An official quantization of meta-llama/Meta-Llama-3-8B using PV-Tuning on top of AQLM . For this quantization, we used 1 codebook of 16 bits for groups of 16 weights.
The 1x16g16 models require aqlm inference library v1.1.6 or newer:
pip install aqlm[gpu,cpu]>=1.1.6
Note that a large portion of this model are the 16-bit embeddings/logits matrices. You can significantly reduce the model footprint by quantizing these matrices, e.g. using bitsandbytes
LLM.int8 or NF4 formats. This does not require additional training.
Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
---|---|---|---|---|
meta-llama/Meta-Llama-3-8B | 1x16g8 | 6.99 | 4.1 | Link |
meta-llama/Meta-Llama-3-8B (this) | 1x16g16 | 9.43 | 3.9 | Link |
meta-llama/Meta-Llama-3-70B | 1x16g8 | 4.57 | 21.9 | Link |
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the official GitHub repo. The original code for PV-Tuning can be found in the AQLM@pv-tuning branch.
- Downloads last month
- 100
This model does not have enough activity to be deployed to Inference API (serverless) yet.
Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.