Qwen3.8-27B-MixedInt2-ultra-lite

A mixed-precision AutoRound quantized version of Qwen/Qwen3.8-27B, optimized to significantly reduce memory requirements while preserving the quality of the original model.

Base Model

Base model: Qwen/Qwen3.8-27B

This model is a quantized version of the original Qwen3.8-27B checkpoint. It is not a fine-tune, merge, or distillation.

Quantization

Quantization was performed using Intel AutoRound with a custom mixed-precision quantization configuration.

The quantization recipe was designed to balance:

• Model quality
• Memory efficiency
• Inference performance
• vLLM compatibility

Some model components are intentionally retained at higher precision where appropriate.

Property	Value
Quantization framework	Intel AutoRound
Quantization type	Custom Mixed-Precision INT2
Group size	32, 64, 128
Base model	Qwen/Qwen3.8-27B
Language layers	64
Vision tower	Preserved at original precision

The detailed mixed-precision allocation strategy is not included in this model card.

Evaluation

Evaluation was performed using AutoRound's evaluation interface with LM Evaluation Harness.

The following results compare the original Qwen3.8-27B model against Qwen3.8-27B-MixedInt2-ultra-lite.

Benchmark	Metric	Qwen3.8-27B	Qwen3.8-27B-MixedInt2-ultra-lite	Difference	Recovery Rate
MMLU	acc	83.49%	78.30%	-5.19 pp	93.78%
GSM8K	exact_match (flexible)	72.86%	43.29%	-29.57 pp	59.42%
ARC-Challenge	acc_norm	—	—	—	—
BoolQ	acc	—	—	—	—
HellaSwag	acc_norm	—	—	—	—
PIQA	acc_norm	—	—	—	—
WinoGrande	acc	—	—	—	—
Average	—	—	—	—	—

MMLU Category Breakdown

MMLU Category	Qwen3.8-27B	Qwen3.8-27B-MixedInt2-ultra-lite	Difference	Recovery Rate
Humanities	77.39%	73.33%	-4.06 pp	94.75%
Other	86.03%	81.20%	-4.83 pp	94.39%
Social Sciences	90.74%	85.99%	-4.75 pp	94.77%
STEM	83.03%	75.36%	-7.67 pp	90.76%

GSM8K

Metric	Qwen3.8-27B	Qwen3.8-27B-MixedInt2-ultra-lite	Difference	Recovery Rate
Flexible Exact Match	72.86%	43.29%	-29.57 pp	59.42%
Strict Exact Match	70.36%	35.33%	-35.03 pp	50.22%

Recovery Rate represents benchmark performance relative to the original Qwen3.8-27B checkpoint. A recovery rate above 100% indicates that the quantized model scored higher than the original model in that particular evaluation. Benchmark preservation does not imply identical behavior for every prompt, multimodal workload, long-context workload, or generation setting.

Usage

This checkpoint is intended for inference engines with AutoRound quantization support, including compatible versions of vLLM.

vLLM

This PR is required to serve this model with vLLM. vLLM PR #52890.

As of August 24 at 9:30 PM KST, the model can now be served with vLLM. Previously, mixed 2-bit serving was not possible due to issues in both config.json and quantization_config.json, but these have now been fixed.

Example Serving

vllm serve Pilcothink/Qwen3.8-27B-MixedInt2-ultra-lite \
  --host 0.0.0.0 --port 8000 \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --enable-prefix-caching \
  --tool-call-parser qwen3_coder \
  --kv-cache-dtype fp8 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --mm-encoder-tp-mode data \
  --max-num-seqs 10

Example with Serving 1M Context

vllm serve Pilcothink/Qwen3.8-27B-MixedInt2-ultra-lite \
  --host 0.0.0.0 \
  --port 8000 \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --enable-prefix-caching \
  --tool-call-parser qwen3_coder \
  --kv-cache-dtype fp8 \
  --max-model-len 1000000 \
  --max-num-batched-tokens 8192 \
  --mm-encoder-tp-mode data \
  --max-num-seqs 10 \
  --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}'

With MTP enabled, an error appears to occur due to incompatibility with the Humming kernel’s weight-loading path. MTP is currently not usable with Humming.

Additional reasoning and tool-calling options should be configured according to the vLLM version being used.

Notes

• This is a quantized derivative of Qwen3.8-27B.
• The model uses a custom mixed-precision INT2 quantization recipe.
• The vision components are preserved at their original precision.
• This ultra-lite variant is designed to further reduce memory requirements compared with higher-bit quantized variants.
• Quantization may introduce small behavioral differences compared with the original checkpoint.

Acknowledgements

• Base model: Qwen Team
• Quantization framework: Intel AutoRound

Please refer to the original Qwen3.8-27B model card for licensing, intended usage, limitations, and other information applicable to the base model.