wsl() {
if lspci | grep -E 'VGA|3D' | grep -iq "Microsoft Corporation Device 008e"; then
# We make the assumption this WSL2 cars is NVIDIA, then check for nvidia-smi
# Make sure the container was run with `--gpus all` as the only required parameter
echo "NVIDIA GPU detected via WSL2"
# nvidia-smi should be installed in the container
if nvidia-smi; then
GPU_ACCELERATION=true
GPU_VENDOR=nvidia
else
echo "NVIDIA GPU detected via WSL2, but nvidia-smi is not installed. GPU acceleration will not be available."
fi
fi
}
function check_amd() {
if lspci | grep -E 'VGA|3D' | grep -iq amd; then
echo "AMD GPU detected"
# Check if ROCm is installed
if [ -d /opt/rocm ]; then
GPU_ACCELERATION=true
GPU_VENDOR=amd
else
echo "AMD GPU detected, but ROCm is not installed. GPU acceleration will not be available."
fi
fi
}
function check_nvidia() {
if lspci | grep -E 'VGA|3D' | grep -iq nvidia; then
echo "NVIDIA GPU detected"
# nvidia-smi should be installed in the container
if nvidia-smi; then
GPU_ACCELERATION=true
GPU_VENDOR=nvidia
else
echo "NVIDIA GPU detected, but nvidia-smi is not installed. GPU acceleration will not be available."
fi
fi
}
function check_metal() {
if system_profiler SPDisplaysDataType | grep -iq 'Metal'; then
echo "Apple Metal supported GPU detected"
GPU_ACCELERATION=true
GPU_VENDOR=apple
fi
}
function detect_gpu() {
case "$(uname -s)" in
Linux)
check_nvidia
check_amd
check_intel
check_nvidia_wsl
;;
Darwin)
check_metal
;;
esac
}
function detect_gpu_size() {
# Attempting to find GPU memory size for NVIDIA GPUs
if [ "$GPU_ACCELERATION" = true ] && [ "$GPU_VENDOR" = "nvidia" ]; then
echo "NVIDIA GPU detected. Attempting to find memory size..."
# Using head -n 1 to get the total memory of the 1st NVIDIA GPU detected.
# If handling multiple GPUs is required in the future, this is the place to do it
nvidia_sm=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -n 1)
if [ ! -z "$nvidia_sm" ]; then
echo "Total GPU Memory: $nvidia_sm MiB"
# if bigger than 8GB, use 16GB
#if [ "$nvidia_sm" -gt 8192 ]; then
# GPU_SIZE=gpu-16g
#else
GPU_SIZE=gpu-8g
#fi
else
echo "Unable to determine NVIDIA GPU memory size. Falling back to CPU."
GPU_SIZE=gpu-8g
fi
elif [ "$GPU_ACCELERATION" = true ] && [ "$GPU_VENDOR" = "intel" ]; then
GPU_SIZE=intel
# Default to a generic GPU size until we implement GPU size detection for non NVIDIA GPUs
elif [ "$GPU_ACCELERATION" = true ]; then
echo "Non-NVIDIA GPU detected. Specific GPU memory size detection is not implemented."
GPU_SIZE=gpu-8g
# default to cpu if GPU_SIZE is not set
else
echo "GPU acceleration is not enabled or supported. Defaulting to CPU."
GPU_SIZE=cpu
fi
}
function check_vars() {
if [ -z "$MODELS" ]; then
echo "MODELS environment variable is not set. Please set it to a comma-separated list of model YAML files to load."
exit 1
fi
if [ -z "$PROFILE" ]; then
echo "PROFILE environment variable is not set. Please set it to one of the following: cpu, gpu-8g, gpu-16g, apple"
exit 1
fi
}
detect_gpu
detect_gpu_size
PROFILE="${PROFILE:-$GPU_SIZE}" # default to cpu
export MODELS="${MODELS:-/aio/${PROFILE}/embeddings.yaml,/aio/${PROFILE}/rerank.yaml,/aio/${PROFILE}/text-to-speech.yaml,/aio/${PROFILE}/image-gen.yaml,/aio/${PROFILE}/text-to-text.yaml,/aio/${PROFILE}/speech-to-text.yaml,/aio/${PROFILE}/vad.yaml,/aio/${PROFILE}/vision.yaml}"
check_vars
echo "===> Starting LocalAI[$PROFILE] with the following models: $MODELS"
exec /entrypoint.sh "$@"

View File

@@ -0,0 +1,13 @@
embeddings: true
name: text-embedding-ada-002
backend: llama-cpp
parameters:
model: huggingface://bartowski/granite-embedding-107m-multilingual-GGUF/granite-embedding-107m-multilingual-f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'

25
aio/gpu-8g/image-gen.yaml Normal file
View File

@@ -0,0 +1,25 @@
name: stablediffusion
parameters:
model: DreamShaper_8_pruned.safetensors
backend: diffusers
step: 25
f16: true
diffusers:
pipeline_type: StableDiffusionPipeline
cuda: true
enable_parameters: "negative_prompt,num_inference_steps"
scheduler_type: "k_dpmpp_2m"
download_files:
- filename: DreamShaper_8_pruned.safetensors
uri: huggingface://Lykon/DreamShaper/DreamShaper_8_pruned.safetensors
usage: |
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"step": 25,
"size": "512x512"
}'

33
aio/gpu-8g/rerank.yaml Normal file
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@@ -0,0 +1,33 @@
name: jina-reranker-v1-base-en
reranking: true
f16: true
parameters:
model: jina-reranker-v1-tiny-en.f16.gguf
backend: llama-cpp
download_files:
- filename: jina-reranker-v1-tiny-en.f16.gguf
sha256: 5f696cf0d0f3d347c4a279eee8270e5918554cdac0ed1f632f2619e4e8341407
uri: huggingface://mradermacher/jina-reranker-v1-tiny-en-GGUF/jina-reranker-v1-tiny-en.f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/v1/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "jina-reranker-v1-base-en",
"query": "Organic skincare products for sensitive skin",
"documents": [
"Eco-friendly kitchenware for modern homes",
"Biodegradable cleaning supplies for eco-conscious consumers",
"Organic cotton baby clothes for sensitive skin",
"Natural organic skincare range for sensitive skin",
"Tech gadgets for smart homes: 2024 edition",
"Sustainable gardening tools and compost solutions",
"Sensitive skin-friendly facial cleansers and toners",
"Organic food wraps and storage solutions",
"All-natural pet food for dogs with allergies",
"Yoga mats made from recycled materials"
],
"top_n": 3
}'

View File

@@ -0,0 +1,18 @@
name: whisper-1
backend: whisper
parameters:
model: ggml-whisper-base.bin
usage: |
## example audio file
wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
## Send the example audio file to the transcriptions endpoint
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@$PWD/gb1.ogg" -F model="whisper-1"
download_files:
- filename: "ggml-whisper-base.bin"
sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"

View File

@@ -0,0 +1,15 @@
name: tts-1
download_files:
- filename: voice-en-us-amy-low.tar.gz
uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
backend: piper
parameters:
model: en-us-amy-low.onnx
usage: |
To test if this model works as expected, you can use the following curl command:
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
"model":"tts-1",
"input": "Hi, this is a test."
}'

View File

@@ -0,0 +1,54 @@
context_size: 4096
f16: true
backend: llama-cpp
function:
capture_llm_results:
- (?s)<Thought>(.*?)</Thought>
grammar:
properties_order: name,arguments
json_regex_match:
- (?s)<Output>(.*?)</Output>
replace_llm_results:
- key: (?s)<Thought>(.*?)</Thought>
value: ""
mmap: true
name: gpt-4
parameters:
model: localai-functioncall-qwen2.5-7b-v0.5-q4_k_m.gguf
stopwords:
- <|im_end|>
- <dummy32000>
- </s>
template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{ .RoleName }}
{{ if .FunctionCall -}}
Function call:
{{ else if eq .RoleName "tool" -}}
Function response:
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if .FunctionCall -}}
{{toJson .FunctionCall}}
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are an AI assistant that executes function calls, and these are the tools at your disposal:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
<|im_end|>
{{.Input -}}
<|im_start|>assistant
download_files:
- filename: localai-functioncall-qwen2.5-7b-v0.5-q4_k_m.gguf
sha256: 4e7b7fe1d54b881f1ef90799219dc6cc285d29db24f559c8998d1addb35713d4
uri: huggingface://mudler/LocalAI-functioncall-qwen2.5-7b-v0.5-Q4_K_M-GGUF/localai-functioncall-qwen2.5-7b-v0.5-q4_k_m.gguf

8
aio/gpu-8g/vad.yaml Normal file
View File

@@ -0,0 +1,8 @@
backend: silero-vad
name: silero-vad
parameters:
model: silero-vad.onnx
download_files:
- filename: silero-vad.onnx
uri: https://huggingface.co/onnx-community/silero-vad/resolve/main/onnx/model.onnx
sha256: a4a068cd6cf1ea8355b84327595838ca748ec29a25bc91fc82e6c299ccdc5808

50
aio/gpu-8g/vision.yaml Normal file
View File

@@ -0,0 +1,50 @@
context_size: 4096
backend: llama-cpp
f16: true
mmap: true
mmproj: minicpm-v-4_5-mmproj-f16.gguf
name: gpt-4o
parameters:
model: minicpm-v-4_5-Q4_K_M.gguf
stopwords:
- <|im_end|>
- <dummy32000>
- </s>
- <|endoftext|>
template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{ .RoleName }}
{{ if .FunctionCall -}}
Function call:
{{ else if eq .RoleName "tool" -}}
Function response:
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if .FunctionCall -}}
{{toJson .FunctionCall}}
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are a function calling AI model. You are provided with functions to execute. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
For each function call return a json object with function name and arguments
<|im_end|>
{{.Input -}}
<|im_start|>assistant
download_files:
- filename: minicpm-v-4_5-Q4_K_M.gguf
sha256: c1c3c33100b15b4caf7319acce4e23c0eb0ce1cbd12f70e8d24f05aa67b7512f
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/ggml-model-Q4_K_M.gguf
- filename: minicpm-v-4_5-mmproj-f16.gguf
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/mmproj-model-f16.gguf
sha256: 7a7225a32e8d453aaa3d22d8c579b5bf833c253f784cdb05c99c9a76fd616df8

13
aio/intel/embeddings.yaml Normal file
View File

@@ -0,0 +1,13 @@
embeddings: true
name: text-embedding-ada-002
backend: llama-cpp
parameters:
model: huggingface://bartowski/granite-embedding-107m-multilingual-GGUF/granite-embedding-107m-multilingual-f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'

20
aio/intel/image-gen.yaml Normal file
View File

@@ -0,0 +1,20 @@
name: stablediffusion
parameters:
model: Lykon/dreamshaper-8
backend: diffusers
step: 25
f16: true
diffusers:
pipeline_type: StableDiffusionPipeline
cuda: true
enable_parameters: "negative_prompt,num_inference_steps"
scheduler_type: "k_dpmpp_2m"
usage: |
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"step": 25,
"size": "512x512"
}'

