How ashishpatel26/llm-finetuning works
This repository is a collection of independently runnable notebooks, not a single application pipeline. Users choose a notebook to fine-tune or optimize a model, build a chatbot or retrieval/knowledge-graph workflow, or evaluate a model and generation strategy. The groups reflect those documented product areas; each notebook is represented as its own workflow. The README excerpt and filenames establish these capabilities, but no notebook source was available to verify internal calls, data flow, or integrations.
Main entry points: 1.Efficiently_train_Large_Language_Models_with_LoRA_and_Hugging_Face.ipynb LoRA training · 2.Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.ipynb Llama 2 tuning · 3.Guanaco Chatbot Demo with LLaMA-7B Model.ipynb Guanaco chatbot · 4.PEFT Finetune-Bloom-560m-tagger.ipynb Bloom tagger
Components 24
Model fine-tuning
- LoRA training
1.Efficiently_train_Large_Language_Models_with_LoRA_and_Hugging_Face.ipynb - Llama 2 tuning
2.Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.ipynb - Guanaco chatbot
3.Guanaco Chatbot Demo with LLaMA-7B Model.ipynb - Bloom tagger
4.PEFT Finetune-Bloom-560m-tagger.ipynb - OPT tuning
5.Finetune_Meta_OPT-6-1b_Model_bnb_peft.ipynb - Falcon training
6.Finetune Falcon-7b with BNB Self Supervised Training.ipynb - QLoRA tuning
7.FineTune_LLAMA2_with_QLORA.ipynb - Vicuna tuning
8.Stable_Vicuna13B_8bit_in_Colab.ipynb - GPT-NeoX training
9.GPT-neo-x-20B-bnb_4bit_training.ipynb - MPT instruction tuning
10.MPT_Instruct_30B.ipynb - RLHF training
11_RLHF_Training_for_CustomDataset_for_AnyModel.ipynb - Phi SFT
12_Fine_tuning_Microsoft_Phi_1_5b_on_custom_dataset(dialogstudio).ipynb - GPT-3.5 tuning
13.Fine_tuning_OpenAI_GPT_3_5_turbo.ipynb - Mistral AutoTrain
14.Finetuning_Mistral_7b_Using_AutoTrain.ipynb
Model optimization
- HQQ quantization
19.HQQ_1bit_ipynb.ipynb - Gemma Unsloth tuning
20.Alpaca_+_Gemma2_9b_Unsloth_2x_faster_finetuning.ipynb - Cache-augmented generation
23_CAG_Optimizing_LLMs_with_cache_augmented_generation.ipynb
Retrieval and knowledge
- LangChain RAG
15.RAG_LangChain.ipynb - Neo4j question answering
16.Neo4j_and_LangChain_for_Enhanced_Question_Answering.ipynb - Graph question answering
17.OpenAI_Constructing_Graph_for_Questio_Answer.ipynb - Document knowledge graph
18.Convert_Document_to_Knowledge_Graph_Langchain_Openai.ipynb
Evaluation
- RAG evaluation
21_RAG_Pipeline_Evaluation_Using_MLFLOW_Best_Industry_Practise.ipynb - Hugging Face evaluation
22.Evaluate_a_Hugging_Face_LLM_with_mlflow_evaluate.ipynb
Other components
- Notebook user
Connections 23
- Notebook user → LoRA training: runs
- Notebook user → Llama 2 tuning: runs
- Notebook user → Guanaco chatbot: runs
- Notebook user → Bloom tagger: runs
- Notebook user → OPT tuning: runs
- Notebook user → Falcon training: runs
- Notebook user → QLoRA tuning: runs
- Notebook user → Vicuna tuning: runs
- Notebook user → GPT-NeoX training: runs
- Notebook user → MPT instruction tuning: runs
- Notebook user → RLHF training: runs
- Notebook user → Phi SFT: runs
- Notebook user → GPT-3.5 tuning: runs
- Notebook user → Mistral AutoTrain: runs
- Notebook user → HQQ quantization: runs
- Notebook user → Gemma Unsloth tuning: runs
- Notebook user → Cache-augmented generation: runs
- Notebook user → LangChain RAG: runs
- Notebook user → Neo4j question answering: runs
- Notebook user → Graph question answering: runs
- Notebook user → Document knowledge graph: runs
- Notebook user → RAG evaluation: runs
- Notebook user → Hugging Face evaluation: runs