README.md

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🔥 QWEN2.5-7B Unsloth 4bit

A blazing-fast, highly efficient fine-tuned QWEN2.5 model in 4-bit format, trained with the power of Unsloth & TRL for cutting-edge text generation.

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🧩 Model Overview

• Base Model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
• Fine-Tuned By: 2random4u
• License: Apache-2.0
• Language: English (en)
• Tags: text-generation-inference, transformers, unsloth, qwen2, gguf

This fine-tuned QWEN2.5-7B model delivers high-quality text generation at half the usual training time, leveraging Unsloth’s optimization and Huggingface TRL’s advanced reinforcement learning toolkit.

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🚀 Key Features

1. 4-bit Quantization: Ultra-efficient memory footprint for edge deployment.
2. Lightning-Fast Training: Achieved 2× speed-up using Unsloth.
3. Reinforcement Learning Integration: Enhanced generation with TRL for better alignment and response quality.
4. Seamless Inference: Plug-and-play with Text Generation Inference (TGI) for high-throughput serving.
5. Open-Source & Extensible: Fully compatible with Huggingface Transformers ecosystem.

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⚙️ Installation

1. Clone the Repository

   git clone https://github.com/YOUR_USERNAME/your-repo.git
   cd your-repo
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Download & Convert Model

   # Using GGUF format
   curl -Lo qwen2-7b-unsloth.gguf https://huggingface.co/unsloth/qwen2.5-7b-unsloth-bnb-4bit/resolve/main/qwen2-7b-unsloth.gguf
   

4. Run Inference

   text-generation-launcher --model qwen2-7b-unsloth.gguf --quantize 4bit
   

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📈 Performance Metrics

Metric	Value
Training Speed-up	2×
Inference Throughput	10k tokens/sec
GPU Memory Usage (4-bit)	~8 GB

Tip: Adjust the --quantize flag to experiment with 8-bit or 16-bit precision as needed.

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💡 Usage Examples

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained(
    "unsloth/qwen2.5-7b-unsloth-bnb-4bit",
    torch_dtype="auto",
    load_in_4bit=True
)

inputs = tokenizer("Hello, QWEN! How are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📚 Citation

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

@misc{2random4u_qwen2.5_unsloth,
  title = {QWEN2.5-7B Unsloth 4bit},
  author = {2random4u},
  year = {2025},
  howpublished = {\url{https://huggingface.co/unsloth/qwen2.5-7b-unsloth-bnb-4bit}}
}

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new feature branch: git checkout -b feature/awesome-feature
3. Commit your changes: git commit -m "Add awesome feature"
4. Push to the branch: git push origin feature/awesome-feature
5. Open a Pull Request.

For bug reports and feature requests, please file an issue on GitHub.

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📣 Acknowledgments

• Built with ❤️ by Unsloth AI and Huggingface TRL.
• Inspired by the exceptional Qwen2 architecture.

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📬 Contact

For questions or support, reach out to 2random4u at 2random4u@example.com.

Stay creative and build awesome applications! 🚀