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LLM-FineTuning-Large-Language-Models

LLM (Large Language Model) FineTuning

TutorialsLLM fundamentalsJupyter Notebook
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创建于 2023-10-22 · 更新于 2026-09-09 · 今日第 16681 名
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rohan-paul/LLM-FineTuning-Large-Language-Models 排名第 16,681，有 578 个星标。

同类别比较

rohan-paul/LLM-FineTuning-Large-Language-Models 排名第 16,681，此处有 8 个同类条目；zjhellofss/KuiperLLama 排名第 16,684。

同类别比较
名称排名指标
the-ai-merge/multimodal-agents-course16,632
语言:
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星标数:
581
分叉数:
149
简介:
An MCP Multimodal AI Agent with eyes and ears!
MazzaWill/neo4j-python-pandas-py2neo-v316,640
语言:
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Excel-to-Neo4j knowledge graph examples: legacy py2neo v3 plus modern Neo4j GraphRAG/vector search.
claudio-silva/claude-artifact-runner16,644
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ukairia777/tensorflow-nlp-tutorial16,650
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zjhellofss/KuiperLLama16,684
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578
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校招、秋招、春招、实习好项目，带你从零动手实现支持LLama2/3和Qwen2.5的大模型推理框架。
persistforever/cifar10-tensorflow16,699
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zsdonghao/deep-learning-book16,743
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24kchengYe/human-skill-tree16,774
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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别LLM fundamentals
关键词Finetuning
星标数578
分叉数137
贡献者2
创建日期2023-10-22
更新日期2026-09-09
1 天星标数0
7 天星标数0
排名16,681
上次排名16,470
排名变化-211
7 天星标排名15,664
上次 7 天星标排名15,671
7 天星标排名变化7
总星标排名17,288
上次总星标排名17,261
总星标排名变化-27
国家India
README 摘录

LLM (Large Language Models) FineTuning Projects and notes on common practical techniques Find me in Twitter 📚 I write daily for my 112K+ readers on actionable AI developments. | | 📌 Why we need to identify 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 ?

常见问题

记录了多少星标和分叉？

rohan-paul/LLM-FineTuning-Large-Language-Models 有 578 个星标和 137 个分叉。

仓库排名是多少？

rohan-paul/LLM-FineTuning-Large-Language-Models 排名第 16,681。

记录的语言和类别是什么？

rohan-paul/LLM-FineTuning-Large-Language-Models 有 578 个星标，语言为 Jupyter Notebook，类别为 Tutorials。

哪个仓库排名最接近？

rohan-paul/LLM-FineTuning-Large-Language-Models 与 zjhellofss/KuiperLLama 比较，后者排名第 16,684，有 578 个星标。

README

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

Find me in Twitter

📚 I write daily for my 112K+ readers on actionable AI developments. Get a 1300+ page Python book as soon as you subscribing (its FREE) ↓↓)

 

Fine-tuning LLM (and YouTube Video Explanations)

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

Fine-tuning LLM

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

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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