Neura Market
lit-llama logo

lit-llama

Free

5.8k

FreeFree tier
Inputs: textOutputs: text
Type
Open Source
Company
Lightning AI
Visit Website GitHub (40 stars)

About lit-llama

Lit-LLaMA is an open-source, independent implementation of Meta's LLaMA language model, built on the nanoGPT architecture and released under the Apache 2.0 license. It provides a fully transparent codebase for pretraining, finetuning, and inference of LLaMA-style models, supporting advanced techniques such as flash attention, Int8 and GPTQ 4-bit quantization, LoRA, and LLaMA-Adapter fine-tuning. The project is designed for researchers and developers who need a flexible, permissively licensed alternative to the original GPL-licensed LLaMA code. However, the repository is no longer actively maintained; users are directed to the successor project, LitGPT, for ongoing development and support. Lit-LLaMA works with the original LLaMA weights distributed by Meta under a research-only license, and users interested in LLaMA 2, Open LLaMA, or Vicuna weights should refer to the Lit-GPT repository.

Key Features

Independent implementation of LLaMA based on nanoGPT
Apache 2.0 license for full open-source use
Supports flash attention for efficient inference
Int8 and GPTQ 4-bit quantization for reduced memory usage
LoRA and LLaMA-Adapter fine-tuning methods
Pretraining and inference capabilities
Compatible with original LLaMA weights (research-only license)

Pros & Cons

Pros
  • Fully open-source under Apache 2.0 license, avoiding GPL restrictions
  • Supports multiple quantization methods (Int8, GPTQ 4-bit) for efficiency
  • Includes flash attention for faster inference
  • Offers LoRA and LLaMA-Adapter fine-tuning for parameter-efficient adaptation
  • Built on the well-known nanoGPT codebase for ease of understanding
Cons
  • No longer actively maintained; users should consider the LitGPT successor
  • Requires original LLaMA weights from Meta, which are under a research-only license
  • Free tier limits or usage restrictions are not applicable as it is self-hosted software
  • Documentation and community support may be limited due to deprecation
  • Setup and usage require technical expertise in Python and machine learning

Best For

Research into large language model architectures and trainingFine-tuning LLaMA models on custom datasets with LoRA or adaptersDeploying quantized LLaMA models for resource-constrained environmentsEducational exploration of transformer-based language modelsBuilding applications that require a permissively licensed LLaMA implementation

FAQ

Is Lit-LLaMA still maintained?
Based on the repository, Lit-LLaMA is no longer actively maintained. The project recommends using the LitGPT repository as a more up-to-date alternative.
What license does Lit-LLaMA use?
Lit-LLaMA is released under the Apache 2.0 license, which allows for free use, modification, and distribution, subject to the terms of that license.
Can I use Lit-LLaMA with LLaMA 2 weights?
The repository notes that it works with original LLaMA weights. For LLaMA 2, Open LLaMA, or Vicuna weights, users should refer to the Lit-GPT repository.
What hardware do I need to run Lit-LLaMA?
Hardware requirements depend on the model size and quantization used. The repository supports quantization (Int8, GPTQ 4-bit) to reduce memory usage, but specific requirements should be determined based on the chosen configuration.
Does Lit-LLaMA support fine-tuning?
Yes, Lit-LLaMA supports fine-tuning via LoRA and LLaMA-Adapter methods, as described in the repository.