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license: apache-2.0 |
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language: |
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- en |
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This is 2-bit quantization of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) using [QuIP#](https://cornell-relaxml.github.io/quip-sharp/) |
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## Model loading |
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Please follow the instruction of [QuIP-for-all](https://github.com/chu-tianxiang/QuIP-for-all) for usage. |
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As an alternative, you can use [vLLM branch](https://github.com/chu-tianxiang/vllm-gptq/tree/quip_gemv) for faster inference. QuIP has to launch like 5 kernels for each linear layer, so it's very helpful for vLLM to use cuda-graph to reduce launching overhead. BTW, If you have problem installing fast-hadamard-transform from pip, you can also install it from [source](https://github.com/Dao-AILab/fast-hadamard-transform) |
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## Perplexity |
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Measured at Wikitext with 4096 context length |
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| fp16 | 2-bit | |
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| ------- | ------- | |
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| 3.8825 | 5.2799 | |
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## Speed |
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Measured with `examples/benchmark_latency.py` script at vLLM repo. |
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At batch size = 1, it generates at 16.3 tokens/s with single 3090. |
