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---
license: apache-2.0
---
I used AutoAWQ to quantize Kimi K2
The base model is [here](https://huggingface.co/QuixiAI/Kimi-K2-Base-AWQ)
```
$ docker run -it --rm \
--gpus all \
--network=host \
--shm-size=1024g \
--entrypoint /bin/sh \
-v /home/hotaisle/workspace/models:/models \
-v $HOME/.cache/huggingface:/root/.cache/huggingface \
vllm/vllm-openai:latest \
-c "pip install blobfile && python3 -m vllm.entrypoints.openai.api_server --model QuixiAI/Kimi-K2-Instruct-AWQ --port 8000 --tensor-parallel-size 8 --trust-remote-code --gpu-memory-utilization 0.95 --enable-prefix-caching --enable-chunked-prefill --dtype bfloat16"```
```
vLLM seems to have a bug that prevents it from inferencing.
```
(VllmWorker rank=1 pid=644) ERROR 08-03 22:42:00 [multiproc_executor.py:511] WorkerProc failed to start.
...
(VllmWorker rank=1 pid=644) ERROR 08-03 22:42:00 [multiproc_executor.py:511] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 443, in weight_loader
(VllmWorker rank=1 pid=644) ERROR 08-03 22:42:00 [multiproc_executor.py:511] param[shard_offset:shard_offset + shard_size] = loaded_weight
(VllmWorker rank=1 pid=644) ERROR 08-03 22:42:00 [multiproc_executor.py:511] ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(VllmWorker rank=1 pid=644) ERROR 08-03 22:42:00 [multiproc_executor.py:511] RuntimeError: The expanded size of the tensor (264) must match the existing size (72) at non-singleton dimension 1. Target sizes: [576, 264]. Tensor sizes: [7168, 72]
``` |