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Link to original content: http://github.com/rohan-paul/LLM-FineTuning-Large-Language-Models
GitHub - rohan-paul/LLM-FineTuning-Large-Language-Models: LLM (Large Language Model) FineTuning
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Finetune_llama_2_GPTQ
Finetune_llama_2_GPTQ
 
 
LLM_Techniques_and_utils
LLM_Techniques_and_utils
 
 
Mixtral_Chatbot_with_Gradio
Mixtral_Chatbot_with_Gradio
 
 
Other-Language_Models_BERT_related
Other-Language_Models_BERT_related
 
 
Quantize_with_HF_transformers
Quantize_with_HF_transformers
 
 
assets
assets
 
 
gemma-2b_ORPO_FineTuning_full_precision
gemma-2b_ORPO_FineTuning_full_precision
 
 
.gitignore
.gitignore
 
 
CodeLLaMA_34B_Conversation_with_Streamlit.py
CodeLLaMA_34B_Conversation_with_Streamlit.py
 
 
Falcon-7B_FineTuning_with_PEFT_and_QLORA.ipynb
Falcon-7B_FineTuning_with_PEFT_and_QLORA.ipynb
 
 
FineTuning_phi-1_5_with_PRFT_LoRA.ipynb
FineTuning_phi-1_5_with_PRFT_LoRA.ipynb
 
 
Finetune_codellama-34B-with-QLoRA.ipynb
Finetune_codellama-34B-with-QLoRA.ipynb
 
 
Finetune_opt_bnb_peft.ipynb
Finetune_opt_bnb_peft.ipynb
 
 
Inference_Yarn-Llama-2-13b-128k_Github.ipynb
Inference_Yarn-Llama-2-13b-128k_Github.ipynb
 
 
Jamba_Finetuning_Colab-Pro.ipynb
Jamba_Finetuning_Colab-Pro.ipynb
 
 
LlaMa-2-FineTuning.ipynb
LlaMa-2-FineTuning.ipynb
 
 
Llama-3_Finetuning_on_custom_dataset_with_unsloth.ipynb
Llama-3_Finetuning_on_custom_dataset_with_unsloth.ipynb
 
 
Llama_3_Finetuning_ORPO_with_Unsloth.ipynb
Llama_3_Finetuning_ORPO_with_Unsloth.ipynb
 
 
Local-Inferencing_LlaMa-2.ipynb
Local-Inferencing_LlaMa-2.ipynb
 
 
Mistral-7B-Inferencing.ipynb
Mistral-7B-Inferencing.ipynb
 
 
Mistral_7B_Instruct_GPTQ_finetune.ipynb
Mistral_7B_Instruct_GPTQ_finetune.ipynb
 
 
Mistral_7b_FineTuning_with_DPO_Direct_Preference_Optimization.ipynb
Mistral_7b_FineTuning_with_DPO_Direct_Preference_Optimization.ipynb
 
 
Mistral_FineTuning_with_PEFT_and_QLORA.ipynb
Mistral_FineTuning_with_PEFT_and_QLORA.ipynb
 
 
Nous-Hermes-2-Yi-34B-GGUF_in_Kaggle_free_GPU_with_llama_cpp.ipynb
Nous-Hermes-2-Yi-34B-GGUF_in_Kaggle_free_GPU_with_llama_cpp.ipynb
 
 
README.md
README.md
 
 
TinyLlama_with_Unsloth_and_RoPE_Scaling_dolly-15k.ipynb
TinyLlama_with_Unsloth_and_RoPE_Scaling_dolly-15k.ipynb
 
 
TogetherAI_API_with_LangChain.ipynb
TogetherAI_API_with_LangChain.ipynb
 
 
Web_scraping_with_Large_Language_Models_LLM_AnthropicAI_LangChainAI.ipynb
Web_scraping_with_Large_Language_Models_LLM_AnthropicAI_LangChainAI.ipynb
 
 
enable_json_mode.ipynb
enable_json_mode.ipynb
 
 
layered_inference_with_airllm_70B_LLM_Inference_on_a_Single_4GB_GPU.ipynb
layered_inference_with_airllm_70B_LLM_Inference_on_a_Single_4GB_GPU.ipynb
 
 
tinyllama_fine-tuning_Taylor_Swift.ipynb
tinyllama_fine-tuning_Taylor_Swift.ipynb
 
 
togetherai-api-with_Mixtral.ipynb
togetherai-api-with_Mixtral.ipynb
 
 

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LLM (Large Language Models) FineTuning Projects and notes on common practical techniques

Find me here..

Fine-tuning LLM (and YouTube Video Explanations)

Notebook 🟠 YouTube Video
Finetune Llama-3-8B with unsloth 4bit quantized with ORPO Youtube Link
Llama-3 Finetuning on custom dataset with unsloth Youtube Link
CodeLLaMA-34B - Conversational Agent Youtube Link
Inference Yarn-Llama-2-13b-128k with KV Cache to answer quiz on very long textbook Youtube Link
Mistral 7B FineTuning with_PEFT and QLORA Youtube Link
Falcon finetuning on openassistant-guanaco Youtube Link
Fine Tuning Phi 1_5 with PEFT and QLoRA Youtube Link
Web scraping with Large Language Models (LLM)-AnthropicAI + LangChainAI Youtube Link

Fine-tuning LLM

Notebook Colab
📌 Gemma_2b_finetuning_ORPO_full_precision Open In Colab
📌 Jamba_Finetuning_Colab-Pro Open In Colab
📌 Finetune codellama-34B with QLoRA Open In Colab
📌 Mixtral Chatbot with Gradio
📌 togetherai api to run Mixtral Open In Colab
📌 Integrating TogetherAI with LangChain 🦙 Open In Colab
📌 Mistral-7B-Instruct_GPTQ - Finetune on finance-alpaca dataset 🦙 Open In Colab
📌 Mistral 7b FineTuning with DPO Direct_Preference_Optimization Open In Colab
📌 Finetune llama_2_GPTQ
📌 TinyLlama with Unsloth and_RoPE_Scaling dolly-15 dataset Open In Colab
📌 Tinyllama fine-tuning with Taylor_Swift Song lyrics Open In Colab

LLM Techniques and utils - Explained

LLM Concepts
📌 DPO (Direct Preference Optimization) training and its datasets
📌 4-bit LLM Quantization with GPTQ
📌 Quantize with HF Transformers
📌 Understanding rank r in LoRA and related Matrix_Math
📌 Rotary Embeddings (RopE) is one of the Fundamental Building Blocks of LlaMA-2 Implementation
📌 Chat Templates in HuggingFace
📌 How is Mixtral 8x7B is a dense 47Bn param model
📌 The concept of validation log perplexity in LLM training - a note on fundamentals.
📌 Why we need to identify target_layers for LoRA/QLoRA
📌 Evaluate Token per sec
📌 traversing through nested attributes (or sub-modules) of a PyTorch module
📌 Implementation of Sparse Mixtures-of-Experts layer in PyTorch from Mistral Official Repo
📌 Util method to extract a specific token's representation from the last hidden states of a transformer model.
📌 Convert PyTorch model's parameters and tensors to half-precision floating-point format
📌 Quantizing 🤗 Transformers models with the GPTQ method
📌 Quantize Mixtral-8x7B so it can run in 24GB GPU
📌 What is GGML or GGUF in the world of Large Language Models ?

Other Smaller Language Models

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LLM (Large Language Model) FineTuning

Topics

pytorch gpt-3 large-language-models llm llm-serving gpt3-turbo llm-training llm-inference open-source-llm llama2 llm-finetuning mistral-7b

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