33
aio/intel/rerank.yaml Normal file
View File

@@ -0,0 +1,33 @@
name: jina-reranker-v1-base-en
reranking: true
f16: true
parameters:
model: jina-reranker-v1-tiny-en.f16.gguf
backend: llama-cpp
download_files:
- filename: jina-reranker-v1-tiny-en.f16.gguf
sha256: 5f696cf0d0f3d347c4a279eee8270e5918554cdac0ed1f632f2619e4e8341407
uri: huggingface://mradermacher/jina-reranker-v1-tiny-en-GGUF/jina-reranker-v1-tiny-en.f16.gguf
usage: |
You can test this model with curl like this:
curl http://localhost:8080/v1/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "jina-reranker-v1-base-en",
"query": "Organic skincare products for sensitive skin",
"documents": [
"Eco-friendly kitchenware for modern homes",
"Biodegradable cleaning supplies for eco-conscious consumers",
"Organic cotton baby clothes for sensitive skin",
"Natural organic skincare range for sensitive skin",
"Tech gadgets for smart homes: 2024 edition",
"Sustainable gardening tools and compost solutions",
"Sensitive skin-friendly facial cleansers and toners",
"Organic food wraps and storage solutions",
"All-natural pet food for dogs with allergies",
"Yoga mats made from recycled materials"
],
"top_n": 3
}'

View File

@@ -0,0 +1,18 @@
name: whisper-1
backend: whisper
parameters:
model: ggml-whisper-base.bin
usage: |
## example audio file
wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
## Send the example audio file to the transcriptions endpoint
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@$PWD/gb1.ogg" -F model="whisper-1"
download_files:
- filename: "ggml-whisper-base.bin"
sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"

View File

@@ -0,0 +1,15 @@
name: tts-1
download_files:
- filename: voice-en-us-amy-low.tar.gz
uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
backend: piper
parameters:
model: en-us-amy-low.onnx
usage: |
To test if this model works as expected, you can use the following curl command:
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
"model":"tts-1",
"input": "Hi, this is a test."
}'

View File

@@ -0,0 +1,54 @@
context_size: 4096
f16: true
backend: llama-cpp
function:
capture_llm_results:
- (?s)<Thought>(.*?)</Thought>
grammar:
properties_order: name,arguments
json_regex_match:
- (?s)<Output>(.*?)</Output>
replace_llm_results:
- key: (?s)<Thought>(.*?)</Thought>
value: ""
mmap: true
name: gpt-4
parameters:
model: localai-functioncall-qwen2.5-7b-v0.5-q4_k_m.gguf
stopwords:
- <|im_end|>
- <dummy32000>
- </s>
template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{ .RoleName }}
{{ if .FunctionCall -}}
Function call:
{{ else if eq .RoleName "tool" -}}
Function response:
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if .FunctionCall -}}
{{toJson .FunctionCall}}
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are an AI assistant that executes function calls, and these are the tools at your disposal:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
<|im_end|>
{{.Input -}}
<|im_start|>assistant
download_files:
- filename: localai-functioncall-phi-4-v0.3-q4_k_m.gguf
sha256: 23fee048ded2a6e2e1a7b6bbefa6cbf83068f194caa9552aecbaa00fec8a16d5
uri: huggingface://mudler/LocalAI-functioncall-phi-4-v0.3-Q4_K_M-GGUF/localai-functioncall-phi-4-v0.3-q4_k_m.gguf

8
aio/intel/vad.yaml Normal file
View File

@@ -0,0 +1,8 @@
backend: silero-vad
name: silero-vad
parameters:
model: silero-vad.onnx
download_files:
- filename: silero-vad.onnx
uri: https://huggingface.co/onnx-community/silero-vad/resolve/main/onnx/model.onnx
sha256: a4a068cd6cf1ea8355b84327595838ca748ec29a25bc91fc82e6c299ccdc5808

51
aio/intel/vision.yaml Normal file
View File

@@ -0,0 +1,51 @@
context_size: 4096
backend: llama-cpp
f16: true
mmap: true
mmproj: minicpm-v-4_5-mmproj-f16.gguf
name: gpt-4o
parameters:
model: minicpm-v-4_5-Q4_K_M.gguf
stopwords:
- <|im_end|>
- <dummy32000>
- </s>
- <|endoftext|>
template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{ .RoleName }}
{{ if .FunctionCall -}}
Function call:
{{ else if eq .RoleName "tool" -}}
Function response:
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if .FunctionCall -}}
{{toJson .FunctionCall}}
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are a function calling AI model. You are provided with functions to execute. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
For each function call return a json object with function name and arguments
<|im_end|>
{{.Input -}}
<|im_start|>assistant
download_files:
- filename: minicpm-v-4_5-Q4_K_M.gguf
sha256: c1c3c33100b15b4caf7319acce4e23c0eb0ce1cbd12f70e8d24f05aa67b7512f
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/ggml-model-Q4_K_M.gguf
- filename: minicpm-v-4_5-mmproj-f16.gguf
uri: huggingface://openbmb/MiniCPM-V-4_5-gguf/mmproj-model-f16.gguf
sha256: 7a7225a32e8d453aaa3d22d8c579b5bf833c253f784cdb05c99c9a76fd616df8

View File

@@ -20,7 +20,7 @@ RUN apt-get update && \
build-essential \
git ccache \
ca-certificates \
make cmake wget libopenblas-dev \
make cmake wget \
curl unzip \
libssl-dev && \
apt-get clean && \
@@ -180,11 +180,6 @@ RUN <<EOT bash
fi
EOT
RUN if [ "${BACKEND}" = "opus" ]; then \
apt-get update && apt-get install -y --no-install-recommends libopus-dev pkg-config && \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
fi
COPY . /LocalAI
RUN git config --global --add safe.directory /LocalAI

View File

@@ -202,11 +202,6 @@ 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 "/${BACKEND}/lib"
# Run backend-specific packaging if a package.sh exists
RUN if [ -f "/${BACKEND}/package.sh" ]; then \
cd /${BACKEND} && bash package.sh; \
fi
FROM scratch
ARG BACKEND=rerankers
COPY --from=builder /${BACKEND}/ /

View File

@@ -53,6 +53,7 @@ The backend system provides language-specific Dockerfiles that handle the build
#### Go Backends (`go/`)
- **whisper**: OpenAI Whisper speech recognition in Go with GGML cpp backend (whisper.cpp)
- **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
- **local-store**: Vector storage backend

View File

@@ -9,7 +9,6 @@ package backend;
service Backend {
rpc Health(HealthMessage) returns (Reply) {}
rpc Free(HealthMessage) returns (Result) {}
rpc Predict(PredictOptions) returns (Reply) {}
rpc LoadModel(ModelOptions) returns (Result) {}
rpc PredictStream(PredictOptions) returns (stream Reply) {}
@@ -35,9 +34,6 @@ service Backend {
rpc VAD(VADRequest) returns (VADResponse) {}
rpc AudioEncode(AudioEncodeRequest) returns (AudioEncodeResult) {}
rpc AudioDecode(AudioDecodeRequest) returns (AudioDecodeResult) {}
rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
}
@@ -165,23 +161,6 @@ message PredictOptions {
string ToolChoice = 49; // JSON string or object specifying tool choice behavior
int32 Logprobs = 50; // Number of top logprobs to return (maps to OpenAI logprobs parameter)
int32 TopLogprobs = 51; // Number of top logprobs to return per token (maps to OpenAI top_logprobs parameter)
map<string, string> Metadata = 52; // Generic per-request metadata (e.g., enable_thinking)
}
// ToolCallDelta represents an incremental tool call update from the C++ parser.
// Used for both streaming (partial diffs) and non-streaming (final tool calls).
message ToolCallDelta {
int32 index = 1; // tool call index (0-based)
string id = 2; // tool call ID (e.g., "call_abc123")
string name = 3; // function name (set on first appearance)
string arguments = 4; // arguments chunk (incremental in streaming, full in non-streaming)
}
// ChatDelta represents incremental content/reasoning/tool_call updates parsed by the C++ backend.
message ChatDelta {
string content = 1; // content text delta
string reasoning_content = 2; // reasoning/thinking text delta
repeated ToolCallDelta tool_calls = 3; // tool call deltas
}
// The response message containing the result
@@ -193,7 +172,6 @@ message Reply {
double timing_token_generation = 5;
bytes audio = 6;
bytes logprobs = 7; // JSON-encoded logprobs data matching OpenAI format
repeated ChatDelta chat_deltas = 8; // Parsed chat deltas from C++ autoparser (streaming + non-streaming)
}
message GrammarTrigger {
@@ -387,14 +365,6 @@ message SoundGenerationRequest {
optional bool sample = 6;
optional string src = 7;
optional int32 src_divisor = 8;
optional bool think = 9;
optional string caption = 10;
optional string lyrics = 11;
optional int32 bpm = 12;
optional string keyscale = 13;
optional string language = 14;
optional string timesignature = 15;
optional bool instrumental = 17;
}
message TokenizationResponse {
@@ -445,86 +415,7 @@ message DetectResponse {
repeated Detection Detections = 1;
}
message ToolFormatMarkers {
string format_type = 1; // "json_native", "tag_with_json", "tag_with_tagged"
// Tool section markers
string section_start = 2; // e.g., "<tool_call>", "[TOOL_CALLS]"
string section_end = 3; // e.g., "</tool_call>"
string per_call_start = 4; // e.g., "<|tool_call_begin|>"
string per_call_end = 5; // e.g., "<|tool_call_end|>"
// Function name markers (TAG_WITH_JSON / TAG_WITH_TAGGED)
string func_name_prefix = 6; // e.g., "<function="
string func_name_suffix = 7; // e.g., ">"
string func_close = 8; // e.g., "</function>"
// Argument markers (TAG_WITH_TAGGED)
string arg_name_prefix = 9; // e.g., "<param="
string arg_name_suffix = 10; // e.g., ">"
string arg_value_prefix = 11;
string arg_value_suffix = 12; // e.g., "</param>"
string arg_separator = 13; // e.g., "\n"
// JSON format fields (JSON_NATIVE)
string name_field = 14; // e.g., "name"
string args_field = 15; // e.g., "arguments"
string id_field = 16; // e.g., "id"
bool fun_name_is_key = 17;
bool tools_array_wrapped = 18;
bool uses_python_dicts = 19;
// Reasoning markers
string reasoning_start = 20; // e.g., "<think>"
string reasoning_end = 21; // e.g., "</think>"
// Content markers
string content_start = 22;
string content_end = 23;
// Args wrapper markers
string args_start = 24; // e.g., "<args>"
string args_end = 25; // e.g., "</args>"
// JSON parameter ordering
string function_field = 26; // e.g., "function" (wrapper key in JSON)
repeated string parameter_order = 27;
// Generated ID field (alternative field name for generated IDs)
string gen_id_field = 28; // e.g., "call_id"
// Call ID markers (position and delimiters for tool call IDs)
string call_id_position = 29; // "none", "pre_func_name", "between_func_and_args", "post_args"
string call_id_prefix = 30; // e.g., "[CALL_ID]"
string call_id_suffix = 31; // e.g., ""
}
message AudioEncodeRequest {
bytes pcm_data = 1;
int32 sample_rate = 2;
int32 channels = 3;
map<string, string> options = 4;
}
message AudioEncodeResult {
repeated bytes frames = 1;
int32 sample_rate = 2;
int32 samples_per_frame = 3;
}
message AudioDecodeRequest {
repeated bytes frames = 1;
map<string, string> options = 2;
}
message AudioDecodeResult {
bytes pcm_data = 1;
int32 sample_rate = 2;
int32 samples_per_frame = 3;
}
message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)
ToolFormatMarkers tool_format = 3; // Auto-detected tool format markers from differential template analysis
}

