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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
| Date | Stars |
|---|---|
| 2026-07-31 | 6084 |
| 2026-08-01 | 6084 |
| 2026-08-02 | 6084 |
| 2026-08-06 | 6084 |
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<div align="center"> <img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/Lit_LLaMA_Badge3x.png" alt="Lit-LLaMA" width="128"/> # ⚡ Lit-LLaMA ️  [](https://dev.azure.com/Lightning-AI/lit%20Models/_build/latest?definitionId=49&branchName=main) [](https://github.com/Lightning-AI/lit-llama/blob/master/LICENSE) [](https://discord.gg/VptPCZkGNa) <pre> <b>⚠️ Warning: Not Actively Maintained</b> This repository is no longer actively maintained. For a more up-to-date alternative, please visit the LitGPT project: <a href="https://github.com/Lightning-AI/litgpt">https://github.com/Lightning-AI/litgpt</a>, which serves as the successor to this repository. Feel free to explore, reuse, or fork, but be aware that no further updates or support will be provided. </pre> <img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/Llama_pineapple.gif" alt="Lit-LLaMA and pineapple pizza" width="500px"/> </div> # ⚡ Lit-LLaMA ️ Independent implementation of [LLaMA](<https://github.com/facebookresearch/llama>) pretraining, finetuning, and inference code that is fully open source under the **Apache 2.0 license.** This implementation builds on [nanoGPT](<https://github.com/karpathy/nanoGPT>). The open-source code in this repository works with the original LLaMA weights that are distributed by Meta under a [research-only license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#model-details). ## Looking for LLaMA 2? Meta AI has since released LLaMA 2. Additionally, new Apache 2.0 licensed weights are being released as part of the [Open LLaMA project](https://github.com/openlm-research/open_llama). To run LLaMA 2 weights, Open LLaMA weights, or Vicuna weights (among other LLaMA-like checkpoints), **check out the [Lit-GPT repository](https://github.com/Lightning-AI/lit-gpt)**. ## Why? We believe that AI should be fully open source and part of the collective knowledge. The original [LLaMA code](https://github.com/facebookresearch/llama) is [GPL licensed](https://github.com/facebookresearch/llama/blob/main/LICENSE) which means any project using it must also be released under GPL. This "taints" any other code and prevents integration with the rest of the ecosystem. **Lit-LLaMA solves that for good.** ## Design principles **Lit-LLaMA** is: - **Simple:** Single-file implementation without boilerplate. - **Correct:** Numerically equivalent to the original model. - **Optimized:** Runs on consumer hardware or at scale. - **Open-source:** No strings attached. ## Get involved! [Join our Discord](https://discord.gg/VptPCZkGNa) to build high-performance, truly open-source models for the common benefit of the community. ## Setup Clone the repo ```bash git clone https://github.com/Lightning-AI/lit-llama cd lit-llama ``` install dependencies ```bash pip install -e ".[all]" ``` You are all set! 🎉 ## Use the model To generate text predictions, you need to download the model weights. **If you don't have them, check out our [guide](howto/download_weights.md).** Run inference: ```bash python generate.py --prompt "Hello, my name is" ``` This will run the 7B model and require ~26 GB of GPU memory (A100 GPU). [Full guide for generating samples from the model](howto/inference.md). ### Run Lit-LLaMA on consumer devices On GPUs with `bfloat16` support, the `generate.py` script will automatically convert the weights and consume about ~14 GB. For GPUs with less memory, or ones that don't support `bfloat16`, enable quantization (`--quantize llm.int8`): ```bash python generate.py --quantize llm.int8 --prompt "Hello, my name is
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1dd8cd1d5447283e, desc:fine-tuning, desc:fine tuning, desc:lora
matched fp:1dd8cd1d5447283e, desc:quantization, desc:int8, desc:flash attention