Update README.md

README.md

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 An official quantization of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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.
 
-For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
+__The 1x16g16 models require aqlm inference library v1.1.6 or newer:__
+
+`pip install aqlm[gpu,cpu]>=1.1.6`
+
+
+Note that a large portion of this model are the 16-bit embeddings/logits matrices. You can significantly reduce the model footprint by quantizing these matrices, e.g. using `bitsandbytes` LLM.int8 or NF4 formats.
 
 
 | Model      | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link                                                                 |
 |------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
-| meta-llama/Meta-Llama-3-8B (this) | 1x16g8        | 6.99          | 4.1            | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-8B-AQLM-PV-2Bit-1x16) |
+| meta-llama/Meta-Llama-3-8B  | 1x16g8        | 9.43          |  4.1           | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-8B-AQLM-PV-2Bit-1x16) |
+| meta-llama/Meta-Llama-3-8B (this)  | 1x16g16        | 6.99          | 3.9            | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-8B-AQLM-PV-1Bit-1x16) |
 | meta-llama/Meta-Llama-3-70B | 1x16g8        | 4.57           | 21.9           | [Link](https://huggingface.co/ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-2Bit-1x16)|
 
-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](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.