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@@ -20,7 +20,5 @@ For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
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  | 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) |
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  | meta-llama/Meta-Llama-3-70B (this) | 1x16g8 | 4.57 | 21.9 | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-2Bit-1x16)|
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- The 1x16g16 (1-bit) models are on the way, as soon as we update the inference lib with their respective kernels.
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  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).
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  The original code for PV-Tuning can be found in the [AQLM@pv-tuning](https://github.com/Vahe1994/AQLM/tree/pv-tuning) branch.
 
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  | 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) |
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  | meta-llama/Meta-Llama-3-70B (this) | 1x16g8 | 4.57 | 21.9 | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-2Bit-1x16)|
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  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).
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  The original code for PV-Tuning can be found in the [AQLM@pv-tuning](https://github.com/Vahe1994/AQLM/tree/pv-tuning) branch.