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=e30f1fdf74ea9238ff562901aa974c75aab6619b
LLAMA_VERSION?=2634ed207a17db1a54bd8df0555bd8499a6ab691
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=

View File

@@ -17,7 +17,6 @@
#include "backend.pb.h"
#include "backend.grpc.pb.h"
#include "common.h"
#include "chat-auto-parser.h"
#include <getopt.h>
#include <grpcpp/ext/proto_server_reflection_plugin.h>
#include <grpcpp/grpcpp.h>
@@ -363,7 +362,7 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
params.mmproj.path = request->mmproj();
}
// params.model_alias ??
params.model_alias.insert(request->modelfile());
params.model_alias = request->modelfile();
if (!request->cachetypekey().empty()) {
params.cache_type_k = kv_cache_type_from_str(request->cachetypekey());
}
@@ -418,12 +417,6 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// n_ctx_checkpoints: max context checkpoints per slot (default: 8)
params.n_ctx_checkpoints = 8;
// llama memory fit fails if we don't provide a buffer for tensor overrides
const size_t ntbo = llama_max_tensor_buft_overrides();
while (params.tensor_buft_overrides.size() < ntbo) {
params.tensor_buft_overrides.push_back({nullptr, nullptr});
}
// decode options. Options are in form optname:optvale, or if booleans only optname.
for (int i = 0; i < request->options_size(); i++) {
std::string opt = request->options(i);
@@ -867,56 +860,6 @@ public:
return logprobs_json;
}
// Helper: populate chat_deltas on a Reply from oaicompat_msg_diffs (streaming chunks)
static void populate_chat_deltas_from_diffs(backend::Reply & reply,
const std::vector<common_chat_msg_diff> & diffs) {
for (const auto & diff : diffs) {
auto* delta = reply.add_chat_deltas();
if (!diff.content_delta.empty()) {
delta->set_content(diff.content_delta);
}
if (!diff.reasoning_content_delta.empty()) {
delta->set_reasoning_content(diff.reasoning_content_delta);
}
if (diff.tool_call_index != std::string::npos) {
auto* tc = delta->add_tool_calls();
tc->set_index(static_cast<int32_t>(diff.tool_call_index));
if (!diff.tool_call_delta.id.empty()) {
tc->set_id(diff.tool_call_delta.id);
}
if (!diff.tool_call_delta.name.empty()) {
tc->set_name(diff.tool_call_delta.name);
}
if (!diff.tool_call_delta.arguments.empty()) {
tc->set_arguments(diff.tool_call_delta.arguments);
}
}
}
}
// Helper: populate chat_deltas on a Reply from final oaicompat_msg (non-streaming)
static void populate_chat_deltas_from_final(backend::Reply & reply,
const common_chat_msg & msg) {
// Content delta
if (!msg.content.empty() || !msg.reasoning_content.empty() || !msg.tool_calls.empty()) {
auto* delta = reply.add_chat_deltas();
if (!msg.content.empty()) {
delta->set_content(msg.content);
}
if (!msg.reasoning_content.empty()) {
delta->set_reasoning_content(msg.reasoning_content);
}
// Tool calls as individual deltas within the same ChatDelta
for (size_t i = 0; i < msg.tool_calls.size(); i++) {
auto* tc = delta->add_tool_calls();
tc->set_index(static_cast<int32_t>(i));
tc->set_id(msg.tool_calls[i].id);
tc->set_name(msg.tool_calls[i].name);
tc->set_arguments(msg.tool_calls[i].arguments);
}
}
}
grpc::Status PredictStream(grpc::ServerContext* context, const backend::PredictOptions* request, grpc::ServerWriter<backend::Reply>* writer) override {
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
@@ -1312,52 +1255,6 @@ public:
body_json["add_generation_prompt"] = data["add_generation_prompt"];
}
// Pass sampling parameters to body_json so oaicompat_chat_params_parse respects them
// and doesn't overwrite them with defaults in the returned parsed_data
if (data.contains("n_predict")) {
body_json["max_tokens"] = data["n_predict"];
}
if (data.contains("ignore_eos")) {
body_json["ignore_eos"] = data["ignore_eos"];
}
if (data.contains("stop")) {
body_json["stop"] = data["stop"];
}
if (data.contains("temperature")) {
body_json["temperature"] = data["temperature"];
}
if (data.contains("top_p")) {
body_json["top_p"] = data["top_p"];
}
if (data.contains("frequency_penalty")) {
body_json["frequency_penalty"] = data["frequency_penalty"];
}
if (data.contains("presence_penalty")) {
body_json["presence_penalty"] = data["presence_penalty"];
}
if (data.contains("seed")) {
body_json["seed"] = data["seed"];
}
if (data.contains("logit_bias")) {
body_json["logit_bias"] = data["logit_bias"];
}
if (data.contains("top_k")) {
body_json["top_k"] = data["top_k"];
}
if (data.contains("min_p")) {
body_json["min_p"] = data["min_p"];
}
// Pass enable_thinking via chat_template_kwargs (where oaicompat_chat_params_parse reads it)
const auto& metadata = request->metadata();
auto et_it = metadata.find("enable_thinking");
if (et_it != metadata.end()) {
if (!body_json.contains("chat_template_kwargs")) {
body_json["chat_template_kwargs"] = json::object();
}
body_json["chat_template_kwargs"]["enable_thinking"] = (et_it->second == "true");
}
// Debug: Print full body_json before template processing (includes messages, tools, tool_choice, etc.)
SRV_DBG("[CONVERSATION DEBUG] PredictStream: Full body_json before oaicompat_chat_params_parse:\n%s\n", body_json.dump(2).c_str());
@@ -1538,76 +1435,127 @@ public:
return grpc::Status(grpc::StatusCode::INTERNAL, error_json.value("message", "Error occurred"));
}
// Lambda to build a Reply from JSON + attach chat deltas from a result
auto build_reply_from_json = [](const json & res_json, server_task_result * raw_result) -> backend::Reply {
backend::Reply reply;
std::string completion_text = res_json.value("content", "");
reply.set_message(completion_text);
reply.set_tokens(res_json.value("tokens_predicted", 0));
reply.set_prompt_tokens(res_json.value("tokens_evaluated", 0));
if (res_json.contains("timings")) {
reply.set_timing_prompt_processing(res_json.at("timings").value("prompt_ms", 0.0));
reply.set_timing_token_generation(res_json.at("timings").value("predicted_ms", 0.0));
}
json logprobs_json = extract_logprobs_from_json(res_json);
if (!logprobs_json.empty() && !logprobs_json.is_null()) {
reply.set_logprobs(logprobs_json.dump());
}
return reply;
};
auto attach_chat_deltas = [](backend::Reply & reply, server_task_result * raw_result) {
// Try streaming partial result first
auto* partial = dynamic_cast<server_task_result_cmpl_partial*>(raw_result);
if (partial && !partial->oaicompat_msg_diffs.empty()) {
populate_chat_deltas_from_diffs(reply, partial->oaicompat_msg_diffs);
return;
}
// Try final result
auto* final_res = dynamic_cast<server_task_result_cmpl_final*>(raw_result);
if (final_res && final_res->is_updated) {
populate_chat_deltas_from_diffs(reply, final_res->oaicompat_msg_diffs);
}
};
// Process first result
json first_res_json = first_result->to_json();
if (first_res_json.is_array()) {
for (const auto & res : first_res_json) {
auto reply = build_reply_from_json(res, first_result.get());
attach_chat_deltas(reply, first_result.get());
std::string completion_text = res.value("content", "");
backend::Reply reply;
reply.set_message(completion_text);
int32_t tokens_predicted = res.value("tokens_predicted", 0);
reply.set_tokens(tokens_predicted);
int32_t tokens_evaluated = res.value("tokens_evaluated", 0);
reply.set_prompt_tokens(tokens_evaluated);
if (res.contains("timings")) {
double timing_prompt_processing = res.at("timings").value("prompt_ms", 0.0);
reply.set_timing_prompt_processing(timing_prompt_processing);
double timing_token_generation = res.at("timings").value("predicted_ms", 0.0);
reply.set_timing_token_generation(timing_token_generation);
}
// Extract and set logprobs if present
json logprobs_json = extract_logprobs_from_json(res);
if (!logprobs_json.empty() && !logprobs_json.is_null()) {
std::string logprobs_str = logprobs_json.dump();
reply.set_logprobs(logprobs_str);
}
writer->Write(reply);
}
} else {
auto reply = build_reply_from_json(first_res_json, first_result.get());
attach_chat_deltas(reply, first_result.get());
std::string completion_text = first_res_json.value("content", "");
backend::Reply reply;
reply.set_message(completion_text);
int32_t tokens_predicted = first_res_json.value("tokens_predicted", 0);
reply.set_tokens(tokens_predicted);
int32_t tokens_evaluated = first_res_json.value("tokens_evaluated", 0);
reply.set_prompt_tokens(tokens_evaluated);
if (first_res_json.contains("timings")) {
double timing_prompt_processing = first_res_json.at("timings").value("prompt_ms", 0.0);
reply.set_timing_prompt_processing(timing_prompt_processing);
double timing_token_generation = first_res_json.at("timings").value("predicted_ms", 0.0);
reply.set_timing_token_generation(timing_token_generation);
}
// Extract and set logprobs if present
json logprobs_json = extract_logprobs_from_json(first_res_json);
if (!logprobs_json.empty() && !logprobs_json.is_null()) {
std::string logprobs_str = logprobs_json.dump();
reply.set_logprobs(logprobs_str);
}
writer->Write(reply);
}
// Process subsequent results
while (rd.has_next()) {
// Check if context is cancelled before processing result
if (context->IsCancelled()) {
break;
}
auto result = rd.next([&context]() { return context->IsCancelled(); });
if (result == nullptr) {
// connection is closed
break;
}
json res_json = result->to_json();
if (res_json.is_array()) {
for (const auto & res : res_json) {
auto reply = build_reply_from_json(res, result.get());
attach_chat_deltas(reply, result.get());
std::string completion_text = res.value("content", "");
backend::Reply reply;
reply.set_message(completion_text);
int32_t tokens_predicted = res.value("tokens_predicted", 0);
reply.set_tokens(tokens_predicted);
int32_t tokens_evaluated = res.value("tokens_evaluated", 0);
reply.set_prompt_tokens(tokens_evaluated);
if (res.contains("timings")) {
double timing_prompt_processing = res.at("timings").value("prompt_ms", 0.0);
reply.set_timing_prompt_processing(timing_prompt_processing);
double timing_token_generation = res.at("timings").value("predicted_ms", 0.0);
reply.set_timing_token_generation(timing_token_generation);
}
// Extract and set logprobs if present
json logprobs_json = extract_logprobs_from_json(res);
if (!logprobs_json.empty() && !logprobs_json.is_null()) {
std::string logprobs_str = logprobs_json.dump();
reply.set_logprobs(logprobs_str);
}
writer->Write(reply);
}
} else {
auto reply = build_reply_from_json(res_json, result.get());
attach_chat_deltas(reply, result.get());
std::string completion_text = res_json.value("content", "");
backend::Reply reply;
reply.set_message(completion_text);
int32_t tokens_predicted = res_json.value("tokens_predicted", 0);
reply.set_tokens(tokens_predicted);
int32_t tokens_evaluated = res_json.value("tokens_evaluated", 0);
reply.set_prompt_tokens(tokens_evaluated);
if (res_json.contains("timings")) {
double timing_prompt_processing = res_json.at("timings").value("prompt_ms", 0.0);
reply.set_timing_prompt_processing(timing_prompt_processing);
double timing_token_generation = res_json.at("timings").value("predicted_ms", 0.0);
reply.set_timing_token_generation(timing_token_generation);
}
// Extract and set logprobs if present
json logprobs_json = extract_logprobs_from_json(res_json);
if (!logprobs_json.empty() && !logprobs_json.is_null()) {
std::string logprobs_str = logprobs_json.dump();
reply.set_logprobs(logprobs_str);
}
writer->Write(reply);
}
}
@@ -2038,52 +1986,6 @@ public:
body_json["add_generation_prompt"] = data["add_generation_prompt"];
}
// Pass sampling parameters to body_json so oaicompat_chat_params_parse respects them
// and doesn't overwrite them with defaults in the returned parsed_data
if (data.contains("n_predict")) {
body_json["max_tokens"] = data["n_predict"];
}
if (data.contains("ignore_eos")) {
body_json["ignore_eos"] = data["ignore_eos"];
}
if (data.contains("stop")) {
body_json["stop"] = data["stop"];
}
if (data.contains("temperature")) {
body_json["temperature"] = data["temperature"];
}
if (data.contains("top_p")) {
body_json["top_p"] = data["top_p"];
}
if (data.contains("frequency_penalty")) {
body_json["frequency_penalty"] = data["frequency_penalty"];
}
if (data.contains("presence_penalty")) {
body_json["presence_penalty"] = data["presence_penalty"];
}
if (data.contains("seed")) {
body_json["seed"] = data["seed"];
}
if (data.contains("logit_bias")) {
body_json["logit_bias"] = data["logit_bias"];
}
if (data.contains("top_k")) {
body_json["top_k"] = data["top_k"];
}
if (data.contains("min_p")) {
body_json["min_p"] = data["min_p"];
}
// Pass enable_thinking via chat_template_kwargs (where oaicompat_chat_params_parse reads it)
const auto& predict_metadata = request->metadata();
auto predict_et_it = predict_metadata.find("enable_thinking");
if (predict_et_it != predict_metadata.end()) {
if (!body_json.contains("chat_template_kwargs")) {
body_json["chat_template_kwargs"] = json::object();
}
body_json["chat_template_kwargs"]["enable_thinking"] = (predict_et_it->second == "true");
}
// Debug: Print full body_json before template processing (includes messages, tools, tool_choice, etc.)
SRV_DBG("[CONVERSATION DEBUG] Predict: Full body_json before oaicompat_chat_params_parse:\n%s\n", body_json.dump(2).c_str());
@@ -2270,8 +2172,7 @@ public:
std::cout << "[DEBUG] Received " << all_results.results.size() << " results" << std::endl;
if (all_results.results.size() == 1) {
// single result
auto* final_res = dynamic_cast<server_task_result_cmpl_final*>(all_results.results[0].get());
GGML_ASSERT(final_res != nullptr);
GGML_ASSERT(dynamic_cast<server_task_result_cmpl_final*>(all_results.results[0].get()) != nullptr);
json result_json = all_results.results[0]->to_json();
reply->set_message(result_json.value("content", ""));
@@ -2294,11 +2195,6 @@ public:
reply->set_logprobs(logprobs_str);
}
// Populate chat deltas from the autoparser's final parsed message
if (final_res->is_updated) {
populate_chat_deltas_from_final(*reply, final_res->oaicompat_msg);
}
} else {
// multiple results (multitask)
json arr = json::array();
@@ -2621,113 +2517,6 @@ public:
response->set_rendered_template(rendered_template);
// Run differential template analysis to detect tool format markers
if (params_base.use_jinja) {
try {
// Get template source and reconstruct a common_chat_template for analysis
std::string tmpl_src = common_chat_templates_source(ctx_server.impl->chat_params.tmpls.get());
if (!tmpl_src.empty()) {
const auto * vocab = llama_model_get_vocab(ctx_server.impl->model);
std::string token_bos, token_eos;
if (vocab) {
auto bos_id = llama_vocab_bos(vocab);
auto eos_id = llama_vocab_eos(vocab);
if (bos_id != LLAMA_TOKEN_NULL) {
token_bos = common_token_to_piece(vocab, bos_id, true);
}
if (eos_id != LLAMA_TOKEN_NULL) {
token_eos = common_token_to_piece(vocab, eos_id, true);
}
}
common_chat_template tmpl(tmpl_src, token_bos, token_eos);
struct autoparser::autoparser ap;
ap.analyze_template(tmpl);
if (ap.analysis_complete && ap.tools.format.mode != autoparser::tool_format::NONE) {
auto * tf = response->mutable_tool_format();
// Format type
switch (ap.tools.format.mode) {
case autoparser::tool_format::JSON_NATIVE:
tf->set_format_type("json_native");
break;
case autoparser::tool_format::TAG_WITH_JSON:
tf->set_format_type("tag_with_json");
break;
case autoparser::tool_format::TAG_WITH_TAGGED:
tf->set_format_type("tag_with_tagged");
break;
default:
break;
}
// Tool section markers
tf->set_section_start(ap.tools.format.section_start);
tf->set_section_end(ap.tools.format.section_end);
tf->set_per_call_start(ap.tools.format.per_call_start);
tf->set_per_call_end(ap.tools.format.per_call_end);
// Function markers
tf->set_func_name_prefix(ap.tools.function.name_prefix);
tf->set_func_name_suffix(ap.tools.function.name_suffix);
tf->set_func_close(ap.tools.function.close);
// Argument markers
tf->set_arg_name_prefix(ap.tools.arguments.name_prefix);
tf->set_arg_name_suffix(ap.tools.arguments.name_suffix);
tf->set_arg_value_prefix(ap.tools.arguments.value_prefix);
tf->set_arg_value_suffix(ap.tools.arguments.value_suffix);
tf->set_arg_separator(ap.tools.arguments.separator);
tf->set_args_start(ap.tools.arguments.start);
tf->set_args_end(ap.tools.arguments.end);
// JSON format fields
tf->set_name_field(ap.tools.format.name_field);
tf->set_args_field(ap.tools.format.args_field);
tf->set_id_field(ap.tools.format.id_field);
tf->set_fun_name_is_key(ap.tools.format.fun_name_is_key);
tf->set_tools_array_wrapped(ap.tools.format.tools_array_wrapped);
tf->set_uses_python_dicts(ap.tools.format.uses_python_dicts);
tf->set_function_field(ap.tools.format.function_field);
tf->set_gen_id_field(ap.tools.format.gen_id_field);
for (const auto & p : ap.tools.format.parameter_order) {
tf->add_parameter_order(p);
}
// Call ID markers
switch (ap.tools.call_id.pos) {
case autoparser::call_id_position::NONE:
tf->set_call_id_position("none");
break;
case autoparser::call_id_position::PRE_FUNC_NAME:
tf->set_call_id_position("pre_func_name");
break;
case autoparser::call_id_position::BETWEEN_FUNC_AND_ARGS:
tf->set_call_id_position("between_func_and_args");
break;
case autoparser::call_id_position::POST_ARGS:
tf->set_call_id_position("post_args");
break;
}
tf->set_call_id_prefix(ap.tools.call_id.prefix);
tf->set_call_id_suffix(ap.tools.call_id.suffix);
// Reasoning markers
tf->set_reasoning_start(ap.reasoning.start);
tf->set_reasoning_end(ap.reasoning.end);
// Content markers
tf->set_content_start(ap.content.start);
tf->set_content_end(ap.content.end);
}
}
} catch (const std::exception & e) {
SRV_WRN("ModelMetadata: failed to run autoparser analysis: %s\n", e.what());
}
}
return grpc::Status::OK;
}
};

