This is a hasty 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, but it should work on a 3090!
(It might also theoretically let you run LLaMA-65B on an A100, but I haven't tried this.)
The code contains the following changes:
- Loads all model_dicts into the same GPU
- Loads existing weights from specified directory
- Quantizes loaded layers on the host machine after weights are loaded.
- Added dependencies on
bitsandbytes,tqdm.
It takes over a minute, and up to 50 GB of RAM, to load in the floats and quantize the model, and it's far from optimal re: throughput. Someone (maybe me) should publish quantized weights to get around this!
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.