Finetuning meta-llama/Llama-2-7b-hf for Text Generation Summarization
Llama-2-7b
Welcome to the finetuning process for meta-llama/Llama-2-7b-hf, a powerful language model designed for text generation and summarization tasks. By following the instructions below, you can fine-tune the model on your own dataset and harness its capabilities for your specific needs.
Installation To begin, ensure you have the autotrain-advanced package installed in your Python environment:
pip install autotrain-advanced
Update your torch installation:
autotrain setup --update-torch
Fine-tuning the Llama-2-7b Model To start the fine-tuning process, execute the following command, adjusting the parameters to your dataset and requirements:
autotrain llm --train --project_name my-llama --model meta-llama/Llama-2-7b-hf --data_path . --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 2 --trainer sft --push_to_hub --repo_id Sakil/meta_llama_2finetuned
Make sure to replace my-llama with your desired project name and configure the data path, learning rate, batch size, and number of training epochs as needed.
refrence
https://about.fb.com/news/2023/07/llama-2/
https://huggingface.co/docs/transformers/main/model_doc/llama2