transformers, which has out-of-the-box int8 support. I'll keep this repo up as a means of space-efficiently testing LLaMA weights packaged as state_dicts, but for serious inference or training workloads I encourage users to migrate to transformers. Instructions for converting weights can be found here.
This is a fork of the LLaMA code that runs LLaMA-13B
comfortably within 24 GiB of RAM.
It relies almost entirely on the bitsandbytes and LLM.int8() work of Tim Dettmers.
I've tested it on an RTX 4090, and it reportedly works on the 3090. It might also theoretically allow us to run LLaMA-65B on an 80GB A100, but I haven't tried this.
The code contains the following changes:
- Removes parallelism constructs
- Quantizes weights on the host machine
- Loads weights incrementally to avoid severe memory problems
- Added dependencies on
bitsandbytes,tqdm. - Repetition penalty settings (
--repetition_penalty, default 1.15)
On my Ubuntu machine with 64 GB of RAM and an RTX 4090, it takes about 25 seconds to load in the floats and quantize the model.
Users should be ready to expand their swapfiles if they don't have enough RAM.
Llamanon has also produced a slightly uncouth user's guide for using this repo, which I won't reproduce here but seems generally trustworthy.
You will likely need to build bitsandbytes from source.
If you have interesting ideas for further development, I can be reached at https://twitter.com/ecjwg.
python example.py --ckpt_dir [TARGET_DIR]/13B --tokenizer_path [TARGET_DIR]/tokenizer.model --max_batch_size=1
This repository is intended as a minimal, hackable and readable example to load LLaMA (arXiv) models and run inference. In order to download the checkpoints and tokenizer, fill this google form
In a conda env with pytorch / cuda available, run
pip install -r requirements.txt
Then in this repository
pip install -e .
Once your request is approved, you will receive links to download the tokenizer and model files.
Edit the download.sh script with the signed url provided in the email to download the model weights and tokenizer.
The provided example.py can be run on a single or multi-gpu node with torchrun and will output completions for two pre-defined prompts. Using TARGET_FOLDER as defined in download.sh:
torchrun --nproc_per_node MP example.py --ckpt_dir $TARGET_FOLDER/model_size --tokenizer_path $TARGET_FOLDER/tokenizer.model
Different models require different MP values:
| Model | MP |
|---|---|
| 7B | 1 |
| 13B | 2 |
| 33B | 4 |
| 65B | 8 |
- 1. The download.sh script doesn't work on default bash in MacOS X
- 2. Generations are bad!
- 3. CUDA Out of memory errors
- 4. Other languages
See MODEL_CARD.md
See the LICENSE file.