Instructions to use KenSensei/Qwen2.5-Coder-3B-High with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KenSensei/Qwen2.5-Coder-3B-High with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KenSensei/Qwen2.5-Coder-3B-High",
	filename="Qwen2.5-Coder-3B-High.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use KenSensei/Qwen2.5-Coder-3B-High with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf KenSensei/Qwen2.5-Coder-3B-High: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 KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KenSensei/Qwen2.5-Coder-3B-High: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 KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M

Use Docker

docker model run hf.co/KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M

LM Studio
Jan
Ollama
How to use KenSensei/Qwen2.5-Coder-3B-High with Ollama:
```
ollama run hf.co/KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
```

Unsloth Studio new

How to use KenSensei/Qwen2.5-Coder-3B-High 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 KenSensei/Qwen2.5-Coder-3B-High 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 KenSensei/Qwen2.5-Coder-3B-High to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KenSensei/Qwen2.5-Coder-3B-High to start chatting

Pi new

How to use KenSensei/Qwen2.5-Coder-3B-High with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf KenSensei/Qwen2.5-Coder-3B-High: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": "KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KenSensei/Qwen2.5-Coder-3B-High with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf KenSensei/Qwen2.5-Coder-3B-High: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 KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use KenSensei/Qwen2.5-Coder-3B-High with Docker Model Runner:
```
docker model run hf.co/KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M
```

Lemonade

How to use KenSensei/Qwen2.5-Coder-3B-High with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KenSensei/Qwen2.5-Coder-3B-High:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-3B-High-Q4_K_M

List all available models

lemonade list

Qwen2.5-Coder-3B-High

Fine-tuned version of Qwen2.5-Coder-3B optimized specifically for Python programming tasks. Outperforms the base model on Python-related problems, code generation, and real-world development scenarios.

🚀 Overview

This repository hosts a fine-tuned variant of Qwen2.5-Coder-3B, trained on a high-quality dataset of Python programming problems, coding challenges, and real-world software engineering examples. The fine-tuning process significantly enhances the model’s ability to understand and generate idiomatic, efficient, and correct Python code.

✅ Key Improvements Over Base Model:

Higher accuracy on Python syntax, standard library usage, and common frameworks (e.g., Pandas, NumPy, asyncio)
Better code completion and function generation from natural language prompts
Improved reasoning for algorithmic problems (e.g., sorting, recursion, data structures)
More consistent and readable output formatting

📦 Model Files (GGUF Format)

All models are provided in GGUF format for broad compatibility with inference engines like llama.cpp, Ollama, LM Studio, and more.

Filename	Quantization	Size	Recommended Use Case
`Qwen2.5-Coder-3B-High.F16.gguf`	Float16	~6.2 GB	Maximum quality (GPU)
`Qwen2.5-Coder-3B-High.Q8_0.gguf`	Q8_0	~3.3 GB	High quality, CPU/GPU
`Qwen2.5-Coder-3B-High.Q5_K_M.gguf`	Q5_K_M	~2.2 GB	Balanced speed/quality
`Qwen2.5-Coder-3B-High.Q4_K_M.gguf`	Q4_K_M	~1.9 GB	Fast inference, low RAM

💡 Recommendation: Start with Q5_K_M for most local development tasks.

📈 Performance

Evaluated on an internal benchmark of 200 Python-specific prompts (including LeetCode-style problems, docstring-to-code, bug fixes, and library usage):

Metric	Base Qwen2.5-Coder-3B	Qwen2.5-Coder-3B-High
Code Correctness (Pass@1)	68%	84%
Syntax Validity	92%	98%
Library Usage Accuracy	71%	89%
Readability (Human Eval)	3.8 / 5	4.5 / 5

🔍 Benchmark details available upon request.

🛠️ Usage Examples

With `llama.cpp`

./main -m ./models/Qwen2.5-Coder-3B-High.Q5_K_M.gguf \
       -p "Write a Python function that takes a list of integers and returns the sum of even numbers." \
       -n 256 --temp 0.2

Downloads last month: 57

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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