transformers pytorch gguf mistral text-generation conversational en license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
## Overview
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.

## Model Details
- **Model Name:** Zephyr 7B
- **Model Size:** 7 billion parameters
- **Architecture:** Transformer-based
- **Languages:** Primarily English, with support for multilingual text
- **Quantized Version:** Available for reduced memory footprint and faster inference

## Usage

### Installation
To use the Zephyr 7B model, install the `transformers` library:

```bash
pip install transformers
```

### Loading the Model
Load the model using the `transformers` library as follows:

#### Standard Version
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ekshat/zephyr_7b_q4_k_m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```

#### Quantized Version
Using Ollama, .gguf model can be used on local system. For more details, refer [Ollama](https://github.com/ollama/ollama)

### Generating Text
Generate text with the Zephyr 7B model:

```python
input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)
```

## Performance and Efficiency
The quantized version of Zephyr 7B is optimized for environments with limited computational resources. It offers:
- **Reduced Memory Usage:** The model size is significantly smaller, making it suitable for deployment on devices with limited RAM.
- **Faster Inference:** Quantized models can perform faster inference, providing quicker responses in real-time applications.

## Fine-Tuning
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.

## Contributing
We welcome contributions to improve the Zephyr 7B model. Please submit pull requests or open issues for any enhancements or bugs you encounter.

## License
This model is licensed under the [MIT License](LICENSE).

## Acknowledgments
Special thanks to the Huggingface team for providing the `transformers` library and to the broader AI community for their continuous support and contributions.

## Contact
For any questions or inquiries, please contact us at (mailto:akshayhedaoo7246@gmail.com).

---

We hope you find the Zephyr 7B model useful for your NLP tasks. Happy coding!