View File

@@ -1,54 +0,0 @@
cmake_minimum_required(VERSION 3.14)
project(goacestepcpp LANGUAGES C CXX)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(ACESTEP_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sources/acestep.cpp)
# Override upstream's CMAKE_CUDA_ARCHITECTURES before add_subdirectory.
# Upstream sets 120a/121a for CUDA >= 12.8, but those archs require a newer
# toolkit than 12.8.x ships. Pre-defining this variable makes the upstream
# "if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)" guard skip its broken defaults.
if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
set(CMAKE_CUDA_ARCHITECTURES "75-virtual;80-virtual;86-real;89-real")
endif()
# EXCLUDE_FROM_ALL: only build targets we explicitly depend on (acestep-core, ggml),
# skip upstream standalone executables (ace-understand, dit-vae, etc.)
add_subdirectory(${ACESTEP_DIR} acestep EXCLUDE_FROM_ALL)
add_library(goacestepcpp MODULE cpp/goacestepcpp.cpp)
target_link_libraries(goacestepcpp PRIVATE acestep-core ggml ggml-base ggml-cpu)
# Include dirs matching link_ggml_backends macro, but with absolute paths
target_include_directories(goacestepcpp PRIVATE ${ACESTEP_DIR}/src ${ACESTEP_DIR})
target_include_directories(goacestepcpp SYSTEM PRIVATE ${ACESTEP_DIR}/ggml/include)
# Link GPU backends if available (mirrors link_ggml_backends macro)
foreach(backend blas cuda metal vulkan)
if(TARGET ggml-${backend})
target_link_libraries(goacestepcpp PRIVATE ggml-${backend})
string(TOUPPER ${backend} BACKEND_UPPER)
target_compile_definitions(goacestepcpp PRIVATE ACESTEP_HAVE_${BACKEND_UPPER})
if(backend STREQUAL "cuda")
find_package(CUDAToolkit QUIET)
if(CUDAToolkit_FOUND)
target_link_libraries(goacestepcpp PRIVATE CUDA::cudart)
endif()
endif()
endif()
endforeach()
if(MSVC)
target_compile_options(goacestepcpp PRIVATE /W4 /wd4100 /wd4505)
else()
target_compile_options(goacestepcpp PRIVATE -Wall -Wextra -Wshadow -Wconversion
-Wno-unused-parameter -Wno-unused-function -Wno-sign-conversion)
endif()
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)
target_link_libraries(goacestepcpp PRIVATE stdc++fs)
endif()
set_property(TARGET goacestepcpp PROPERTY CXX_STANDARD 17)
set_target_properties(goacestepcpp PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})

