An official quantization of meta-llama/Llama-2-7b using PV-Tuning on top of AQLM.
0
9 commits
1 linked in READMEs
updated May 31, 2024
An official quantization of meta-llama/Llama-2-7b using PV-Tuning on top of AQLM. 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 |
| Llama-2-7b | 2x8 | 5.90 | 2.2 | Link |
| Llama-2-7b (this) | 1x16g16 | 9.21 | 1.7 | Link |
| Llama-2-13b | 1x16 | 5.05 | 4.1 | Link |
| Llama-2-70b | 1x16 | 3.78 | 18.8 | 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.
An official quantization of meta-llama/Llama-2-7b using PV-Tuning on top of AQLM.
0
9 commits
1 linked in READMEs
updated May 31, 2024
An official quantization of meta-llama/Llama-2-7b using PV-Tuning on top of AQLM. 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 |
| Llama-2-7b | 2x8 | 5.90 | 2.2 | Link |
| Llama-2-7b (this) | 1x16g16 | 9.21 | 1.7 | Link |
| Llama-2-13b | 1x16 | 5.05 | 4.1 | Link |
| Llama-2-70b | 1x16 | 3.78 | 18.8 | 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.
