Sign up to save tools and stay up to date with the latest in AI
bg
bg
1

GitHub - unslothai/unsloth: 80% faster 50% less memory LLM finetuning

Dec 02, 2023 - github.com
The markdown data provides instructions on how to install and use Unsloth, a software that currently supports Linux distros and Pytorch versions 2.1 and above. The installation process involves using conda to install several packages including cudatoolkit, xformers, bitsandbytes, pytorch, and pytorch-cuda. Then, pip is used to install unsloth from its GitHub repository. Depending on the CUDA version, users may need to change 'cu121' to 'cu118'.

The data also includes Python code for importing the necessary modules and setting up the FastLlamaModel from Unsloth. The model can be patched and fast LoRA weights can be added. The model supports any llama model and allows for customization of several parameters such as max_seq_length, dtype, and load_in_4bit. The final part of the code is for using Huggingface's Trainer and dataset loading. The code ends with a command to configure the dynamic linker run-time bindings.

Key takeaways:

  • Unsloth is a tool that currently only supports Linux distributions and Pytorch version 2.1 or higher.
  • The tool can be installed using conda and pip commands, and supports CUDA versions 11.8 and 12.1.
  • Unsloth includes a FastLlamaModel that can be loaded with specific parameters, including sequence length, data type, and whether to use 4-bit quantization.
  • The model can be patched and trained using Huggingface's Trainer and dataset loading.
View Full Article

Comments (0)

Be the first to comment!