View File

@@ -1,127 +0,0 @@
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc --ignore=1)
# acestep.cpp version
ACESTEP_REPO?=https://github.com/ace-step/acestep.cpp
ACESTEP_CPP_VERSION?=5aa065445541094cba934299cd498bbb9fa5c434
SO_TARGET?=libgoacestepcpp.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DGGML_CUDA=ON
else ifeq ($(BUILD_TYPE),openblas)
CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
else ifeq ($(BUILD_TYPE),hipblas)
CMAKE_ARGS+=-DGGML_HIPBLAS=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON
else ifeq ($(OS),Darwin)
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DGGML_METAL=OFF
else
CMAKE_ARGS+=-DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
endif
endif
ifeq ($(BUILD_TYPE),sycl_f16)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DGGML_SYCL_F16=ON
endif
ifeq ($(BUILD_TYPE),sycl_f32)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx
endif
sources/acestep.cpp:
mkdir -p sources/acestep.cpp
cd sources/acestep.cpp && \
git init && \
git remote add origin $(ACESTEP_REPO) && \
git fetch origin && \
git checkout $(ACESTEP_CPP_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
# Detect OS
UNAME_S := $(shell uname -s)
# Only build CPU variants on Linux
ifeq ($(UNAME_S),Linux)
VARIANT_TARGETS = libgoacestepcpp-avx.so libgoacestepcpp-avx2.so libgoacestepcpp-avx512.so libgoacestepcpp-fallback.so
else
# On non-Linux (e.g., Darwin), build only fallback variant
VARIANT_TARGETS = libgoacestepcpp-fallback.so
endif
acestep-cpp: main.go goacestepcpp.go $(VARIANT_TARGETS)
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o acestep-cpp ./
package: acestep-cpp
bash package.sh
build: package
clean: purge
rm -rf libgoacestepcpp*.so package sources/acestep.cpp acestep-cpp
purge:
rm -rf build*
# Variants must build sequentially: each uses its own build-<name> directory,
# but parallel builds can still race on shared resources (jobserver, disk I/O).
.NOTPARALLEL:
# Build all variants (Linux only)
ifeq ($(UNAME_S),Linux)
libgoacestepcpp-avx.so: sources/acestep.cpp
$(info ${GREEN}I acestep-cpp build info:avx${RESET})
SO_TARGET=libgoacestepcpp-avx.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgoacestepcpp-custom
rm -rf build-libgoacestepcpp-avx.so
libgoacestepcpp-avx2.so: sources/acestep.cpp
$(info ${GREEN}I acestep-cpp build info:avx2${RESET})
SO_TARGET=libgoacestepcpp-avx2.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) libgoacestepcpp-custom
rm -rf build-libgoacestepcpp-avx2.so
libgoacestepcpp-avx512.so: sources/acestep.cpp
$(info ${GREEN}I acestep-cpp build info:avx512${RESET})
SO_TARGET=libgoacestepcpp-avx512.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) libgoacestepcpp-custom
rm -rf build-libgoacestepcpp-avx512.so
endif
# Build fallback variant (all platforms)
libgoacestepcpp-fallback.so: sources/acestep.cpp
$(info ${GREEN}I acestep-cpp build info:fallback${RESET})
SO_TARGET=libgoacestepcpp-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgoacestepcpp-custom
rm -rf build-libgoacestepcpp-fallback.so
libgoacestepcpp-custom: CMakeLists.txt cpp/goacestepcpp.cpp cpp/goacestepcpp.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) --target goacestepcpp && \
cd .. && \
mv build-$(SO_TARGET)/libgoacestepcpp.so ./$(SO_TARGET)
test: acestep-cpp
@echo "Running acestep-cpp tests..."
bash test.sh
@echo "acestep-cpp tests completed."
all: acestep-cpp package

View File

@@ -1,202 +0,0 @@
package main
import (
"context"
"os"
"os/exec"
"path/filepath"
"testing"
"time"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"google.golang.org/grpc"
"google.golang.org/grpc/credentials/insecure"
)
const (
testAddr = "localhost:50051"
startupWait = 5 * time.Second
)
func skipIfNoModel(t *testing.T) string {
t.Helper()
modelDir := os.Getenv("ACESTEP_MODEL_DIR")
if modelDir == "" {
t.Skip("ACESTEP_MODEL_DIR not set, skipping test (set to directory with GGUF models)")
}
if _, err := os.Stat(filepath.Join(modelDir, "acestep-5Hz-lm-0.6B-Q8_0.gguf")); os.IsNotExist(err) {
t.Skipf("LM model file not found in %s, skipping", modelDir)
}
if _, err := os.Stat(filepath.Join(modelDir, "Qwen3-Embedding-0.6B-Q8_0.gguf")); os.IsNotExist(err) {
t.Skipf("Text encoder model file not found in %s, skipping", modelDir)
}
if _, err := os.Stat(filepath.Join(modelDir, "acestep-v15-turbo-Q8_0.gguf")); os.IsNotExist(err) {
t.Skipf("DiT model file not found in %s, skipping", modelDir)
}
if _, err := os.Stat(filepath.Join(modelDir, "vae-BF16.gguf")); os.IsNotExist(err) {
t.Skipf("VAE model file not found in %s, skipping", modelDir)
}
return modelDir
}
func startServer(t *testing.T) *exec.Cmd {
t.Helper()
binary := os.Getenv("ACESTEP_BINARY")
if binary == "" {
binary = "./acestep-cpp"
}
if _, err := os.Stat(binary); os.IsNotExist(err) {
t.Skipf("Backend binary not found at %s, skipping", binary)
}
cmd := exec.Command(binary, "--addr", testAddr)
cmd.Stdout = os.Stderr
cmd.Stderr = os.Stderr
if err := cmd.Start(); err != nil {
t.Fatalf("Failed to start server: %v", err)
}
time.Sleep(startupWait)
return cmd
}
func stopServer(cmd *exec.Cmd) {
if cmd != nil && cmd.Process != nil {
cmd.Process.Kill()
cmd.Wait()
}
}
func dialGRPC(t *testing.T) *grpc.ClientConn {
t.Helper()
conn, err := grpc.Dial(testAddr,
grpc.WithTransportCredentials(insecure.NewCredentials()),
grpc.WithDefaultCallOptions(
grpc.MaxCallRecvMsgSize(50*1024*1024),
grpc.MaxCallSendMsgSize(50*1024*1024),
),
)
if err != nil {
t.Fatalf("Failed to dial gRPC: %v", err)
}
return conn
}
func TestServerHealth(t *testing.T) {
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
resp, err := client.Health(context.Background(), &pb.HealthMessage{})
if err != nil {
t.Fatalf("Health check failed: %v", err)
}
if string(resp.Message) != "OK" {
t.Fatalf("Expected OK, got %s", string(resp.Message))
}
}
func TestLoadModel(t *testing.T) {
modelDir := skipIfNoModel(t)
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
// Get base directory from main model file for relative paths
mainModelPath := filepath.Join(modelDir, "acestep-5Hz-lm-0.6B-Q8_0.gguf")
resp, err := client.LoadModel(context.Background(), &pb.ModelOptions{
ModelFile: mainModelPath,
Options: []string{
"text_encoder_model:Qwen3-Embedding-0.6B-Q8_0.gguf",
"dit_model:acestep-v15-turbo-Q8_0.gguf",
"vae_model:vae-BF16.gguf",
},
})
if err != nil {
t.Fatalf("LoadModel failed: %v", err)
}
if !resp.Success {
t.Fatalf("LoadModel returned failure: %s", resp.Message)
}
}
func TestSoundGeneration(t *testing.T) {
modelDir := skipIfNoModel(t)
tmpDir, err := os.MkdirTemp("", "acestep-test")
if err != nil {
t.Fatal(err)
}
defer os.RemoveAll(tmpDir)
outputFile := filepath.Join(tmpDir, "output.wav")
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
// Get base directory from main model file for relative paths
mainModelPath := filepath.Join(modelDir, "acestep-5Hz-lm-0.6B-Q8_0.gguf")
// Load models
loadResp, err := client.LoadModel(context.Background(), &pb.ModelOptions{
ModelFile: mainModelPath,
Options: []string{
"text_encoder_model:Qwen3-Embedding-0.6B-Q8_0.gguf",
"dit_model:acestep-v15-turbo-Q8_0.gguf",
"vae_model:vae-BF16.gguf",
},
})
if err != nil {
t.Fatalf("LoadModel failed: %v", err)
}
if !loadResp.Success {
t.Fatalf("LoadModel returned failure: %s", loadResp.Message)
}
// Generate music
duration := float32(10.0)
temperature := float32(0.85)
bpm := int32(120)
caption := "A cheerful electronic dance track"
timesig := "4/4"
_, err = client.SoundGeneration(context.Background(), &pb.SoundGenerationRequest{
Text: caption,
Caption: &caption,
Dst: outputFile,
Duration: &duration,
Temperature: &temperature,
Bpm: &bpm,
Timesignature: &timesig,
})
if err != nil {
t.Fatalf("SoundGeneration failed: %v", err)
}
// Verify output file exists and has content
info, err := os.Stat(outputFile)
if os.IsNotExist(err) {
t.Fatal("Output audio file was not created")
}
if err != nil {
t.Fatalf("Failed to stat output file: %v", err)
}
t.Logf("Output file size: %d bytes", info.Size())
// WAV header is 44 bytes minimum; any real audio should be much larger
if info.Size() < 1000 {
t.Errorf("Output file too small (%d bytes), expected real audio data", info.Size())
}
}

