https://github.com/shaadclt/textgeneration-llama3-huggingface
This repository demonstrates how to leverage the Llama3 large language model from Meta for text generation tasks using Hugging Face Transformers in a Jupyter Notebook environment.
https://github.com/shaadclt/textgeneration-llama3-huggingface
Last synced: 7 months ago
JSON representation
This repository demonstrates how to leverage the Llama3 large language model from Meta for text generation tasks using Hugging Face Transformers in a Jupyter Notebook environment.
- Host: GitHub
- URL: https://github.com/shaadclt/textgeneration-llama3-huggingface
- Owner: shaadclt
- Created: 2024-04-21T06:24:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-22T16:24:00.000Z (over 1 year ago)
- Last Synced: 2025-02-02T09:41:23.447Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 22.5 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Exploring Llama3 Text Generation with Hugging Face Transformers (Jupyter Notebook)
This repository demonstrates how to leverage the LLama3 large language model from Meta for text generation tasks using Hugging Face Transformers in a Jupyter Notebook environment.
## Requirements:
1. Python 3.7+
2. transformers library (pip install transformers)
3. torch library (pip install torch)
4. accelerate library (pip install accelerate)
5. A Hugging Face Hub account (optional, for accessing different Llama3 models)
## Instructions:
1. Clone this repository.
```bash
git clone https://github.com/shaadclt/Llama3-HuggingFace.git
```
2. Open **llama3_huggingface.ipynb** in a Jupyter Notebook environment.
3. Run the notebook cells to experiment with text generation prompts using LLaMa3.
## Additional Notes:
- This example uses the meta-llama/Meta-Llama-3-8B model from Hugging Face Hub. Refer to the Hugging Face Hub for a listing of available Llama3 models and their access requirements.
- The code includes optional arguments for max_length (controlling the maximum length of the generated text) and num_return_sequences (specifying the number of generated text samples to return).
## Further Exploration:
- Experiment with different text prompts to explore LLaMa3's capabilities in various creative and informative text generation tasks.
- Consider fine-tuning the model on a specific dataset for tailored performance.
- Refer to the Hugging Face Transformers documentation for more advanced usage and customization options.