---
library_name: transformers
tags:
- llama
- facebook
- meta
- llama-3
- conversational
- text-generation-inference
---

An official quantization of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) using [PV-Tuning](https://arxiv.org/abs/2405.14852) on top of [AQLM](https://arxiv.org/abs/2401.06118) .

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`


| Model      | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link                                                                 |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| meta-llama/Meta-Llama-3-8B | 1x16g8        | 6.99          | 4.1            | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-8B-AQLM-PV-2Bit-1x16) |
| meta-llama/Meta-Llama-3-8B  | 1x16g16        | 9.43          | 3.9            | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-8B-AQLM-PV-1Bit-1x16) |
| meta-llama/Meta-Llama-3-70B | 1x16g8        | 4.57           | 21.9           | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-2Bit-1x16)|
| meta-llama/Meta-Llama-3-70B (this) | 1x16g16        | 8.67           | 13           | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-2Bit-1x16)|

To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM).
The original code for PV-Tuning can be found in the [AQLM@pv-tuning](https://github.com/Vahe1994/AQLM/tree/pv-tuning) branch.