View File

@@ -1,306 +0,0 @@
#include "goacestepcpp.h"
#include "ggml-backend.h"
#include "audio-io.h"
#include "bpe.h"
#include "cond-enc.h"
#include "dit-sampler.h"
#include "dit.h"
#include "gguf-weights.h"
#include "philox.h"
#include "qwen3-enc.h"
#include "qwen3-lm.h"
#include "request.h"
#include "vae.h"
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <random>
#include <string>
#include <vector>
// Global model contexts (loaded once, reused across requests)
static DiTGGML g_dit = {};
static DiTGGMLConfig g_dit_cfg;
static VAEGGML g_vae = {};
static bool g_dit_loaded = false;
static bool g_vae_loaded = false;
static bool g_is_turbo = false;
// Silence latent [15000, 64] — read once from DiT GGUF
static std::vector<float> g_silence_full;
// Paths for per-request loading (text encoder, tokenizer)
static std::string g_text_enc_path;
static std::string g_dit_path;
static std::string g_lm_path;
static void ggml_log_cb(enum ggml_log_level level, const char * log, void * data) {
const char * level_str;
if (!log)
return;
switch (level) {
case GGML_LOG_LEVEL_DEBUG:
level_str = "DEBUG";
break;
case GGML_LOG_LEVEL_INFO:
level_str = "INFO";
break;
case GGML_LOG_LEVEL_WARN:
level_str = "WARN";
break;
case GGML_LOG_LEVEL_ERROR:
level_str = "ERROR";
break;
default:
level_str = "?????";
break;
}
fprintf(stderr, "[%-5s] ", level_str);
fputs(log, stderr);
fflush(stderr);
}
int load_model(const char * lm_model_path, const char * text_encoder_path,
const char * dit_model_path, const char * vae_model_path) {
ggml_log_set(ggml_log_cb, nullptr);
ggml_backend_load_all();
g_lm_path = lm_model_path;
g_text_enc_path = text_encoder_path;
g_dit_path = dit_model_path;
// Load DiT model
fprintf(stderr, "[acestep-cpp] Loading DiT from %s\n", dit_model_path);
dit_ggml_init_backend(&g_dit);
if (!dit_ggml_load(&g_dit, dit_model_path, g_dit_cfg, nullptr, 0.0f)) {
fprintf(stderr, "[acestep-cpp] FATAL: failed to load DiT from %s\n", dit_model_path);
return 1;
}
g_dit_loaded = true;
// Read DiT GGUF metadata + silence_latent
{
GGUFModel gf = {};
if (gf_load(&gf, dit_model_path)) {
g_is_turbo = gf_get_bool(gf, "acestep.is_turbo");
const void * sl_data = gf_get_data(gf, "silence_latent");
if (sl_data) {
g_silence_full.resize(15000 * 64);
memcpy(g_silence_full.data(), sl_data, 15000 * 64 * sizeof(float));
fprintf(stderr, "[acestep-cpp] silence_latent: [15000, 64] loaded\n");
} else {
fprintf(stderr, "[acestep-cpp] FATAL: silence_latent not found in %s\n", dit_model_path);
gf_close(&gf);
return 2;
}
gf_close(&gf);
} else {
fprintf(stderr, "[acestep-cpp] FATAL: cannot read GGUF metadata from %s\n", dit_model_path);
return 2;
}
}
// Load VAE model
fprintf(stderr, "[acestep-cpp] Loading VAE from %s\n", vae_model_path);
vae_ggml_load(&g_vae, vae_model_path);
g_vae_loaded = true;
fprintf(stderr, "[acestep-cpp] All models loaded successfully (turbo=%d)\n", g_is_turbo);
return 0;
}
int generate_music(const char * caption, const char * lyrics, int bpm,
const char * keyscale, const char * timesignature,
float duration, float temperature, bool instrumental,
int seed, const char * dst, int threads) {
if (!g_dit_loaded || !g_vae_loaded) {
fprintf(stderr, "[acestep-cpp] ERROR: models not loaded\n");
return 1;
}
const int FRAMES_PER_SECOND = 25;
// Defaults
if (duration <= 0)
duration = 30.0f;
std::string cap_str = caption ? caption : "";
std::string lyrics_str = (instrumental || !lyrics) ? "" : lyrics;
std::string ks_str = keyscale ? keyscale : "N/A";
std::string ts_str = timesignature ? timesignature : "4/4";
std::string lang_str = "unknown";
char bpm_str[16];
if (bpm > 0) {
snprintf(bpm_str, sizeof(bpm_str), "%d", bpm);
} else {
snprintf(bpm_str, sizeof(bpm_str), "N/A");
}
int num_steps = 8;
float guidance_scale = g_is_turbo ? 1.0f : 7.0f;
float shift = 1.0f;
if (seed < 0) {
std::random_device rd;
seed = (int)(rd() & 0x7FFFFFFF);
}
// Compute T (latent frames at 25Hz)
int T = (int)(duration * FRAMES_PER_SECOND);
T = ((T + g_dit_cfg.patch_size - 1) / g_dit_cfg.patch_size) * g_dit_cfg.patch_size;
int S = T / g_dit_cfg.patch_size;
if (T > 15000) {
fprintf(stderr, "[acestep-cpp] ERROR: T=%d exceeds max 15000\n", T);
return 2;
}
int Oc = g_dit_cfg.out_channels; // 64
int ctx_ch = g_dit_cfg.in_channels - Oc; // 128
fprintf(stderr, "[acestep-cpp] T=%d, S=%d, duration=%.1fs, seed=%d\n", T, S, duration, seed);
// 1. Load BPE tokenizer from text encoder GGUF
BPETokenizer tok;
if (!load_bpe_from_gguf(&tok, g_text_enc_path.c_str())) {
fprintf(stderr, "[acestep-cpp] FATAL: failed to load BPE tokenizer\n");
return 3;
}
// 2. Build formatted prompts (matches dit-vae.cpp text2music template)
std::string instruction = "Fill the audio semantic mask based on the given conditions:";
char metas[512];
snprintf(metas, sizeof(metas),
"- bpm: %s\n- timesignature: %s\n- keyscale: %s\n- duration: %d seconds\n",
bpm_str, ts_str.c_str(), ks_str.c_str(), (int)duration);
std::string text_str = std::string("# Instruction\n") + instruction + "\n\n" +
"# Caption\n" + cap_str + "\n\n" +
"# Metas\n" + metas + "<|endoftext|>\n";
std::string lyric_str = std::string("# Languages\n") + lang_str + "\n\n# Lyric\n" +
lyrics_str + "<|endoftext|>";
// 3. Tokenize
auto text_ids = bpe_encode(&tok, text_str.c_str(), true);
auto lyric_ids = bpe_encode(&tok, lyric_str.c_str(), true);
int S_text = (int)text_ids.size();
int S_lyric = (int)lyric_ids.size();
fprintf(stderr, "[acestep-cpp] caption: %d tokens, lyrics: %d tokens\n", S_text, S_lyric);
// 4. Text encoder forward
Qwen3GGML text_enc = {};
qwen3_init_backend(&text_enc);
if (!qwen3_load_text_encoder(&text_enc, g_text_enc_path.c_str())) {
fprintf(stderr, "[acestep-cpp] FATAL: failed to load text encoder\n");
return 4;
}
int H_text = text_enc.cfg.hidden_size; // 1024
std::vector<float> text_hidden(H_text * S_text);
qwen3_forward(&text_enc, text_ids.data(), S_text, text_hidden.data());
fprintf(stderr, "[acestep-cpp] TextEncoder forward done\n");
// 5. Lyric embedding
std::vector<float> lyric_embed(H_text * S_lyric);
qwen3_embed_lookup(&text_enc, lyric_ids.data(), S_lyric, lyric_embed.data());
// 6. Condition encoder
CondGGML cond = {};
cond_ggml_init_backend(&cond);
if (!cond_ggml_load(&cond, g_dit_path.c_str())) {
fprintf(stderr, "[acestep-cpp] FATAL: failed to load condition encoder\n");
qwen3_free(&text_enc);
return 5;
}
const int S_ref = 750;
std::vector<float> silence_feats(S_ref * 64);
memcpy(silence_feats.data(), g_silence_full.data(), S_ref * 64 * sizeof(float));
int enc_S = 0;
std::vector<float> enc_hidden;
cond_ggml_forward(&cond, text_hidden.data(), S_text, lyric_embed.data(), S_lyric,
silence_feats.data(), S_ref, enc_hidden, &enc_S);
fprintf(stderr, "[acestep-cpp] ConditionEncoder done, enc_S=%d\n", enc_S);
qwen3_free(&text_enc);
cond_ggml_free(&cond);
// 7. Build context [T, ctx_ch] = silence[64] + mask[64]
std::vector<float> context(T * ctx_ch);
for (int t = 0; t < T; t++) {
const float * src = g_silence_full.data() + t * Oc;
for (int c = 0; c < Oc; c++) {
context[t * ctx_ch + c] = src[c];
}
for (int c = 0; c < Oc; c++) {
context[t * ctx_ch + Oc + c] = 1.0f;
}
}
// 8. Build schedule
std::vector<float> schedule(num_steps);
for (int i = 0; i < num_steps; i++) {
float t = 1.0f - (float)i / (float)num_steps;
schedule[i] = shift * t / (1.0f + (shift - 1.0f) * t);
}
// 9. Generate noise (Philox)
std::vector<float> noise(Oc * T);
philox_randn((long long)seed, noise.data(), Oc * T, true);
// 10. DiT generate
std::vector<float> output(Oc * T);
fprintf(stderr, "[acestep-cpp] DiT generate: T=%d, steps=%d, guidance=%.1f\n", T, num_steps, guidance_scale);
dit_ggml_generate(&g_dit, noise.data(), context.data(), enc_hidden.data(), enc_S,
T, 1, num_steps, schedule.data(), output.data(), guidance_scale,
nullptr, nullptr, -1);
fprintf(stderr, "[acestep-cpp] DiT generation done\n");
// 11. VAE decode
int T_audio_max = T * 1920;
std::vector<float> audio(2 * T_audio_max);
int T_audio = vae_ggml_decode_tiled(&g_vae, output.data(), T, audio.data(), T_audio_max, 256, 64);
if (T_audio < 0) {
fprintf(stderr, "[acestep-cpp] ERROR: VAE decode failed\n");
return 6;
}
fprintf(stderr, "[acestep-cpp] VAE decode done: %d samples (%.2fs @ 48kHz)\n", T_audio,
(float)T_audio / 48000.0f);
// 12. Peak normalization to -1.0 dB
{
float peak = 0.0f;
int n_samples = 2 * T_audio;
for (int i = 0; i < n_samples; i++) {
float a = audio[i] < 0 ? -audio[i] : audio[i];
if (a > peak) {
peak = a;
}
}
if (peak > 1e-6f) {
const float target_amp = powf(10.0f, -1.0f / 20.0f);
float gain = target_amp / peak;
for (int i = 0; i < n_samples; i++) {
audio[i] *= gain;
}
}
}
// 13. Write WAV output
if (!audio_write_wav(dst, audio.data(), T_audio, 48000)) {
fprintf(stderr, "[acestep-cpp] ERROR: failed to write %s\n", dst);
return 7;
}
fprintf(stderr, "[acestep-cpp] Wrote %s: %d samples (%.2fs @ 48kHz stereo)\n",
dst, T_audio, (float)T_audio / 48000.0f);
return 0;
}

View File

@@ -1,11 +0,0 @@
#include <cstddef>
#include <cstdint>
extern "C" {
int load_model(const char *lm_model_path, const char *text_encoder_path,
const char *dit_model_path, const char *vae_model_path);
int generate_music(const char *caption, const char *lyrics, int bpm,
const char *keyscale, const char *timesignature,
float duration, float temperature, bool instrumental,
int seed, const char *dst, int threads);
}

