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--- |
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library_name: transformers |
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tags: |
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- phi-3 |
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- phi-3-medium |
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- phi-3-medium-4k-instruct |
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- conversational |
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- text-generation-inference |
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pipeline_tag: text-generation |
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language: |
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- en |
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--- |
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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). |
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For this quantization, we used 1 codebook of 16 bits for groups of 8 weights. |
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Results (0-shot `acc`): |
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Results: |
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| Model | Quantization | WikiText-2 | C4 | Model size, Gb | |
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|------|------|-------|------|------| |
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| [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) | None | | | 27.9 | |
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| | [1x16g8 (2-bit, this model)](https://huggingface.co/ISTA-DASLab/Phi-3-medium-4k-instruct-AQLM-PV-2Bit-1x16-hf) | 5.18 | 8.56 | 4.2Gb | |
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| | [1x16g16 (1-bit, model link)](https://huggingface.co/ISTA-DASLab/Phi-3-medium-4k-instruct-AQLM-PV-1Bit-1x16-hf) | 7.42 | 10.40 | 2.7Gb | |
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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 , |
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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. |
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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. |
