Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Official quantization of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using [PV-Tuning](https://arxiv.org/abs/2405.14852) on top of [AQLM](https://arxiv.org/abs/2401.06118).
|
2 |
+
|
3 |
+
For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
|
4 |
+
|
5 |
+
Results (0-shot `acc`):
|
6 |
+
| Model | Quantization | WinoGrande | PiQA | HellaSwag | ArcE | ArcC | Model size, Gb |
|
7 |
+
|------|------|------|-------|-------|-------|------|------|
|
8 |
+
|Mistral-7B-v0.1| None | 0.7364 | 0.8047 | 0.6115 | 0.7887 | 0.4923 | 14.5 |
|
9 |
+
| |1x16 (this) | 0.7151 | 0.7976 | 0.5880 | 0.7698 | 0.4514 | 2.51 |
|
10 |
+
|
11 |
+
The 1x16g16 (1-bit) models are on the way, as soon as we update the inference lib with their respective kernels.
|
12 |
+
|
13 |
+
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).
|
14 |
+
The original code for PV-Tuning can be found in the [AQLM@pv-tuning](https://github.com/Vahe1994/AQLM/tree/pv-tuning) branch.
|