View File

@@ -1,109 +0,0 @@
package main
import (
"fmt"
"os"
"path/filepath"
"strings"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
var (
CppLoadModel func(lmModelPath, textEncoderPath, ditModelPath, vaeModelPath string) int
CppGenerateMusic func(caption, lyrics string, bpm int, keyscale, timesignature string, duration, temperature float32, instrumental bool, seed int, dst string, threads int) int
)
type AceStepCpp struct {
base.SingleThread
}
func (a *AceStepCpp) Load(opts *pb.ModelOptions) error {
// ModelFile is the LM model path
lmModel := opts.ModelFile
// Get the base directory from ModelFile for resolving relative paths
baseDir := filepath.Dir(lmModel)
var textEncoderModel, ditModel, vaeModel string
for _, oo := range opts.Options {
parts := strings.SplitN(oo, ":", 2)
if len(parts) != 2 {
fmt.Fprintf(os.Stderr, "Unrecognized option: %v\n", oo)
continue
}
switch parts[0] {
case "text_encoder_model":
textEncoderModel = parts[1]
case "dit_model":
ditModel = parts[1]
case "vae_model":
vaeModel = parts[1]
default:
fmt.Fprintf(os.Stderr, "Unrecognized option: %v\n", oo)
}
}
if textEncoderModel == "" {
return fmt.Errorf("text_encoder_model option is required")
}
if ditModel == "" {
return fmt.Errorf("dit_model option is required")
}
if vaeModel == "" {
return fmt.Errorf("vae_model option is required")
}
// Resolve relative paths to the base directory
// If the path doesn't start with "/" it's relative
if !filepath.IsAbs(textEncoderModel) {
textEncoderModel = filepath.Join(baseDir, textEncoderModel)
}
if !filepath.IsAbs(ditModel) {
ditModel = filepath.Join(baseDir, ditModel)
}
if !filepath.IsAbs(vaeModel) {
vaeModel = filepath.Join(baseDir, vaeModel)
}
// Also resolve the lmModel if it's relative
if !filepath.IsAbs(lmModel) {
lmModel = filepath.Join(baseDir, lmModel)
}
fmt.Fprintf(os.Stderr, "[acestep-cpp] Resolved paths:\n")
fmt.Fprintf(os.Stderr, " LM Model: %s\n", lmModel)
fmt.Fprintf(os.Stderr, " Text Encoder: %s\n", textEncoderModel)
fmt.Fprintf(os.Stderr, " DiT Model: %s\n", ditModel)
fmt.Fprintf(os.Stderr, " VAE Model: %s\n", vaeModel)
if ret := CppLoadModel(lmModel, textEncoderModel, ditModel, vaeModel); ret != 0 {
return fmt.Errorf("failed to load acestep models (error code: %d)", ret)
}
return nil
}
func (a *AceStepCpp) SoundGeneration(req *pb.SoundGenerationRequest) error {
caption := req.GetCaption()
if caption == "" {
caption = req.GetText()
}
lyrics := req.GetLyrics()
bpm := int(req.GetBpm())
keyscale := req.GetKeyscale()
timesignature := req.GetTimesignature()
duration := req.GetDuration()
temperature := req.GetTemperature()
instrumental := req.GetInstrumental()
seed := 42
threads := 4
if ret := CppGenerateMusic(caption, lyrics, bpm, keyscale, timesignature, duration, temperature, instrumental, seed, req.GetDst(), threads); ret != 0 {
return fmt.Errorf("failed to generate music (error code: %d)", ret)
}
return nil
}

View File

@@ -1,47 +0,0 @@
package main
// Note: this is started internally by LocalAI and a server is allocated for each model
import (
"flag"
"os"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
type LibFuncs struct {
FuncPtr any
Name string
}
func main() {
// Get library name from environment variable, default to fallback
libName := os.Getenv("ACESTEP_LIBRARY")
if libName == "" {
libName = "./libgoacestepcpp-fallback.so"
}
gosd, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(err)
}
libFuncs := []LibFuncs{
{&CppLoadModel, "load_model"},
{&CppGenerateMusic, "generate_music"},
}
for _, lf := range libFuncs {
purego.RegisterLibFunc(lf.FuncPtr, gosd, lf.Name)
}
flag.Parse()
if err := grpc.StartServer(*addr, &AceStepCpp{}); err != nil {
panic(err)
}
}

View File

@@ -1,65 +0,0 @@
#!/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 $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/acestep-cpp $CURDIR/package/
cp -fv $CURDIR/libgoacestepcpp-*.so $CURDIR/package/
cp -fv $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
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/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
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
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/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ $(uname -s) = "Darwin" ]; then
echo "Detected Darwin"
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${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

@@ -1,52 +0,0 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath $0)")
cd /
echo "CPU info:"
if [ "$(uname)" != "Darwin" ]; then
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
fi
LIBRARY="$CURDIR/libgoacestepcpp-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/libgoacestepcpp-avx.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/libgoacestepcpp-avx2.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx2.so"
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/libgoacestepcpp-avx512.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx512.so"
fi
fi
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export ACESTEP_LIBRARY=$LIBRARY
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec $CURDIR/lib/ld.so $CURDIR/acestep-cpp "$@"
fi
echo "Using library: $LIBRARY"
exec $CURDIR/acestep-cpp "$@"

View File

@@ -1,54 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
echo "Running acestep-cpp backend tests..."
# The test requires:
# - ACESTEP_MODEL_DIR: path to directory containing GGUF model files
# - ACESTEP_BINARY: path to the acestep-cpp binary (defaults to ./acestep-cpp)
#
# Tests that require the model will be skipped if ACESTEP_MODEL_DIR is not set
# or the directory does not contain the required model files.
cd "$CURDIR"
# Only auto-download models when ACESTEP_MODEL_DIR is not explicitly set
if [ -z "$ACESTEP_MODEL_DIR" ]; then
export ACESTEP_MODEL_DIR="./acestep-models"
if [ ! -d "$ACESTEP_MODEL_DIR" ]; then
echo "Creating acestep-models directory for tests..."
mkdir -p "$ACESTEP_MODEL_DIR"
REPO_ID="Serveurperso/ACE-Step-1.5-GGUF"
echo "Repository: ${REPO_ID}"
echo ""
# Files to download (smallest quantizations for testing)
FILES=(
"acestep-5Hz-lm-0.6B-Q8_0.gguf"
"Qwen3-Embedding-0.6B-Q8_0.gguf"
"acestep-v15-turbo-Q8_0.gguf"
"vae-BF16.gguf"
)
BASE_URL="https://huggingface.co/${REPO_ID}/resolve/main"
for file in "${FILES[@]}"; do
dest="${ACESTEP_MODEL_DIR}/${file}"
if [ -f "${dest}" ]; then
echo " [skip] ${file} (already exists)"
else
echo " [download] ${file}..."
curl -L -o "${dest}" "${BASE_URL}/${file}" --progress-bar
echo " [done] ${file}"
fi
done
fi
fi
# Run Go tests
go test -v -timeout 600s .
echo "All acestep-cpp tests passed."

View File

@@ -0,0 +1,9 @@
GOCMD=go
huggingface:
CGO_ENABLED=0 $(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o huggingface ./
package:
bash package.sh
build: huggingface package

View File

@@ -0,0 +1,64 @@
package main
// This is a wrapper to statisfy the GRPC service interface
// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
import (
"fmt"
"os"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/langchain"
)
type LLM struct {
base.Base
langchain *langchain.HuggingFace
model string
}
func (llm *LLM) Load(opts *pb.ModelOptions) error {
var err error
hfToken := os.Getenv("HUGGINGFACEHUB_API_TOKEN")
if hfToken == "" {
return fmt.Errorf("no huggingface token provided")
}
llm.langchain, err = langchain.NewHuggingFace(opts.Model, hfToken)
llm.model = opts.Model
return err
}
func (llm *LLM) Predict(opts *pb.PredictOptions) (string, error) {
o := []langchain.PredictOption{
langchain.SetModel(llm.model),
langchain.SetMaxTokens(int(opts.Tokens)),
langchain.SetTemperature(float64(opts.Temperature)),
langchain.SetStopWords(opts.StopPrompts),
}
pred, err := llm.langchain.PredictHuggingFace(opts.Prompt, o...)
if err != nil {
return "", err
}
return pred.Completion, nil
}
func (llm *LLM) PredictStream(opts *pb.PredictOptions, results chan string) error {
o := []langchain.PredictOption{
langchain.SetModel(llm.model),
langchain.SetMaxTokens(int(opts.Tokens)),
langchain.SetTemperature(float64(opts.Temperature)),
langchain.SetStopWords(opts.StopPrompts),
}
go func() {
res, err := llm.langchain.PredictHuggingFace(opts.Prompt, o...)
if err != nil {
fmt.Println("err: ", err)
}
results <- res.Completion
close(results)
}()
return nil
}

View File

@@ -0,0 +1,21 @@
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, &LLM{}); err != nil {
panic(err)
}
}

View File

@@ -0,0 +1,12 @@
#!/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)")
mkdir -p $CURDIR/package
cp -avrf $CURDIR/huggingface $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/

6
backend/go/huggingface/run.sh Executable file
View File

@@ -0,0 +1,6 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath $0)")
exec $CURDIR/huggingface "$@"

View File

@@ -18,20 +18,6 @@ type LLM struct {
draftModel *llama.LLama
}
// Free releases GPU resources and frees the llama model
// This should be called when the model is being unloaded to properly release VRAM
func (llm *LLM) Free() error {
if llm.llama != nil {
llm.llama.Free()
llm.llama = nil
}
if llm.draftModel != nil {
llm.draftModel.Free()
llm.draftModel = nil
}
return nil
}
func (llm *LLM) Load(opts *pb.ModelOptions) error {
ropeFreqBase := float32(10000)
ropeFreqScale := float32(1)

View File

@@ -6,7 +6,4 @@ local-store:
package:
bash package.sh
build: local-store package
clean:
rm -f local-store
build: local-store package

View File

@@ -8,5 +8,5 @@ set -e
CURDIR=$(dirname "$(realpath $0)")
mkdir -p $CURDIR/package
cp -avf $CURDIR/local-store $CURDIR/package/
cp -avrf $CURDIR/local-store $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/

View File

@@ -1,19 +0,0 @@
GOCMD?=go
GO_TAGS?=
OPUS_CFLAGS := $(shell pkg-config --cflags opus)
OPUS_LIBS := $(shell pkg-config --libs opus)
libopusshim.so: csrc/opus_shim.c
$(CC) -shared -fPIC -o $@ $< $(OPUS_CFLAGS) $(OPUS_LIBS)
opus: libopusshim.so
$(GOCMD) build -tags "$(GO_TAGS)" -o opus ./
package: opus
bash package.sh
build: package
clean:
rm -f opus libopusshim.so

