Instructions to use pedrodev2026/microcoder-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pedrodev2026/microcoder-1.5b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pedrodev2026/microcoder-1.5b-GGUF",
	filename="microcoder-1.5b-GGUF-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pedrodev2026/microcoder-1.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio

How to use pedrodev2026/microcoder-1.5b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pedrodev2026/microcoder-1.5b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pedrodev2026/microcoder-1.5b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pedrodev2026/microcoder-1.5b-GGUF to start chatting

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-server -hf pedrodev2026/microcoder-1.5b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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

Hermes Agent new

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-server -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 new
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

microcoder-1.5b-GGUF / MODEL_CREDITS.md

pedrodev2026

Create MODEL_CREDITS.md

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Model Credits - Microcoder-1.5B

Base Model

This fine-tuned model is built upon Qwen 2.5 Coder 1.5B Instruct, created and maintained by Alibaba Cloud.

Original Model Information

Model Name: Qwen 2.5 Coder 1.5B Instruct
Creator: Alibaba Cloud
Repository: Qwen Hugging Face
License: Apache 2.0

The Qwen 2.5 Coder series represents a significant advancement in code generation models, optimized for programming tasks and instruction following.

Model Redistribution

We acknowledge Unsloth for their role in redistributing and optimizing the base model, making it more accessible to the community.

Organization: Unsloth
Website: Unsloth.ai

Fine-Tuned Model (Microcoder-1.5B)

License: BSD-3-Clause
Status: This fine-tuned version incorporates specialized training and optimizations

License Summary

Component	License
Base Model (Qwen 2.5 Coder 1.5B)	Apache 2.0
Fine-tuned Model (Microcoder-1.5B)	BSD-3-Clause

Dataset Credits

For detailed information about the datasets used in the fine-tuning process, please refer to DATASET_CREDITS.md.

Attribution

When using Microcoder-1.5B, please provide appropriate attribution to:

Alibaba Cloud - for the original Qwen 2.5 Coder model
Unsloth - for model redistribution and optimization
Microcoder Contributors - for the fine-tuning and improvements

Citation

If you use this model in your research or projects, please consider citing:

@misc{microcoder2026,
  title={Microcoder-1.5B: A Fine-tuned Code Generation Model},
  author={[pedrodev2026]},
  year={2026},
  url={[https://huggingface.co/pedrodev2026/microcoder-1.5b]}
}

And also cite the original Qwen model:

@article{hui2024qwen2,
      title={Qwen2.5-Coder Technical Report},
      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},
      journal={arXiv preprint arXiv:2409.12186},
      year={2024}
}

Last Updated: 2026 Model Version: 1.5B