base on LLM Finetuning with peft # LLM-Finetuning # PEFT Fine-Tuning Project 🚀 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.  ## Fine Tuning Notebook Table 📑 | 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. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/1.Efficiently_train_Large_Language_Models_with_LoRA_and_Hugging_Face.ipynb) | | **2. Fine-Tune Your Own Llama 2 Model in a Colab Notebook** | Guide to fine-tuning your Llama 2 model using Colab. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/2.Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.ipynb) | | **3. Guanaco Chatbot Demo with LLaMA-7B Model** | Showcase of a chatbot demo powered by LLaMA-7B model. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/3.Guanaco%20Chatbot%20Demo%20with%20LLaMA-7B%20Model.ipynb) | | **4. PEFT Finetune-Bloom-560m-tagger** | Project details for PEFT Finetune-Bloom-560m-tagger. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/4.PEFT%20Finetune-Bloom-560m-tagger.ipynb#scrollTo=MDqJWba-tpnv) | | **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. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/5.Finetune_Meta_OPT-6-1b_Model_bnb_peft.ipynb) | | **6.Finetune Falcon-7b with BNB Self Supervised Training** | Guide for finetuning Falcon-7b using BNB self-supervised training. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/6.Finetune%20Falcon-7b%20with%20BNB%20Self%20Supervised%20Training.ipynb) | | **7.FineTune LLaMa2 with QLoRa** | Guide to fine-tune the Llama 2 7B pre-trained model using the PEFT library and QLoRa method | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/7.FineTune_LLAMA2_with_QLORA.ipynb) | | **8.Stable_Vicuna13B_8bit_in_Colab** | Guide of Fine Tuning Vecuna 13B_8bit | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/8.Stable_Vicuna13B_8bit_in_Colab.ipynb) | | **9. GPT-Neo-X-20B-bnb2bit_training** | Guide How to train the GPT-NeoX-20B model using bfloat16 precision | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/9.GPT-neo-x-20B-bnb_4bit_training.ipynb) | | **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. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/10.MPT_Instruct_30B.ipynb) | | **11.RLHF_Training_for_CustomDataset_for_AnyModel** | How train a Model with RLHF training on any LLM model with custom dataset | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/11_RLHF_Training_for_CustomDataset_for_AnyModel.ipynb) | | **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 | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/12_Fine_tuning_Microsoft_Phi_1_5b_on_custom_dataset(dialogstudio).ipynb) | | **13. Finetuning OpenAI GPT3.5 Turbo** | How to finetune GPT 3.5 on your own data | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/13.Fine_tuning_OpenAI_GPT_3_5_turbo.ipynb) | | **14. Finetuning Mistral-7b FineTuning Model using Autotrain-advanced** | How to finetune Mistral-7b using autotrained-advanced | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/14.Finetuning_Mistral_7b_Using_AutoTrain.ipynb) | | **15. RAG LangChain Tutorial** | How to Use RAG using LangChain | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/15.RAG_LangChain.ipynb) | | **16. Knowledge Graph LLM with LangChain PDF Question Answering** | How to build knowledge graph with pdf question answering | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/16.Neo4j_and_LangChain_for_Enhanced_Question_Answering.ipynb) | | **17. Text to Knolwedge Graph with OpenAI Function with Neo4j and Langchain Agent Question Answering** | How to build knowledge graph from text or Pdf Document with pdf question Answering | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/17.OpenAI_Constructing_Graph_for_Questio_Answer.ipynb) | | **18. Convert the Document to Knowledgegraph using Langchain and Openai** | This notebook is help you to understand how easiest way you can convert your any documents into Knowledgegraph for your next RAG based Application | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/18.Convert_Document_to_Knowledge_Graph_Langchain_Openai.ipynb) | | **19. How to train a 1-bit Model with LLMs?** | This notebook is help you to train a model with 1-bit and 2-bit quantization method using hqq framework | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/19.HQQ_1bit_ipynb.ipynb) | | **20.Alpaca_+_Gemma2_9b_Unsloth_2x_faster_finetuning** | This notebook is help you to train a model with gemma2 9b | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/20.Alpaca_%2B_Gemma2_9b_Unsloth_2x_faster_finetuning.ipynb) | | **21.RAG Pipeline Evaluation Using MLFLOW Best Industry Practice** | This notebook provides a comprehensive guide to evaluating the 21 RAG (Retrieve-then-Answer Generation) pipeline using MLFLOW, adhering to best industry practices. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/21_RAG_Pipeline_Evaluation_Using_MLFLOW_Best_Industry_Practise.ipynb) | | **22. Evaluate a Hugging Face LLM with `mlflow.evaluate()`** | This notebook provides a comprehensive guide on evaluating a Hugging Face Language Learning Model (LLM) using mlflow_evaluate. | [](https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/22.Evaluate_a_Hugging_Face_LLM_with_mlflow_evaluate.ipynb) | ## Contributing 🤝 Contributions are welcome! If you'd like to contribute to this project, feel free to open an issue or submit a pull request. ## License 📝 This project is licensed under the [MIT License](LICENSE). --- Created with ❤️ by [Ashish](https://github.com/ashishpatel26/) ", Assign "at most 3 tags" to the expected json: {"id":"7774","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"