---
library_name: transformers
tags:
- llama
- facebook
- meta
- llama-2
- conversational
- text-generation-inference
---

An official quantization of [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) 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, totalling about 1.58 bits per weight.

__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                                                                 |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16        | 5.68          | 2.4            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-PV-2Bit-1x16-hf) |
| Llama-2-7b | 2x8         | 5.90          | 2.2            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-PV-2Bit-2x8-hf)  |
| Llama-2-7b (this) | 1x16g16     | 9.21          | 1.7            | [Link](https://huggingface.co/justheuristic/Llama-2-7b-AQLM-PV-1Bit-1x16-hf)  |
| Llama-2-13b| 1x16        | 5.05           | 4.1            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-PV-2Bit-1x16-hf)|
| Llama-2-70b| 1x16        | 3.78           | 18.8           | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-PV-2Bit-1x16-hf)|


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.