An Introductory Guide to Fine-Tuning LLMs

https://www.datacamp.com/tutorial/fine-tuning-large-language-models

Fine-tuning Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), offering unprecedented capabilities in tasks like language translation, sentiment analysis, and text generation. This transformative approach leverages pre-trained models like GPT-2, enhancing their performance on specific domains through the fine-tuning process.

 

Over the last year and a half, the field of natural language processing (NLP) has undergone a significant transformation due to the popularization of Large Language Models (LLMs). The natural language skills that these models present have allowed applications that seemed impossible to achieve a few years ago.

LLMs are pushing the boundaries of what was previously considered achievable with capabilities ranging from language translation to sentiment analysis and text generation.

However, we all know training such models is time-consuming and expensive. This is why, fine-tuning large language models is important for tailoring these advanced algorithms to specific tasks or domains.

This process enhances the model's performance on specialized tasks and significantly broadens its applicability across various fields. This means we can take advantage of the Natural Language Processing capacity of pre-trained LLMs and further train them to perform our specific tasks.

Today, explore the essence of pre-trained language models and further delve into the fine-tuning process.

So, let’s navigate through practical steps for fine-tuning a model like GPT-2 using Hugging Face.

 

Fine-tuning vs. RAG

RAG combines the strengths of retrieval-based models and generative models. In RAG, a retriever component searches a large database or knowledge base to find relevant information based on the input query. This retrieved information is then used by a generative model to produce a more accurate and contextually relevant response. Key benefits of RAG include:

• Dynamic knowledge integration: Incorporates real-time information from external sources, making it suitable for tasks requiring up-to-date or specific knowledge.
• Contextual relevance: Enhances the generative model’s responses by providing additional context from the retrieved documents.
• Versatility: Can handle a wider range of queries, including those requiring specific or rare information that the model may not have been trained on.

Choosing between fine-tuning and RAG

When deciding whether to use fine-tuning or RAG, consider the following factors:

• Nature of the task: For tasks that benefit from highly specialized models (e.g., domain-specific applications), fine-tuning is often the preferred approach. RAG is ideal for tasks that require integration of external knowledge or real-time information retrieval.
• Data availability: Fine-tuning requires a substantial amount of labeled data specific to the task. If such data is scarce, RAG’s retrieval component can compensate by providing relevant information from external sources.
• Resource constraints: Fine-tuning can be computationally intensive, whereas RAG leverages existing databases to supplement the generative model, potentially reducing the need for extensive training.

微调框架

moreh

https://docs.moreh.io/tutorials/

Fine-tuning Tutorials

This tutorial is for anyone who wants to fine-tune powerful large language models such as Llama2, Mistral for their own projects. We will walk you through the steps to fine-tune these large language models (LLMs) with MoAI Platform.

• Llama3 8B
• Llama3 70B
• Mistral
• GPT
• Qwen
• Baichuan2
• Llama2 13B

Fine-tuning in machine learning involves adjusting a pre-trained machine learning model's weight on new data to enhance task-specific performance. Essentially, when you want to apply an AI model to a new task, you take an existing model and optimize it with new datasets. This allows you to customize the model to meet your specific needs and domain requirements.

A pre-trained model has a large number of parameters designed for general-purpose use, and effectively fine-tuning such a large model requires a sufficient amount of training data.

With the MoAI Platform, you can easily apply optimized parallelization techniques that consider the GPU's memory size, significantly reducing the time and effort needed before starting training.

#What you will learn here:

1. Loading datasets, models, and tokenizers
2. Running training and checking results
3. Applying automatic parallelization
4. Choosing the right training environment and AI accelerators

LLaMA-Factory

https://github.com/hiyouga/LLaMA-Factory

Features

• Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
• Integrated methods: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
• Scalable resources: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
• Advanced algorithms: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
• Practical tricks: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
• Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
• Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker.

swift

https://github.com/modelscope/swift

SWIFT supports training(PreTraining/Fine-tuning/RLHF), inference, evaluation and deployment of 300+ LLMs and 50+ MLLMs (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by PEFT, we also provide a complete Adapters library to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts.

To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. SWIFT web-ui is available both on Huggingface space and ModelScope studio, please feel free to try!

SWIFT has rich documentations for users, please feel free to check our documentation website:

xtuner

https://github.com/InternLM/xtuner

https://xtuner.readthedocs.io/zh-cn/latest/training/multi_modal_dataset.html

XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models.

Efficient

• Support LLM, VLM pre-training / fine-tuning on almost all GPUs. XTuner is capable of fine-tuning 7B LLM on a single 8GB GPU, as well as multi-node fine-tuning of models exceeding 70B.
• Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput.
• Compatible with DeepSpeed
  标签：tuning,Qwen,LLMs,fine,Introductory,--,models,model,Fine
  From： https://www.cnblogs.com/lightsong/p/18340179