OptimalScale/LMFlow

Health v2 · maintenance only

Adoption not part of the score

8484 stars · 823 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LMFlow is an extensible Python toolkit for finetuning and inference of large foundation models such as LLaMA, GPT-2, and Galactica. It provides lightweight LoRA-based tuning, task tuning, and inference pipelines designed to be user-friendly and efficient.

Use cases

• finetune a large language model on my own dataset
• run LoRA finetuning on LLaMA with limited GPU memory
• adapt a pretrained model to a domain like medicine or math
• run inference with a finetuned 7B or 33B model
• instruction-tune an open-source language model
• train a chatbot model comparable to ChatGPT on a small budget

When to choose

• you want a simple, extensible pipeline for finetuning and inference of large foundation models
• you need memory-efficient tuning methods like LoRA or LISA
• you want an open-source full pipeline covering data, tuning, and inference

When to avoid

• you only need to call hosted LLM APIs without any local training
• you need a production serving stack with autoscaling rather than a research toolkit
• you require non-PyTorch training backends

Facets

library · maturity active

llm-training llm-inference machine-learning deep-learning large-language-models deep-learning machine-learning artificial-intelligence python cross-platform finetuning lora transformers pytorch instruction-tuning foundation-models lisa gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OptimalScale/LMFlow")