Instructions to use pedrodev2026/microcoder-1.5b-GGUF 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 pedrodev2026/microcoder-1.5b-GGUF 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 pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf pedrodev2026/microcoder-1.5b-GGUF: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 pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf pedrodev2026/microcoder-1.5b-GGUF: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 pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use pedrodev2026/microcoder-1.5b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pedrodev2026/microcoder-1.5b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pedrodev2026/microcoder-1.5b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
- Ollama
How to use pedrodev2026/microcoder-1.5b-GGUF with Ollama:
ollama run hf.co/pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use pedrodev2026/microcoder-1.5b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pedrodev2026/microcoder-1.5b-GGUF: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": "pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use pedrodev2026/microcoder-1.5b-GGUF with Docker Model Runner:
docker model run hf.co/pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
- Lemonade
How to use pedrodev2026/microcoder-1.5b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.microcoder-1.5b-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use pedrodev2026/microcoder-1.5b-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pedrodev2026/microcoder-1.5b-GGUF: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 pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use pedrodev2026/microcoder-1.5b-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pedrodev2026/microcoder-1.5b-GGUF: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 "pedrodev2026/microcoder-1.5b-GGUF: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"
Create MODEL_CREDITS.md
Browse files- MODEL_CREDITS.md +74 -0
MODEL_CREDITS.md
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# Model Credits - Microcoder-1.5B
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## Base Model
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This fine-tuned model is built upon **Qwen 2.5 Coder 1.5B Instruct**, created and maintained by [Alibaba Cloud](https://www.alibabacloud.com/).
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### Original Model Information
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- **Model Name**: Qwen 2.5 Coder 1.5B Instruct
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- **Creator**: Alibaba Cloud
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- **Repository**: [Qwen Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
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- **License**: Apache 2.0
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The Qwen 2.5 Coder series represents a significant advancement in code generation models, optimized for programming tasks and instruction following.
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## Model Redistribution
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We acknowledge **Unsloth** for their role in redistributing and optimizing the base model, making it more accessible to the community.
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- **Organization**: Unsloth
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- **Website**: [Unsloth.ai](https://unsloth.ai)
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## Fine-Tuned Model (Microcoder-1.5B)
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- **License**: BSD-3-Clause
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- **Status**: This fine-tuned version incorporates specialized training and optimizations
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## License Summary
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| Component | License |
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|-----------|---------|
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| Base Model (Qwen 2.5 Coder 1.5B) | Apache 2.0 |
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| Fine-tuned Model (Microcoder-1.5B) | BSD-3-Clause |
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## Dataset Credits
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For detailed information about the datasets used in the fine-tuning process, please refer to [`DATASET_CREDITS.md`](./DATASET_CREDITS.md).
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## Attribution
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When using Microcoder-1.5B, please provide appropriate attribution to:
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1. **Alibaba Cloud** - for the original Qwen 2.5 Coder model
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2. **Unsloth** - for model redistribution and optimization
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3. **Microcoder Contributors** - for the fine-tuning and improvements
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## Citation
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If you use this model in your research or projects, please consider citing:
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```bibtex
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@misc{microcoder2026,
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title={Microcoder-1.5B: A Fine-tuned Code Generation Model},
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author={[pedrodev2026]},
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year={2026},
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url={[https://huggingface.co/pedrodev2026/microcoder-1.5b]}
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}
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```
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And also cite the original Qwen model:
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```bibtex
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@article{hui2024qwen2,
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title={Qwen2.5-Coder Technical Report},
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author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
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journal={arXiv preprint arXiv:2409.12186},
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year={2024}
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}
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```
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---
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**Last Updated**: 2026
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**Model Version**: 1.5B
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