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RedHatAI/
QwQ-32B-Preview-quantized.w4a16

Name: QwQ-32B-Preview-quantized.w4a16
Availability: InStock

Qwen/Qwen2.5-32B-Instruct

quantized

apache-2.0

compressed-tensors

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QwQ-32B-Preview-quantized.w4a16

Model Overview

Model Architecture: QwQ-32B-Preview
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: INT4
- Activation quantization: None
Release Date: 3/1/2025
Version: 1.0
Model Developers: Neural Magic

Quantized version of QwQ-32B-Preview. It achieves an average score of 75.87 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 77.20.

Model Optimizations

This model was obtained by only quantizing the weights of QwQ-32B-Preview to INT4 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

python

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 4096, 1
model_name = "neuralmagic-ent/QwQ-32B-Preview-quantized.w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])

messages_list = [
    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]

outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below with the following arguments:

python quantize.py --model_path Qwen/QwQ-32B-Preview --quant_path "output_dir/QwQ-32B-Preview-quantized.w4a16" --calib_size 128 --dampening_frac 0.1 --observer minmax --actorder False

python

from datasets import load_dataset
from transformers import AutoTokenizer
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply
import argparse
from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy

def parse_actorder(value):
    # Interpret the input value for --actorder
    if value.lower() == "false":
        return False
    elif value.lower() == "group":
        return "group"
    else:
        raise argparse.ArgumentTypeError("Invalid value for --actorder. Use 'group' or 'False'.")


parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--quant_path', type=str)
parser.add_argument('--num_bits', type=int, default=4)
parser.add_argument('--sequential_update', type=bool, default=True)
parser.add_argument('--calib_size', type=int, default=256) 
parser.add_argument('--dampening_frac', type=float, default=0.05)
parser.add_argument('--observer', type=str, default="minmax")
parser.add_argument(
    '--actorder',
    type=parse_actorder,
    default=False,  
    help="Specify actorder as 'group' (string) or False (boolean)."
)

args = parser.parse_args()

model = SparseAutoModelForCausalLM.from_pretrained(
    args.model_path,
    device_map="auto",
    torch_dtype="auto",
    use_cache=False,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_path)

NUM_CALIBRATION_SAMPLES = args.calib_size
DATASET_ID = "garage-bAInd/Open-Platypus"
DATASET_SPLIT = "train"
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))

def preprocess(example):
    concat_txt = example["instruction"] + "\n" + example["output"]
    return {"text": concat_txt}

ds = ds.map(preprocess)

def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        truncation=False,
        add_special_tokens=True,
    )

ds = ds.map(tokenize, remove_columns=ds.column_names)
quant_scheme = QuantizationScheme(
    targets=["Linear"],
    weights=QuantizationArgs(
        num_bits=args.num_bits,
        type=QuantizationType.INT,
        symmetric=True,
        group_size=128,
        strategy=QuantizationStrategy.GROUP,
        observer=args.observer,
        actorder=args.actorder
    ),
    input_activations=None,
    output_activations=None,
)

recipe = [
    GPTQModifier(
        targets=["Linear"],
        ignore=["lm_head"],
        sequential_update=args.sequential_update,
        dampening_frac=args.dampening_frac,
        config_groups={"group_0": quant_scheme},
    )
]
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    num_calibration_samples=args.calib_size,
)

# Save to disk compressed.
SAVE_DIR = args.quant_path
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)

Evaluation

The model was evaluated on OpenLLM Leaderboard V1 and V2, using the following commands:

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --tasks openllm \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

OpenLLM Leaderboard V2:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-quantized.w4a16",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

Accuracy

OpenLLM Leaderboard V1 evaluation scores

Metric	Qwen/QwQ-32B-Preview	neuralmagic-ent/QwQ-32B-Preview-quantized.w4a16
ARC-Challenge (Acc-Norm, 25-shot)	70.73	70.14
GSM8K (Strict-Match, 5-shot)	83.09	79.83
HellaSwag (Acc-Norm, 10-shot)	85.77	85.01
MMLU (Acc, 5-shot)	82.67	81.58
TruthfulQA (MC2, 0-shot)	60.88	59.57
Winogrande (Acc, 5-shot)	80.03	79.08
Average Score	77.20	75.87
Recovery	100.00	98.28

OpenLLM Leaderboard V2 evaluation scores

Metric	Qwen/QwQ-32B-Preview	neuralmagic-ent/QwQ-32B-Preview-quantized.w4a16
IFEval (Inst-and-Prompt Level Strict Acc, 0-shot)	42.34	41.09
BBH (Acc-Norm, 3-shot)	53.03	50.19
Math-Hard (Exact-Match, 4-shot)	21.15	22.68
GPQA (Acc-Norm, 0-shot)	2.97	3.64
MUSR (Acc-Norm, 0-shot)	9.57	14.01
MMLU-Pro (Acc, 5-shot)	52.00	49.87
Average Score	30.18	30.25
Recovery	100.00	100.23

RedHatAI/QwQ-32B-Preview-quantized.w4a16