Instructions to use sq6er4/Step3-VL-10B-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sq6er4/Step3-VL-10B-Q4_K_M with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Use Docker
docker model run hf.co/sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use sq6er4/Step3-VL-10B-Q4_K_M with Ollama:
ollama run hf.co/sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
- Unsloth Desktop
- Pi
How to use sq6er4/Step3-VL-10B-Q4_K_M with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sq6er4/Step3-VL-10B-Q4_K_M with Docker Model Runner:
docker model run hf.co/sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
- Lemonade
How to use sq6er4/Step3-VL-10B-Q4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.Step3-VL-10B-Q4_K_M-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use sq6er4/Step3-VL-10B-Q4_K_M with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sq6er4/Step3-VL-10B-Q4_K_M with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "sq6er4/Step3-VL-10B-Q4_K_M:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Check out the documentation for more information.
Step3-VL-10B GGUF
stepfun-ai/Step3-VL-10B 的 GGUF 量化版本,支持 llama.cpp 文本+视觉推理。
模型信息
| 项目 | 值 |
|---|---|
| 原始模型 | stepfun-ai/Step3-VL-10B |
| 语言模型架构 | Qwen3 (36层, 4096维) |
| 视觉编码器 | ViT-Large (47层, 1536维) |
| 视觉投影器 | 2层 Conv2d + Linear (stride-2 下采样) |
| 许可证 | Apache 2.0 |
文件说明
| 文件 | 大小 | 说明 |
|---|---|---|
Step3-VL-10B-Q4_K_M.gguf |
4.7GB | 文本模型 (Q4_K_M 量化) |
mmproj-Step3-VL-10B-f16.gguf |
3.7GB | 视觉投影器 (F16, 包含完整 3 层 projector) |
使用方法
# 启动服务器
llama-server \
-m Step3-VL-10B-Q4_K_M.gguf \
--mmproj mmproj-Step3-VL-10B-f16.gguf \
--host 0.0.0.0 --port 8080 -ngl 99
# 文本推理
curl http://localhost:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"step3-vl","messages":[{"role":"user","content":"Hello"}],"max_tokens":50}'
# 视觉推理 (base64 图片)
IMG_B64=$(base64 -i image.png | tr -d '\n')
curl http://localhost:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d "{\"model\":\"step3-vl\",\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,$IMG_B64\"}},{\"type\":\"text\",\"text\":\"What is this?\"}]}],\"max_tokens\":100}"
mmproj 说明
本仓库的 mmproj 使用最新版 llama.cpp 的 convert_hf_to_gguf.py --mmproj 从原始 safetensors 转换,包含完整的 3 层视觉投影器:
mm.0— Conv2d(1536→3072, k=3, s=2)mm.1— Conv2d(3072→6144, k=3, s=2)mm.model.fc— Linear(6144→4096)
旧版 mmproj 可能缺少 mm.1 和 mm.model.fc,导致加载报错 unable to find tensor mm.1.weight。
测试环境
- Jetson AGX Xavier 32GB (Volta/Sm_70)
- llama.cpp b1-95b8b8e
- 推理速度: 文本 ~12 t/s, 视觉 ~14.5 t/s
- Downloads last month
- 89
Hardware compatibility
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