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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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## Overview |
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The **Zephyr 7B** model is a cutting-edge language model designed for robust natural language understanding and generation. It is suitable for a variety of NLP tasks, such as text generation, summarization, translation, and question answering. This repository hosts both the standard and quantized versions of the Zephyr 7B model, allowing users to choose the version that best fits their resource constraints and performance needs. |
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## Model Details |
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- **Model Name:** Zephyr 7B |
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- **Model Size:** 7 billion parameters |
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- **Architecture:** Transformer-based |
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- **Languages:** Primarily English, with support for multilingual text |
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- **Quantized Version:** Available for reduced memory footprint and faster inference |
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## Usage |
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### Installation |
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To use the Zephyr 7B model, install the `transformers` library: |
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```bash |
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pip install transformers |
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``` |
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### Loading the Model |
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Load the model using the `transformers` library as follows: |
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#### Standard Version |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "ekshat/zephyr_7b_q4_k_m" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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``` |
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#### Quantized Version |
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Using Ollama, .gguf model can be used on local system. For more details, refer [Ollama](https://github.com/ollama/ollama) |
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### Generating Text |
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Generate text with the Zephyr 7B model: |
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```python |
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input_text = "Once upon a time" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(inputs.input_ids, max_length=50) |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Performance and Efficiency |
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The quantized version of Zephyr 7B is optimized for environments with limited computational resources. It offers: |
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- **Reduced Memory Usage:** The model size is significantly smaller, making it suitable for deployment on devices with limited RAM. |
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- **Faster Inference:** Quantized models can perform faster inference, providing quicker responses in real-time applications. |
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## Fine-Tuning |
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You can fine-tune the Zephyr 7B model on your own dataset to better suit specific tasks or domains. Refer to the [Huggingface documentation](https://huggingface.co/transformers/training.html) for guidance on how to fine-tune transformer models. |
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## Contributing |
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We welcome contributions to improve the Zephyr 7B model. Please submit pull requests or open issues for any enhancements or bugs you encounter. |
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## License |
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This model is licensed under the [MIT License](LICENSE). |
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## Acknowledgments |
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Special thanks to the Huggingface team for providing the `transformers` library and to the broader AI community for their continuous support and contributions. |
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## Contact |
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For any questions or inquiries, please contact us at (mailto:akshayhedaoo7246@gmail.com). |
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--- |
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We hope you find the Zephyr 7B model useful for your NLP tasks. Happy coding! |
