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README.md
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base_model:
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- Qwen/Qwen3-Coder-30B-A3B-Instruct
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---
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# Qwen3-Coder-30B-A3B-Instruct
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<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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base_model:
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- Qwen/Qwen3-Coder-30B-A3B-Instruct
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---
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# Qwen3-Coder-30B-A3B-Instruct-AWQ
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## Method
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Quantised using [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor.git), [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) and the following configs:
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```
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recipe = [
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AWQModifier(
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ignore=["lm_head", "re:.*mlp.gate$", "re:.*mlp.shared_expert_gate$"],
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scheme="W4A16",
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targets=["Linear"],
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),
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]
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```
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## Inference
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### vllm
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Please load the model into vllm and sglang as float16 data type for AWQ support and use `tensor_parallel_size <= 2` i.e.,
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```
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vllm serve cpatonn/Qwen3-Coder-30B-A3B-Instruct-AWQ --dtype float16 --tensor-parallel-size 2 --pipeline-parallel-size 2
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```
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# Qwen3-Coder-30B-A3B-Instruct
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<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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