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nm-testing/
Qwen3-Coder-30B-A3B-Instruct-W4A16-awq

Name: Qwen3-Coder-30B-A3B-Instruct-W4A16-awq
Availability: InStock

safetensors

qwen3_moe

Qwen/Qwen3-Coder-30B-A3B-Instruct

quantized

compressed-tensors

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Generation

Requires: https://github.com/vllm-project/llm-compressor/pull/1788

python

from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor import oneshot
from llmcompressor.modifiers.awq import AWQModifier

MODEL_ID = "Qwen/Qwen3-Coder-30B-A3B-Instruct"

SAVE_DIR = MODEL_ID.split("/")[-1] + "-W4A16-awq"


# Configure the quantization algorithm to run.
recipe = [
    AWQModifier(
        duo_scaling=False,
        ignore=[
            "lm_head",
            "re:.*mlp.gate$",
            "re:.*mlp.shared_expert_gate$",
            "re:visual.*",
        ],
        scheme="W4A16",
        targets=["Linear"],
    ),
]

# Select calibration dataset.
DATASET_ID = "codeparrot/self-instruct-starcoder"
DATASET_SPLIT = "curated"

# Select number of samples. 256 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 256
MAX_SEQUENCE_LENGTH = 2048


def get_calib_dataset(tokenizer):
    from datasets import load_dataset

    ds = load_dataset(
        DATASET_ID,
        split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES*10}]",
    )

    def preprocess(example):
        chat_messages = [
            {"role": "user", "content": example["instruction"].strip()},
            {"role": "assistant", "content": example["output"].strip()},
        ]
        tokenized_messages = tokenizer.apply_chat_template(
            chat_messages, tokenize=True
        )
        return {"input_ids": tokenized_messages}

    ds = (
        ds.shuffle(seed=42)
        .map(preprocess, remove_columns=ds.column_names)
        .select(range(NUM_CALIBRATION_SAMPLES))
    )

    return ds


if __name__ == "__main__":
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID, torch_dtype="auto", trust_remote_code=True
    )
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)

    ###
    ### Apply algorithms.
    ###
    oneshot(
        model=model,
        dataset=get_calib_dataset(tokenizer),
        recipe=recipe,
        max_seq_length=MAX_SEQUENCE_LENGTH,
        num_calibration_samples=NUM_CALIBRATION_SAMPLES,
        log_dir=None,
        trust_remote_code_model=True,
    )

    model.save_pretrained(SAVE_DIR)
    tokenizer.save_pretrained(SAVE_DIR)

Evaluation

The model was evaluated on HumanEval and HumanEval+ benchmark with the Neural Magic fork of the EvalPlus implementation of HumanEval+ and the vLLM engine, using the following commands:

bash

python evalplus/codegen/generate.py --model nm-testing/Qwen3-Coder-30B-A3B-Instruct-W4A16-awq --bs 16 --temperature 0.2 --n_samples 50 --root "./results" --dataset humaneval --backend vllm --dtype auto 

python evalplus/evalplus/sanitize.py results/humaneval/nm-testing--Qwen3-Coder-30B-A3B-Instruct-W4A16-awq_vllm_temp_0.2

evalplus.evaluate --dataset humaneval --samples results/humaneval/nm-testing--Qwen3-Coder-30B-A3B-Instruct-W4A16-awq_vllm_temp_0.2-sanitized

Metric	Qwen/Qwen3-Coder-30B-A3B-Instruct	nm-testing/Qwen3-Coder-30B-A3B-Instruct-W4A16-awq
HumanEval pass@1	93.0	93.7
HumanEval pass@10	93.9	94.5
HumanEval+ pass@1	88.7	89.3
HumanEval+ pass@10	89.8	90.2
Average Score	91.35	91.93