Instructions to use devon-ai/devonai-dev0.1-7b 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 devon-ai/devonai-dev0.1-7b 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 devon-ai/devonai-dev0.1-7b:Q4_K_M # Run inference directly in the terminal: llama cli -hf devon-ai/devonai-dev0.1-7b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf devon-ai/devonai-dev0.1-7b:Q4_K_M # Run inference directly in the terminal: llama cli -hf devon-ai/devonai-dev0.1-7b:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf devon-ai/devonai-dev0.1-7b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf devon-ai/devonai-dev0.1-7b:Q4_K_M
Use Docker
docker model run hf.co/devon-ai/devonai-dev0.1-7b:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use devon-ai/devonai-dev0.1-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "devon-ai/devonai-dev0.1-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "devon-ai/devonai-dev0.1-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/devon-ai/devonai-dev0.1-7b:Q4_K_M
- Ollama
How to use devon-ai/devonai-dev0.1-7b with Ollama:
ollama run hf.co/devon-ai/devonai-dev0.1-7b:Q4_K_M
- Unsloth Desktop
- Pi
How to use devon-ai/devonai-dev0.1-7b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf devon-ai/devonai-dev0.1-7b: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": "devon-ai/devonai-dev0.1-7b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use devon-ai/devonai-dev0.1-7b with Docker Model Runner:
docker model run hf.co/devon-ai/devonai-dev0.1-7b:Q4_K_M
- Lemonade
How to use devon-ai/devonai-dev0.1-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull devon-ai/devonai-dev0.1-7b:Q4_K_M
Run and chat with the model
lemonade run user.devonai-dev0.1-7b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use devon-ai/devonai-dev0.1-7b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf devon-ai/devonai-dev0.1-7b: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 devon-ai/devonai-dev0.1-7b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use devon-ai/devonai-dev0.1-7b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf devon-ai/devonai-dev0.1-7b: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 "devon-ai/devonai-dev0.1-7b: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"
DevonAI Dev0.1 (7B)
基於 Qwen2.5-7B-Instruct 以 LoRA(r=16)微調的本地智能體模型,內化 DevonAI 的工具呼叫協定,可在消費級電腦上以 node-llama-cpp 或 Ollama 執行。由 DevonAI 專案訓練管線產生(Kaggle 免費 T4 GPU)。
訓練資訊
| 項目 | 值 |
|---|---|
| 基座模型 | Qwen/Qwen2.5-7B-Instruct (unsloth-bnb-4bit) |
| 微調方式 | LoRA r=16, alpha=16, dropout=0.05, 4-bit QLoRA |
| 訓練資料 | 2,000 筆合成對話(1,749 train / 251 val) |
| Epochs / Steps | 3 epochs / 375 steps |
| Learning rate | 2e-4(cosine, warmup 10) |
| 訓練平台 | Kaggle Notebooks, T4 x2 16GB |
| 量化 | Q4_K_M(4.68 GB) |
訓練目標為讓模型穩定遵循 DevonAI 的工具協定,例如:
{"tool": "terminal", "args": {"command": "ls -la"}} {"tool": "file", "args": {"action": "read", "path": "src/index.ts"}}
檔案
- dev0.1-q4_k_m.gguf — 主模型(Q4_K_M,4.68GB)
- lora/ — LoRA 權重(可續訓)
- training_config.yaml — 訓練超參數
使用方式
node-llama-cpp(DevonAI CLI)
node dist/index.js -b local -m dev0.1-q4_k_m.gguf "你的問題"
Ollama
建立 Modelfile(將 USER 換成你的 HF 帳號)後執行 ollama create dev0.1 -f Modelfile:
FROM https://huggingface.co/USER/devonai-dev0.1-7b/resolve/main/dev0.1-q4_k_m.gguf TEMPLATE """{{- if .System }}<|im_start|>system {{ .System }}<|im_end|> {{- end }} {{- range .Messages }} <|im_start|>{{ .Role }} {{ .Content }}<|im_end|> {{- end }} <|im_start|>assistant""" PARAMETER temperature 0.6 PARAMETER num_ctx 4096
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