Qwen3.8-27B-Quark-Qronos-INT4-W4A16
INT4 weight-only (W4A16) quantized version of Qwen/Qwen3.8-27B, produced with the Qronos algorithm using AMD Quark.
- Weights: INT4, group size 128
- Activations: BF16 (unquantized)
- Algorithm: Qronos (Hessian-based post-training quantization)
- Calibration: 128 samples, sequence length 512
- Base model:
Qwen/Qwen3.8-27B(Apache 2.0)
Benchmark results
| Benchmark | Setting | This model (Qronos) | BF16 base | Recovery % |
|---|---|---|---|---|
| GSM8K, 5-shot (flexible-extract / strict-match) | Thinking: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0, max_gen_toks=8192 | 94.62% / 94.69% | 93.33% / 93.33% | 101.4% |
| GSM8K, 5-shot (flexible-extract / strict-match) | Non-thinking: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0, max_gen_toks=8192 | 89.99% / 89.84% | 90.67% / 89.76% | 99.2% |
| Wikitext perplexity | Greedy | 8.6819 | 8.4364 | 97.2% |
| BFCL Overall Acc (single_turn)* | Greedy (harness default) | 23.80% | 24.38% | 97.6% |
* BFCL Overall Acc reflects only single_turn categories, not the full Gorilla-leaderboard formula (multi-turn/web-search/memory categories were not run and would count as 0 against the public leaderboard's own Overall Acc). Sub-metrics: Non-Live AST 85.17% (base 88.52%), Live AST 82.09% (base 83.05%), Relevance Detection 62.50% (base 75.00%), Irrelevance Detection 70.79% (base 72.22%).
Recovery % = quantized / BF16-base, using flexible-extract for GSM8K rows and base/quantized (inverted, since lower is better) for perplexity — both measured by us against verified-upstream Qwen/Qwen3.8-27B weights, not vendor-reported numbers. GSM8K uses lm-evaluation-harness; non-thinking mode is approximated by pre-closing an empty <think></think> block in the prompt, since the harness task is a raw few-shot completion rather than a chat-templated request. BFCL run via the official bfcl_eval harness.
Eval command
GSM8K, thinking mode, via lm-evaluation-harness's native vLLM backend:
lm-eval run \
--model vllm \
--model_args pretrained=amd/Qwen3.8-27B-Quark-Qronos-INT4-W4A16,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.4,enforce_eager=True,trust_remote_code=True \
--tasks gsm8k \
--num_fewshot 5 \
--gen_kwargs max_gen_toks=8192,do_sample=True,temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0 \
--batch_size auto \
--log_samples
GSM8K, no-thinking mode. The prompt suffix <think>\n\n</think>\n\n is prepended to each answer field to suppress the model's thinking preamble (see custom task yaml below).
# gsm8k_nothink.yaml
task: gsm8k_nothink
dataset_path: openai/gsm8k
dataset_name: main
output_type: generate_until
training_split: train
fewshot_split: train
test_split: test
doc_to_text: "Question: {{question}}\nAnswer: <think>\n\n</think>\n\n"
doc_to_target: "{{answer}}"
metric_list:
- metric: exact_match
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: false
regexes_to_ignore: [",", "\\$", "(?s).*#### ", "\\.$"]
generation_kwargs:
until: ["Question:", "</s>", "<|im_end|>"]
do_sample: false
temperature: 0.0
repeats: 1
num_fewshot: 5
filter_list:
- name: "strict-match"
filter: [{function: "regex", regex_pattern: "#### (\\-?[0-9\\.\\,]+)"}, {function: "take_first"}]
- name: "flexible-extract"
filter: [{function: "regex", group_select: -1, regex_pattern: "(-?[$0-9.,]{2,})|(-?[0-9]+)"}, {function: "take_first"}]
metadata: {version: 3.0}
lm-eval run \
--model vllm \
--model_args pretrained=amd/Qwen3.8-27B-Quark-Qronos-INT4-W4A16,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.4,enforce_eager=True,trust_remote_code=True \
--tasks gsm8k_nothink \
--include_path <dir containing gsm8k_nothink.yaml> \
--num_fewshot 5 \
--gen_kwargs max_gen_toks=8192,do_sample=True,temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0 \
--batch_size auto \
--log_samples
--gen_kwargs on the CLI overrides the YAML task's own generation_kwargs defaults (greedy) with the instruct-mode recommended sampling parameters used for the non-thinking scores above.
Quantization command
python3 quantize_quark.py \
--model_dir Qwen/Qwen3.8-27B \
--output_dir Qwen3.8-27B-Quark-Qronos-INT4-W4A16 \
--quant_scheme int4_wo_128 \
--num_calib_data 128 \
--seq_len 512 \
--quant_algo qronos \
--model_export hf_format \
--data_type auto \
--device cuda
Run from Quark/examples/torch/language_modeling/llm_ptq using AMD Quark with native qwen3_5 architecture support for Qronos (contributed upstream).
Serving
Requires a Quark-compatible inference runtime with W4A16Int4 scheme support (#48606 and #46110).
vllm serve amd/Qwen3.8-27B-Quark-Qronos-INT4-W4A16 \
--trust-remote-code \
--tensor-parallel-size 1 \
--reasoning-parser qwen3
Citation
Quantized using the Qronos algorithm (Zhang et al., ICLR 2026):
@inproceedings{zhang2026qronos,
title={Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization},
author={Zhang, Shihao and Zhang, Haoyu and Colbert, Ian and Saab, Rayan},
booktitle={International Conference on Learning Representations (ICLR)},
year={2026},
url={https://arxiv.org/abs/2505.11695}
}
License
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.