Welcome to the PEFT (Pretraining-Evaluation Fine-Tuning) project repository! This project focuses on efficiently fine-tuning large language models using LoRA and Hugging Face's transformers library.
| Notebook Title | Description | Colab Badge |
|---|---|---|
| 1. Efficiently Train Large Language Models with LoRA and Hugging Face | Details and code for efficient training of large language models using LoRA and Hugging Face. |  |
| 2. Fine-Tune Your Own Llama 2 Model in a Colab Notebook | Guide to fine-tuning your Llama 2 model using Colab. | |
| 3. Guanaco Chatbot Demo with LLaMA-7B Model | Showcase of a chatbot demo powered by LLaMA-7B model. | |
| 4. PEFT Finetune-Bloom-560m-tagger | Project details for PEFT Finetune-Bloom-560m-tagger. | |
| 5. Finetune_Meta_OPT-6-1b_Model_bnb_peft | Details and guide for finetuning the Meta OPT-6-1b Model using PEFT and Bloom-560m-tagger. | |
| 6.Finetune Falcon-7b with BNB Self Supervised Training | Guide for finetuning Falcon-7b using BNB self-supervised training. | |
| 7.FineTune LLaMa2 with QLoRa | Guide to fine-tune the Llama 2 7B pre-trained model using the PEFT library and QLoRa method | |
| 8.Stable_Vicuna13B_8bit_in_Colab | Guide of Fine Tuning Vecuna 13B_8bit | |
| 9. GPT-Neo-X-20B-bnb2bit_training | Guide How to train the GPT-NeoX-20B model using bfloat16 precision | |
| 10. MPT-Instruct-30B Model Training | MPT-Instruct-30B is a large language model from MosaicML that is trained on a dataset of short-form instructions. It can be used to follow instructions, answer questions, and generate text. | |
| 11.RLHF_Training_for_CustomDataset_for_AnyModel | How train a Model with RLHF training on any LLM model with custom dataset | |
| 12.Fine_tuning_Microsoft_Phi_1_5b_on_custom_dataset(dialogstudio) | How train a model with trl SFT Training on Microsoft Phi 1.5 with custom | |
| 13. Finetuning OpenAI GPT3.5 Turbo | How to finetune GPT 3.5 on your own data | |
| 14. Finetuning Mistral-7b FineTuning Model using Autotrain-advanced | How to finetune Mistral-7b using autotrained-advanced | |
Contributions are welcome! If you'd like to contribute to this project, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.
Created with ❤️ by Ashish