View File

@@ -1,256 +0,0 @@
package main
import (
"errors"
"fmt"
"os"
"path/filepath"
"runtime"
"sync"
"github.com/ebitengine/purego"
)
const (
ApplicationVoIP = 2048
ApplicationAudio = 2049
ApplicationRestrictedLowDelay = 2051
)
var (
initOnce sync.Once
initErr error
opusLib uintptr
shimLib uintptr
// libopus functions
cEncoderCreate func(fs int32, channels int32, application int32, errPtr *int32) uintptr
cEncode func(st uintptr, pcm *int16, frameSize int32, data *byte, maxBytes int32) int32
cEncoderDestroy func(st uintptr)
cDecoderCreate func(fs int32, channels int32, errPtr *int32) uintptr
cDecode func(st uintptr, data *byte, dataLen int32, pcm *int16, frameSize int32, decodeFec int32) int32
cDecoderDestroy func(st uintptr)
// shim functions (non-variadic wrappers for opus_encoder_ctl)
cSetBitrate func(st uintptr, bitrate int32) int32
cSetComplexity func(st uintptr, complexity int32) int32
)
func loadLib(names []string) (uintptr, error) {
var firstErr error
for _, name := range names {
h, err := purego.Dlopen(name, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err == nil {
return h, nil
}
if firstErr == nil {
firstErr = err
}
}
return 0, firstErr
}
func ensureInit() error {
initOnce.Do(func() {
initErr = doInit()
})
return initErr
}
const shimHint = "ensure libopus-dev is installed and rebuild, or set OPUS_LIBRARY / OPUS_SHIM_LIBRARY env vars"
func doInit() error {
opusNames := opusSearchPaths()
var err error
opusLib, err = loadLib(opusNames)
if err != nil {
return fmt.Errorf("opus: failed to load libopus (%s): %w", shimHint, err)
}
purego.RegisterLibFunc(&cEncoderCreate, opusLib, "opus_encoder_create")
purego.RegisterLibFunc(&cEncode, opusLib, "opus_encode")
purego.RegisterLibFunc(&cEncoderDestroy, opusLib, "opus_encoder_destroy")
purego.RegisterLibFunc(&cDecoderCreate, opusLib, "opus_decoder_create")
purego.RegisterLibFunc(&cDecode, opusLib, "opus_decode")
purego.RegisterLibFunc(&cDecoderDestroy, opusLib, "opus_decoder_destroy")
shimNames := shimSearchPaths()
shimLib, err = loadLib(shimNames)
if err != nil {
return fmt.Errorf("opus: failed to load libopusshim (%s): %w", shimHint, err)
}
purego.RegisterLibFunc(&cSetBitrate, shimLib, "opus_shim_encoder_set_bitrate")
purego.RegisterLibFunc(&cSetComplexity, shimLib, "opus_shim_encoder_set_complexity")
return nil
}
func opusSearchPaths() []string {
var paths []string
if env := os.Getenv("OPUS_LIBRARY"); env != "" {
paths = append(paths, env)
}
if exe, err := os.Executable(); err == nil {
dir := filepath.Dir(exe)
paths = append(paths, filepath.Join(dir, "libopus.so.0"), filepath.Join(dir, "libopus.so"))
if runtime.GOOS == "darwin" {
paths = append(paths, filepath.Join(dir, "libopus.dylib"))
}
}
paths = append(paths, "libopus.so.0", "libopus.so", "libopus.dylib", "opus.dll")
if runtime.GOOS == "darwin" {
paths = append(paths,
"/opt/homebrew/lib/libopus.dylib",
"/usr/local/lib/libopus.dylib",
)
}
return paths
}
func shimSearchPaths() []string {
var paths []string
if env := os.Getenv("OPUS_SHIM_LIBRARY"); env != "" {
paths = append(paths, env)
}
if exe, err := os.Executable(); err == nil {
dir := filepath.Dir(exe)
paths = append(paths, filepath.Join(dir, "libopusshim.so"))
if runtime.GOOS == "darwin" {
paths = append(paths, filepath.Join(dir, "libopusshim.dylib"))
}
}
paths = append(paths, "./libopusshim.so", "libopusshim.so")
if runtime.GOOS == "darwin" {
paths = append(paths, "./libopusshim.dylib", "libopusshim.dylib")
}
return paths
}
// Encoder wraps a libopus OpusEncoder via purego.
type Encoder struct {
st uintptr
}
func NewEncoder(sampleRate, channels, application int) (*Encoder, error) {
if err := ensureInit(); err != nil {
return nil, err
}
var opusErr int32
st := cEncoderCreate(int32(sampleRate), int32(channels), int32(application), &opusErr)
if opusErr != 0 || st == 0 {
return nil, fmt.Errorf("opus_encoder_create failed: error %d", opusErr)
}
return &Encoder{st: st}, nil
}
// Encode encodes a frame of PCM int16 samples. It returns the number of bytes
// written to out, or a negative error code.
func (e *Encoder) Encode(pcm []int16, frameSize int, out []byte) (int, error) {
if len(pcm) == 0 || len(out) == 0 {
return 0, errors.New("opus encode: empty input or output buffer")
}
n := cEncode(e.st, &pcm[0], int32(frameSize), &out[0], int32(len(out)))
if n < 0 {
return 0, fmt.Errorf("opus_encode failed: error %d", n)
}
return int(n), nil
}
func (e *Encoder) SetBitrate(bitrate int) error {
if ret := cSetBitrate(e.st, int32(bitrate)); ret != 0 {
return fmt.Errorf("opus set bitrate: error %d", ret)
}
return nil
}
func (e *Encoder) SetComplexity(complexity int) error {
if ret := cSetComplexity(e.st, int32(complexity)); ret != 0 {
return fmt.Errorf("opus set complexity: error %d", ret)
}
return nil
}
func (e *Encoder) Close() {
if e.st != 0 {
cEncoderDestroy(e.st)
e.st = 0
}
}
// Decoder wraps a libopus OpusDecoder via purego.
type Decoder struct {
st uintptr
}
func NewDecoder(sampleRate, channels int) (*Decoder, error) {
if err := ensureInit(); err != nil {
return nil, err
}
var opusErr int32
st := cDecoderCreate(int32(sampleRate), int32(channels), &opusErr)
if opusErr != 0 || st == 0 {
return nil, fmt.Errorf("opus_decoder_create failed: error %d", opusErr)
}
return &Decoder{st: st}, nil
}
// Decode decodes an Opus packet into pcm. frameSize is the max number of
// samples per channel that pcm can hold. Returns the number of decoded samples
// per channel.
func (d *Decoder) Decode(data []byte, pcm []int16, frameSize int, fec bool) (int, error) {
if len(pcm) == 0 {
return 0, errors.New("opus decode: empty output buffer")
}
var dataPtr *byte
var dataLen int32
if len(data) > 0 {
dataPtr = &data[0]
dataLen = int32(len(data))
}
decodeFec := int32(0)
if fec {
decodeFec = 1
}
n := cDecode(d.st, dataPtr, dataLen, &pcm[0], int32(frameSize), decodeFec)
if n < 0 {
return 0, fmt.Errorf("opus_decode failed: error %d", n)
}
return int(n), nil
}
func (d *Decoder) Close() {
if d.st != 0 {
cDecoderDestroy(d.st)
d.st = 0
}
}
// Init eagerly loads the opus libraries, returning any error.
// Calling this is optional; the libraries are loaded lazily on first use.
func Init() error {
return ensureInit()
}
// Reset allows re-initialization (for testing).
func Reset() {
initOnce = sync.Once{}
initErr = nil
opusLib = 0
shimLib = 0
}

View File

@@ -1,9 +0,0 @@
#include <opus.h>
int opus_shim_encoder_set_bitrate(OpusEncoder *st, opus_int32 bitrate) {
return opus_encoder_ctl(st, OPUS_SET_BITRATE(bitrate));
}
int opus_shim_encoder_set_complexity(OpusEncoder *st, opus_int32 complexity) {
return opus_encoder_ctl(st, OPUS_SET_COMPLEXITY(complexity));
}

View File

@@ -1,16 +0,0 @@
package main
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, &Opus{}); err != nil {
panic(err)
}
}

View File

@@ -1,184 +0,0 @@
package main
import (
"fmt"
"sync"
"time"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/sound"
)
const (
opusSampleRate = 48000
opusChannels = 1
opusFrameSize = 960 // 20ms at 48kHz
opusMaxPacketSize = 4000
opusMaxFrameSize = 5760 // 120ms at 48kHz
decoderIdleTTL = 60 * time.Second
decoderEvictTick = 30 * time.Second
)
type cachedDecoder struct {
mu sync.Mutex
dec *Decoder
lastUsed time.Time
}
type Opus struct {
base.Base
decodersMu sync.Mutex
decoders map[string]*cachedDecoder
}
func (o *Opus) Load(opts *pb.ModelOptions) error {
o.decoders = make(map[string]*cachedDecoder)
go o.evictLoop()
return Init()
}
func (o *Opus) evictLoop() {
ticker := time.NewTicker(decoderEvictTick)
defer ticker.Stop()
for range ticker.C {
o.decodersMu.Lock()
now := time.Now()
for id, cd := range o.decoders {
if now.Sub(cd.lastUsed) > decoderIdleTTL {
cd.dec.Close()
delete(o.decoders, id)
}
}
o.decodersMu.Unlock()
}
}
// getOrCreateDecoder returns a cached decoder for the given session ID,
// creating one if it doesn't exist yet.
func (o *Opus) getOrCreateDecoder(sessionID string) (*cachedDecoder, error) {
o.decodersMu.Lock()
defer o.decodersMu.Unlock()
if cd, ok := o.decoders[sessionID]; ok {
cd.lastUsed = time.Now()
return cd, nil
}
dec, err := NewDecoder(opusSampleRate, opusChannels)
if err != nil {
return nil, err
}
cd := &cachedDecoder{dec: dec, lastUsed: time.Now()}
o.decoders[sessionID] = cd
return cd, nil
}
func (o *Opus) AudioEncode(req *pb.AudioEncodeRequest) (*pb.AudioEncodeResult, error) {
enc, err := NewEncoder(opusSampleRate, opusChannels, ApplicationAudio)
if err != nil {
return nil, fmt.Errorf("opus encoder create: %w", err)
}
defer enc.Close()
if err := enc.SetBitrate(64000); err != nil {
return nil, fmt.Errorf("opus set bitrate: %w", err)
}
if err := enc.SetComplexity(10); err != nil {
return nil, fmt.Errorf("opus set complexity: %w", err)
}
samples := sound.BytesToInt16sLE(req.PcmData)
if len(samples) == 0 {
return &pb.AudioEncodeResult{
SampleRate: opusSampleRate,
SamplesPerFrame: opusFrameSize,
}, nil
}
if req.SampleRate != 0 && int(req.SampleRate) != opusSampleRate {
samples = sound.ResampleInt16(samples, int(req.SampleRate), opusSampleRate)
}
var frames [][]byte
packet := make([]byte, opusMaxPacketSize)
for offset := 0; offset+opusFrameSize <= len(samples); offset += opusFrameSize {
frame := samples[offset : offset+opusFrameSize]
n, err := enc.Encode(frame, opusFrameSize, packet)
if err != nil {
return nil, fmt.Errorf("opus encode: %w", err)
}
out := make([]byte, n)
copy(out, packet[:n])
frames = append(frames, out)
}
return &pb.AudioEncodeResult{
Frames: frames,
SampleRate: opusSampleRate,
SamplesPerFrame: opusFrameSize,
}, nil
}
func (o *Opus) AudioDecode(req *pb.AudioDecodeRequest) (*pb.AudioDecodeResult, error) {
if len(req.Frames) == 0 {
return &pb.AudioDecodeResult{
SampleRate: opusSampleRate,
SamplesPerFrame: opusFrameSize,
}, nil
}
// Use a persistent decoder when a session ID is provided so that Opus
// prediction state carries across batches. Fall back to a fresh decoder
// for backward compatibility.
sessionID := req.Options["session_id"]
var cd *cachedDecoder
var ownedDec *Decoder
if sessionID != "" && o.decoders != nil {
var err error
cd, err = o.getOrCreateDecoder(sessionID)
if err != nil {
return nil, fmt.Errorf("opus decoder create: %w", err)
}
cd.mu.Lock()
defer cd.mu.Unlock()
} else {
dec, err := NewDecoder(opusSampleRate, opusChannels)
if err != nil {
return nil, fmt.Errorf("opus decoder create: %w", err)
}
ownedDec = dec
defer ownedDec.Close()
}
dec := ownedDec
if cd != nil {
dec = cd.dec
}
var allSamples []int16
var samplesPerFrame int32
pcm := make([]int16, opusMaxFrameSize)
for _, frame := range req.Frames {
n, err := dec.Decode(frame, pcm, opusMaxFrameSize, false)
if err != nil {
return nil, fmt.Errorf("opus decode: %w", err)
}
if samplesPerFrame == 0 {
samplesPerFrame = int32(n)
}
allSamples = append(allSamples, pcm[:n]...)
}
return &pb.AudioDecodeResult{
PcmData: sound.Int16toBytesLE(allSamples),
SampleRate: opusSampleRate,
SamplesPerFrame: samplesPerFrame,
}, nil
}

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