LlamaFactory and LLM-Finetuning

LlamaFactory is a comprehensive fine-tuning framework that abstracts away lower-level details, while LLM-Finetuning is a direct implementation using PEFT (Parameter-Efficient Fine-Tuning) as the core library—making them competitors offering different levels of abstraction for the same task.

LlamaFactory
67
Established
LLM-Finetuning
45
Emerging
Maintenance 20/25
Adoption 10/25
Maturity 16/25
Community 21/25
Maintenance 2/25
Adoption 10/25
Maturity 8/25
Community 25/25
Stars: 68,347
Forks: 8,346
Downloads:
Commits (30d): 21
Language: Python
License: Apache-2.0
Stars: 2,827
Forks: 725
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
No Package No Dependents
No License Stale 6m No Package No Dependents

About LlamaFactory

hiyouga/LlamaFactory

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

This tool helps researchers, data scientists, and ML engineers customize large language models for specific tasks. You input an existing large language model and your own specialized dataset, and it outputs a fine-tuned model that performs better on your unique data or problem. It's designed for anyone who needs to adapt powerful AI models without deep programming.

AI-model-customization natural-language-processing computational-linguistics machine-learning-engineering multimodal-AI

About LLM-Finetuning

ashishpatel26/LLM-Finetuning

LLM Finetuning with peft

This project helps machine learning engineers and researchers adapt large language models (LLMs) like Llama 2, Falcon, or GPT-Neo-X to perform specific tasks using their own custom datasets. You provide an existing LLM and your unique text data, and it outputs a specialized version of that model ready for tasks such as answering domain-specific questions, generating tailored text, or improving chatbot performance. This is for professionals who need to customize powerful AI models without starting from scratch.

large-language-models natural-language-processing AI-model-customization machine-learning-engineering

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