Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- phi-3
+- phi-3-medium
+- phi-3-medium-4k-instruct
+- conversational
+- text-generation-inference
+pipeline_tag: text-generation
+language:
+- en
+---
+
+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).
+
+For this quantization, we used 1 codebook of 16 bits for groups of 8 weights.
+
+Results (0-shot `acc`): 
+
+Results: 
+| Model      | Quantization | WikiText-2 | C4 | Model size, Gb |
+|------|------|-------|------|------|
+| [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) | None |  |  | 27.9 |
+|  | [1x16g8 (2-bit, model link)](https://huggingface.co/ISTA-DASLab/Phi-3-medium-4k-instruct-AQLM-PV-2Bit-1x16-hf) | 5.18	|  8.56 | 4.2Gb |
+|  | [1x16g16 (1-bit, this model)](https://huggingface.co/ISTA-DASLab/Phi-3-medium-4k-instruct-AQLM-PV-1Bit-1x16-hf) | 7.42	| 10.40  | 2.7Gb |
+
+Phi-3-**medium** is not included in the original [PV-Tuining paper](https://arxiv.org/abs/2405.14852). As of yet, we did not have the bandwidth to evaluate it properly. We hope to eventually run the zero-shot evaluation suite, or you can help us by running it yourself and opening a pull-request to the readme!
+
+In general, we always recommend the 2-bit models for best accuracy-size trade-offs. If tempted to use the 1-bit model, try a smaller model ,
+e.g. Phi-3-**mini** quantized with AQLM+PV [(quantized model link)](https://huggingface.co/ISTA-DASLab/Phi-3-mini-4k-instruct-AQLM-PV-2Bit-1x16-hf) and compare the results, or check our [AQLM+PV collection](https://huggingface.co/collections/ISTA-DASLab/aqlmpv-66564dff5d84f00a893ba93f) for a more appropriate size.
+
+
+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.