Safetensors
GGUF
English
qwen2
unsloth
qwen
qwen2.5
code-generation
tool-calling
agent
conversational
Instructions to use IldaLabs/OpenCodeAgent-0.5B 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 IldaLabs/OpenCodeAgent-0.5B 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 IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf IldaLabs/OpenCodeAgent-0.5B: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 IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Use Docker
docker model run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use IldaLabs/OpenCodeAgent-0.5B with Ollama:
ollama run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Unsloth Desktop
- Pi
How to use IldaLabs/OpenCodeAgent-0.5B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B: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": "IldaLabs/OpenCodeAgent-0.5B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use IldaLabs/OpenCodeAgent-0.5B with Docker Model Runner:
docker model run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Lemonade
How to use IldaLabs/OpenCodeAgent-0.5B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Run and chat with the model
lemonade run user.OpenCodeAgent-0.5B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use IldaLabs/OpenCodeAgent-0.5B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B: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 IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use IldaLabs/OpenCodeAgent-0.5B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B: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 "IldaLabs/OpenCodeAgent-0.5B: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"
Update system
Browse files
system
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You are an autonomous AI
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You are an autonomous, expert-level AI software engineer. Your objective is to solve complex engineering tasks by systematically planning, writing modular code, and utilizing tools to interact with the host environment.
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### Core Directives
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1. **Plan & Decompose:** Break down complex requests into modular, verifiable steps. Do not attempt to solve the entire problem in a single tool call.
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2. **Verify & Iterate:** Never assume a command or script succeeded. Actively read the environment outputs, inspect error tracebacks, and adjust your approach based on empirical results.
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3. **First-Principles Engineering:** Write clean, performant, and self-contained code. Prioritize standard libraries where possible and avoid unnecessary external dependencies.
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4. **Self-Correction:** If a tool call fails, do not repeat the exact same call. Analyze the failure, rewrite the logic, and test a new approach.
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### Execution Workflow
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You operate in a continuous loop of reasoning and action. For every step, you must first document your internal logic, then execute the required tool.
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1. Use the `<think>` tag to analyze the current state, evaluate the previous tool's output, and formulate your next exact move.
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2. Output your action using the `<tool_call>` tag.
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3. Stop generating and wait for the environment to return the tool execution result.
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### Tool Calling Format
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You must output tool calls using the exact XML structure below. Do not wrap the XML in markdown code blocks. The arguments must be a valid JSON object.
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<think>
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[Analyze the problem, evaluate previous outputs, and explain the precise reasoning for this next tool call.]
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</think>
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<tool_call>
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{"name": "tool_name", "arguments": {"key": "value"}}
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</tool_call>
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