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8 Commits
dependabot
...
master
| Author | SHA1 | Date | |
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820bd7dd01 | ||
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42cb7bda19 | ||
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2fb9940b8a | ||
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2ff0ad4190 | ||
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bd12103ed4 | ||
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2e17edd72a | ||
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24aab68b3f | ||
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5bdbb10593 |
@@ -1,5 +1,5 @@
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LLAMA_VERSION?=338085c69e486b7155e5b03d7b5087e02c0e2528
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LLAMA_VERSION?=05a6f0e8946914918758db767f6eb04bc1e38507
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LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
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CMAKE_ARGS?=
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@@ -417,6 +417,12 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
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// n_ctx_checkpoints: max context checkpoints per slot (default: 8)
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params.n_ctx_checkpoints = 8;
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// llama memory fit fails if we don't provide a buffer for tensor overrides
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const size_t ntbo = llama_max_tensor_buft_overrides();
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while (params.tensor_buft_overrides.size() < ntbo) {
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params.tensor_buft_overrides.push_back({nullptr, nullptr});
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}
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// decode options. Options are in form optname:optvale, or if booleans only optname.
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for (int i = 0; i < request->options_size(); i++) {
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std::string opt = request->options(i);
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@@ -3,3 +3,6 @@ protobuf
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certifi
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packaging==24.1
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setuptools
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h11
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gradio
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uvicorn
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@@ -4,4 +4,6 @@ certifi
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packaging==24.1
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soundfile
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setuptools
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six
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six
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scipy
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librosa
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@@ -9,7 +9,12 @@ else
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fi
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installRequirements
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if [ "x${USE_PIP}" == "xtrue" ]; then
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pip install "setuptools<70.0.0"
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else
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uv pip install "setuptools<70.0.0"
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fi
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# Apply patch to fix PyTorch compatibility issue in voxcpm
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# This fixes the "Dimension out of range" error in scaled_dot_product_attention
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# by changing .contiguous() to .unsqueeze(0) in the attention module
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@@ -83,7 +83,7 @@ type RunCMD struct {
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EnableTracing bool `env:"LOCALAI_ENABLE_TRACING,ENABLE_TRACING" help:"Enable API tracing" group:"api"`
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TracingMaxItems int `env:"LOCALAI_TRACING_MAX_ITEMS" default:"1024" help:"Maximum number of traces to keep" group:"api"`
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AgentJobRetentionDays int `env:"LOCALAI_AGENT_JOB_RETENTION_DAYS,AGENT_JOB_RETENTION_DAYS" default:"30" help:"Number of days to keep agent job history (default: 30)" group:"api"`
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OpenResponsesStoreTTL string `env:"LOCALAI_OPEN_RESPONSES_STORE_TTL,OPEN_RESPONSES_STORE_TTL" default:"0" help:"TTL for Open Responses store (e.g., 1h, 30m, 0 = no expiration)" group:"api"`
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OpenResponsesStoreTTL string `env:"LOCALAI_OPEN_RESPONSES_STORE_TTL,OPEN_RESPONSES_STORE_TTL" default:"0" help:"TTL for Open Responses store (e.g., 1h, 30m, 0 = no expiration)" group:"api"`
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Version bool
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}
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@@ -23,6 +23,7 @@ import (
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"github.com/mudler/LocalAI/core/templates"
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laudio "github.com/mudler/LocalAI/pkg/audio"
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"github.com/mudler/LocalAI/pkg/functions"
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"github.com/mudler/LocalAI/pkg/utils"
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"github.com/mudler/LocalAI/pkg/grpc/proto"
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model "github.com/mudler/LocalAI/pkg/model"
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"github.com/mudler/LocalAI/pkg/reasoning"
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@@ -949,7 +950,12 @@ func triggerResponse(session *Session, conv *Conversation, c *LockedWebsocket, o
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case types.MessageContentTypeInputAudio:
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textContent += content.Transcript
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case types.MessageContentTypeInputImage:
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msg.StringImages = append(msg.StringImages, content.ImageURL)
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img, err := utils.GetContentURIAsBase64(content.ImageURL)
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if err != nil {
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xlog.Warn("Failed to process image", "error", err)
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continue
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}
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msg.StringImages = append(msg.StringImages, img)
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imgIndex++
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nrOfImgsInMessage++
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}
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@@ -175,8 +175,8 @@ type ToolFunction struct {
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// The description of the function, including guidance on when and how to call it, and guidance about what to tell the user when calling (if anything).
