GitHub - unslothai/unsloth: 5X faster 50% less memory LLM finetuning

slowllama

Fine-tune Llama2 and CodeLLama models, including 70B/35B on Apple M1/M2 devices (for example, Macbook Air or Mac Mini) or consumer nVidia GPUs.

slowllama is not using any quantization. Instead, it offloads parts of model to SSD or main memory on both forward/backward passes. In contrast with training large models from scratch (unattainable... See more

GitHub - arthur-ai/bench: A tool for evaluating LLMs

TL;DR

LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.

LLMLingua: Compressing Prompts for Accelerated Inference of La

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⚡ LitGPT

Pretrain, finetune, evaluate, and deploy 20+ LLMs on your own data

Uses the latest state-of-the-art techniques:

✅ flash attention ✅ fp4/8/16/32 ✅ LoRA, QLoRA, Adapter (v1, v2) ✅ FSDP ✅ 1-1000+ GPUs/TPUs

Lightning AI • Models • Quick start • Inference • Finetune • Pretrain • Deploy • Features • Training recipes (YAML)

Finetune, pretrain and d... See more

LLMTuner

LLMTuner: Fine-Tune Llama, Whisper, and other LLMs with best practices like LoRA, QLoRA, through a sleek, scikit-learn-inspired interface.

Fine-Tuning for LLM Research by AI Hero

This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples pr... See more

promptfoo is a tool for testing and evaluating LLM output quality.

With promptfoo, you can:

Systematically test prompts & models against predefined test cases

Evaluate quality and catch regressions by comparing LLM outputs side-by-side

Speed up evaluations with caching and concurrency

Score outputs automatically by defining test cases

Use as a

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