This is 2-bit quantization of mistralai/Mixtral-8x7B-Instruct-v0.1 using QuIP#
Model loading
Please follow the instruction of QuIP-for-all for usage.
As an alternative, you can use vLLM branch 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
Perplexity
Measured at Wikitext with 4096 context length
| fp16 | 2-bit |
|---|---|
| 3.8825 | 5.2799 |
Speed
Measured with examples/benchmark_latency.py script at vLLM repo.
At batch size = 1, it generates at 16.3 tokens/s with single 3090.
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