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Description string `json:"description"`
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// The type of the tool, i.e. function.
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Parameters any `json:"parameters"`
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// The jsonschema representing the parameters
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Parameters any `json:"parameters,omitempty"`
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}
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func (t ToolFunction) ToolType() ToolType {
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@@ -1,4 +1,46 @@
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---
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- &nanbeige4
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name: "nanbeige4.1-3b-q8"
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url: "github:mudler/LocalAI/gallery/nanbeige4.1.yaml@master"
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urls:
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- https://huggingface.co/Nanbeige/Nanbeige4.1-3B
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- https://huggingface.co/Edge-Quant/Nanbeige4.1-3B-Q8_0-GGUF
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icon: https://cdn-avatars.huggingface.co/v1/production/uploads/646f0d118ff94af23bc44aab/GXHCollpMRgvYqUXQ2BQ7.png
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license: apache-2.0
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description: |
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Nanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust reasoning, preference alignment, and effective agentic behaviors.
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Key features:
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Strong Reasoning: Capable of solving complex, multi-step problems through sustained and coherent reasoning within a single forward pass, reliably producing correct answers on benchmarks like LiveCodeBench-Pro, IMO-Answer-Bench, and AIME 2026 I.
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Robust Preference Alignment: Outperforms same-scale models (e.g., Qwen3-4B-2507, Nanbeige4-3B-2511) and larger models (e.g., Qwen3-30B-A3B, Qwen3-32B) on Arena-Hard-v2 and Multi-Challenge.
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Agentic Capability: First general small model to natively support deep-search tasks and sustain complex problem-solving with >500 rounds of tool invocations; excels in benchmarks like xBench-DeepSearch (75), Browse-Comp (39), and others.
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tags:
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- llm
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- gguf
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- gpu
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- cpu
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- nanbeige
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- reasoning
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- agent
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overrides:
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parameters:
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model: nanbeige4.1-3b-q8_0.gguf
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files:
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- filename: nanbeige4.1-3b-q8_0.gguf
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sha256: a5a4379e50605c5e5a31bb1716a211fb16691fea7e13ede7f88796e1f617d9e0
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uri: huggingface://Edge-Quant/Nanbeige4.1-3B-Q8_0-GGUF/nanbeige4.1-3b-q8_0.gguf
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- !!merge <<: *nanbeige4
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name: "nanbeige4.1-3b-q4"
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urls:
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- https://huggingface.co/Nanbeige/Nanbeige4.1-3B
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- https://huggingface.co/Edge-Quant/Nanbeige4.1-3B-Q4_K_M-GGUF
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overrides:
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parameters:
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model: nanbeige4.1-3b-q4_k_m.gguf
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files:
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- filename: nanbeige4.1-3b-q4_k_m.gguf
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sha256: 043246350c952877b38958a9e35c480419008b6b2d52bedaf2b805ed2447b4df
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uri: huggingface://Edge-Quant/Nanbeige4.1-3B-Q4_K_M-GGUF/nanbeige4.1-3b-q4_k_m.gguf
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- name: nemo-parakeet-tdt-0.6b
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license: apache-2.0
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url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
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16
gallery/nanbeige4.1.yaml
Normal file
16
gallery/nanbeige4.1.yaml
Normal file
@@ -0,0 +1,16 @@
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---
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name: nanbeige4.1
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config_file: |
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backend: llama-cpp
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function:
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grammar:
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disable: true
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known_usecases:
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- chat
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options:
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- use_jinja:true
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parameters:
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model: llama-cpp/models/nanbeige4.1-3b-q8_0.gguf
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template:
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use_tokenizer_template: true
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@@ -45,9 +45,8 @@ const (
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)
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var (
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cuda13DirExists bool
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cuda12DirExists bool
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capabilityLogged bool
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cuda13DirExists bool
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cuda12DirExists bool
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)
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func init() {
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@@ -72,9 +71,15 @@ func (s *SystemState) Capability(capMap map[string]string) string {
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}
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func (s *SystemState) getSystemCapabilities() string {
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if s.systemCapabilities != "" {
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return s.systemCapabilities
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}
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capability := os.Getenv(capabilityEnv)
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if capability != "" {
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xlog.Info("Using forced capability from environment variable", "capability", capability, "env", capabilityEnv)
