Hackable implementation of state-of-the-art open-source large language models released under the Apache 2.0 license.
Supports the following popular model checkpoints:
| Model | Model size | Reference |
|---|---|---|
| Code Llama by Meta AI | 7B, 13B, 34B, 70B | Rozière et al. 2023 |
| Dolly by Databricks | 3B, 7B, 12B | Conover et al. 2023 |
| Falcon by TII UAE | 7B, 40B, 180B | TII 2023 |
| FreeWilly2 (Stable Beluga 2) by Stability AI | 70B | Stability AI 2023 |
| Function Calling Llama 2 by Trelis | 7B | Trelis et al. 2023 |
| Gemma by Google | 2B, 7B | Google Team, Google Deepmind |
| Llama 2 by Meta AI | 7B, 13B, 70B | Touvron et al. 2023 |
| LongChat by LMSYS | 7B, 13B | LongChat Team 2023 |
| Mistral and Mixtral by Mistral AI | 7B | Mistral website |
| Nous-Hermes by NousResearch | 7B, 13B, 70B | Org page |
| OpenLLaMA by OpenLM Research | 3B, 7B, 13B | Geng & Liu 2023 |
| Phi by Microsoft Research | 1.3B, 2.7B | Li et al. 2023 |
| Platypus by Lee at el. | 7B, 13B, 70B | Lee, Hunter, and Ruiz 2023 |
| Pythia by EleutherAI | {14,31,70,160,410}M, {1,1.4,2.8,6.9,12}B | Biderman et al. 2023 |
| RedPajama-INCITE by Together | 3B, 7B | Together 2023 |
| StableCode by Stability AI | 3B | Stability AI 2023 |
| StableLM by Stability AI | 3B, 7B | Stability AI 2023 |
| StableLM Zephyr by Stability AI | 3B | Stability AI 2023 |
| TinyLlama by Zhang et al. | 1.1B | Zhang et al. 2023 |
| Vicuna by LMSYS | 7B, 13B, 33B | Li et al. 2023 |
This implementation extends on Lit-LLaMA and nanoGPT, and it's powered by Lightning Fabric ⚡.
🏆 NeurIPS 2023 Large Language Model Efficiency Challenge: 1 LLM + 1 GPU + 1 Day
The LitGPT repository was the official starter kit for the NeurIPS 2023 LLM Efficiency Challenge, which is a competition focused on finetuning an existing non-instruction tuned LLM for 24 hours on a single GPU.
This repository follows the main principle of openness through clarity.
LitGPT is:
- Simple: Single-file implementation without boilerplate.
- Correct: Numerically equivalent to the original model.
- Optimized: Runs fast on consumer hardware or at scale.
- Open-source: No strings attached.
Avoiding code duplication is not a goal. Readability and hackability are.
Join our Discord to build high-performance, truly open-source models for the common benefit of the community.
Install with all dependencies (including CLI, quantization, tokenizers for all models, etc.):
pip install 'litgpt[all]'
To generate text predictions, you need to download the model weights. If you don't have them, check out our guide.
Run inference:
python litgpt/generate/base.py --prompt "Hello, my name is"This will run the 3B pretrained model and require ~7 GB of GPU memory using the bfloat16 datatype.
Full guide for generating samples from the model.
You can also chat with the model interactively:
python litgpt/chat/base.py
We support 4-bit quantization (as in QLoRA), (bnb.nf4, bnb.nf4-dq, bnb.fp4, bnb.fp4-dq) and 8-bit quantization (bnb.int8) for inference by following this guide.
We provide a simple training scripts (litgpt/finetune/*.py) that instruction-tunes a pretrained model on the Alpaca dataset.
For example, you can either use
Adapter (Zhang et al. 2023):
python litgpt/finetune/adapter.pyor Adapter v2 (Gao et al. 2023):
python litgpt/finetune/adapter_v2.pyor LoRA (Hu et al. 2021):
python litgpt/finetune/lora.py(Please see the tutorials/finetune_adapter for details on the differences between the two adapter methods.)
The finetuning requires at least one GPU with ~12 GB memory (RTX 3060).
It is expected that you have downloaded the pretrained weights as described above. More details about each finetuning method and how you can apply it to your own data can be found in our technical how-to guides.
These technical tutorials illustrate how to run the finetuning code.
Looking for conceptual tutorials and explanations? We have some additional articles below:
We provide simple training scripts based on Fabric if you want to venture into pretraining. Conversion scripts for our optimized streaming PackedDataset are included.
Follow this guide to start pretraining on
LitGPT includes a variety of dataset preparation scripts for finetuning and pretraining. Additional information about the datasets and dataset preparation is provided in the Preparing Datasets tutorial.
Lightning AI has partnered with Google to add first-class support for Cloud TPUs in Lightning’s frameworks and LitGPT, helping democratize AI for millions of developers and researchers worldwide.
Using TPUs with Lightning is as straightforward as changing one line of code.
We provide scripts fully optimized for TPUs in the XLA directory
We are on a quest towards fully open source AI.
Join us and start contributing, especially on the following areas:
We welcome all individual contributors, regardless of their level of experience or hardware. Your contributions are valuable, and we are excited to see what you can accomplish in this collaborative and supportive environment.
Unsure about contributing? Check out our How to Contribute to LitGPT guide.
Don't forget to join our Discord!
- @karpathy for nanoGPT
- @EleutherAI for GPT-NeoX and the Evaluation Harness
- @TimDettmers for bitsandbytes
- @Microsoft for LoRA
- @tridao for Flash Attention 2
If you use LitGPT in your research, please cite the following work:
@misc{litgpt-2023,
author = {Lightning AI},
title = {LitGPT},
howpublished = {\url{https://github.com/Lightning-AI/litgpt}},
year = {2023},
}
LitGPT is released under the Apache 2.0 license.