Official quantization of Mistral-7B-v0.1 using PV-Tuning on top of AQLM.
0
4 commits
1 linked in READMEs
updated May 30, 2024
Official quantization of Mistral-7B-v0.1 using PV-Tuning on top of AQLM.
For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
Results (0-shot acc):
| Model | Quantization | WinoGrande | PiQA | HellaSwag | ArcE | ArcC | Model size, Gb |
|---|---|---|---|---|---|---|---|
| Mistral-7B-v0.1 | None | 0.7364 | 0.8047 | 0.6115 | 0.7887 | 0.4923 | 14.5 |
| 1x16 (this) | 0.7151 | 0.7976 | 0.5880 | 0.7698 | 0.4514 | 2.51 |
The 1x16g16 (1-bit) models are on the way, as soon as we update the inference lib with their respective kernels.
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.
Official quantization of Mistral-7B-v0.1 using PV-Tuning on top of AQLM.
0
4 commits
1 linked in READMEs
updated May 30, 2024
Official quantization of Mistral-7B-v0.1 using PV-Tuning on top of AQLM.
For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
Results (0-shot acc):
| Model | Quantization | WinoGrande | PiQA | HellaSwag | ArcE | ArcC | Model size, Gb |
|---|---|---|---|---|---|---|---|
| Mistral-7B-v0.1 | None | 0.7364 | 0.8047 | 0.6115 | 0.7887 | 0.4923 | 14.5 |
| 1x16 (this) | 0.7151 | 0.7976 | 0.5880 | 0.7698 | 0.4514 | 2.51 |
The 1x16g16 (1-bit) models are on the way, as soon as we update the inference lib with their respective kernels.
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.