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s.systemCapabilities = capability
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return capability
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}
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@@ -91,20 +96,23 @@ func (s *SystemState) getSystemCapabilities() string {
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capability, err := os.ReadFile(capabilityRunFile)
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if err == nil {
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xlog.Info("Using forced capability run file", "capabilityRunFile", capabilityRunFile, "capability", string(capability), "env", capabilityRunFileEnv)
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return strings.Trim(strings.TrimSpace(string(capability)), "\n")
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s.systemCapabilities = strings.Trim(strings.TrimSpace(string(capability)), "\n")
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return s.systemCapabilities
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}
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}
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// If we are on mac and arm64, we will return metal
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if runtime.GOOS == "darwin" && runtime.GOARCH == "arm64" {
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xlog.Info("Using metal capability (arm64 on mac)", "env", capabilityEnv)
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return metal
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s.systemCapabilities = metal
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return s.systemCapabilities
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}
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// If we are on mac and x86, we will return darwin-x86
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if runtime.GOOS == "darwin" && runtime.GOARCH == "amd64" {
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xlog.Info("Using darwin-x86 capability (amd64 on mac)", "env", capabilityEnv)
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return darwinX86
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s.systemCapabilities = darwinX86
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return s.systemCapabilities
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}
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// If arm64 on linux and a nvidia gpu is detected, we will return nvidia-l4t
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@@ -112,39 +120,43 @@ func (s *SystemState) getSystemCapabilities() string {
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if s.GPUVendor == Nvidia {
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xlog.Info("Using nvidia-l4t capability (arm64 on linux)", "env", capabilityEnv)
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if cuda13DirExists {
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return nvidiaL4TCuda13
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s.systemCapabilities = nvidiaL4TCuda13
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return s.systemCapabilities
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}
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if cuda12DirExists {
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return nvidiaL4TCuda12
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s.systemCapabilities = nvidiaL4TCuda12
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return s.systemCapabilities
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}
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return nvidiaL4T
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s.systemCapabilities = nvidiaL4T
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return s.systemCapabilities
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}
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}
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if cuda13DirExists {
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return nvidiaCuda13
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s.systemCapabilities = nvidiaCuda13
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return s.systemCapabilities
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}
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if cuda12DirExists {
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return nvidiaCuda12
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s.systemCapabilities = nvidiaCuda12
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return s.systemCapabilities
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}
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if s.GPUVendor == "" {
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xlog.Info("Default capability (no GPU detected)", "env", capabilityEnv)
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return defaultCapability
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s.systemCapabilities = defaultCapability
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return s.systemCapabilities
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}
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if !capabilityLogged {
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xlog.Info("Capability automatically detected", "capability", s.GPUVendor, "env", capabilityEnv)
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capabilityLogged = true
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}
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// If vram is less than 4GB, let's default to CPU but warn the user that they can override that via env
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if s.VRAM <= 4*1024*1024*1024 {
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xlog.Warn("VRAM is less than 4GB, defaulting to CPU", "env", capabilityEnv)
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return defaultCapability
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s.systemCapabilities = defaultCapability
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return s.systemCapabilities
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}
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return s.GPUVendor
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s.systemCapabilities = s.GPUVendor
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return s.systemCapabilities
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}
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// BackendPreferenceTokens returns a list of substrings that represent the preferred
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@@ -19,6 +19,8 @@ type SystemState struct {
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Backend Backend
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Model Model
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VRAM uint64
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systemCapabilities string
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}
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type SystemStateOptions func(*SystemState)
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@@ -53,5 +55,7 @@ func GetSystemState(opts ...SystemStateOptions) (*SystemState, error) {
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state.VRAM, _ = xsysinfo.TotalAvailableVRAM()
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xlog.Debug("Total available VRAM", "vram", state.VRAM)
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state.getSystemCapabilities()
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return state, nil
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}
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