Instructions to use bowmanslayer/Qwen3.8-27B-W4A16-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bowmanslayer/Qwen3.8-27B-W4A16-vision with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="bowmanslayer/Qwen3.8-27B-W4A16-vision")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bowmanslayer/Qwen3.8-27B-W4A16-vision")
model = AutoModelForMultimodalLM.from_pretrained("bowmanslayer/Qwen3.8-27B-W4A16-vision", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bowmanslayer/Qwen3.8-27B-W4A16-vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bowmanslayer/Qwen3.8-27B-W4A16-vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bowmanslayer/Qwen3.8-27B-W4A16-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/bowmanslayer/Qwen3.8-27B-W4A16-vision

SGLang

How to use bowmanslayer/Qwen3.8-27B-W4A16-vision with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bowmanslayer/Qwen3.8-27B-W4A16-vision" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bowmanslayer/Qwen3.8-27B-W4A16-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bowmanslayer/Qwen3.8-27B-W4A16-vision" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bowmanslayer/Qwen3.8-27B-W4A16-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use bowmanslayer/Qwen3.8-27B-W4A16-vision with Docker Model Runner:
```
docker model run hf.co/bowmanslayer/Qwen3.8-27B-W4A16-vision
```

Qwen3.8-27B-W4A16-vision

4-bit (W4A16, group size 128) quantization of Qwen/Qwen3.8-27B, with the bf16 vision tower preserved and the MTP head omitted.

18 GB on disk. Serves on 2×RTX 3090 (TP=2) with 256K context and ~12.9 GB free for KV cache.

Attribution Qwen/Qwen3.8-27B (Apache 2.0) → this repo — W4A16 quantization + vision-tower repack.

All training and the vision tower are Qwen's. What is mine here: the W4A16 quantization pipeline, the text/vision split before calibration, and the repack that reattaches the bf16 vision tower to the quantized text stack.

Variants

Repo	MTP head	Size	Use when
this one (`-vision`)	no	18 GB	default
`-vision-mtp`	yes	19 GB	you want to try `--speculative-config`

Both are from the same quantization run; they differ only by the 849 MB model-mtp.safetensors. vLLM skips mtp.* unless speculative decoding is enabled.

Deployment (verified)

Hardware: 2×NVIDIA RTX 3090 24 GB (NVLink), TP=2.

vLLM launch (0.20.2, float16 compute):

vllm serve <path> \
  --host 0.0.0.0 --port 9411 \
  --dtype float16 --tensor-parallel-size 2 \
  --gpu-memory-utilization 0.95 \
  --max-model-len 262144 --max-num-seqs 16 \
  --enable-prefix-caching \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3 \
  --mm-processor-kwargs '{"max_pixels": 2097152}' \
  --limit-mm-per-prompt '{"image": 8, "video": 2}'

Runtime footprint (measured at startup):


Model weights on GPU	8.87 GiB per rank (× 2 ranks)
Available KV cache	12.84 GiB per rank
Total KV pool	~415K tokens across ranks
Marlin kernel	`MarlinLinearKernel` for `GPTQMarlinLinearMethod`

Why the mm flags matter:

--mm-processor-kwargs '{"max_pixels": 2097152}' prevents a tokenizer truncation on images > ~2M pixels (Mismatch in image token count between text and input_ids).
--limit-mm-per-prompt '{"image": 8, "video": 2}' enables the video path and lifts the per-request image cap. vLLM defaults undeclared modalities to zero.

Throughput

Measured on 2×3090 during evaluation:

Load	Decode
Single-request, thinking-on	55–70 tok/s
16 concurrent, thinking-on	~700 tok/s aggregate
Prompt prefill peak	3,300 tok/s per request

Quantization recipe

Same pipeline as bowmanslayer/Qwen3.8-27B-Uncensored-W4A16-vision — that repo's README documents the recipe in detail (text-only calibration on 256 samples × 2048 tokens from NeelNanda/pile-10k; vision tower stripped before quantization and repacked after; linear_attn.in_proj_* excluded on all 48 linear-attention layers; MTP head copied verbatim for the -mtp variant). The only difference between the two is the base checkpoint (this one is the unmodified Qwen 3.8-27B).

Evaluation

All numbers below use thinking ON (Qwen 3.8's default chat template opens a <think> block). This raises multiple-choice scores by 5–10 points over thinking OFF and is not comparable to leaderboards that disable thinking.

Full sampled details are in the accompanying JSON files in this repo (per-item gold and model reply for every task).

Full-set accuracy and truncation-corrected accuracy

Benchmark	Sampled	Accuracy	Ex-truncation Accuracy	Truncation rate
MMLU	150	84.00	95.45	12.0 %
CMMLU	150	84.67	92.03	8.0 %
C-Eval	150	78.67	88.06	10.7 %
ARC-C	150	94.00	94.63	0.7 %
TruthfulQA MC1	150	85.33	92.09	7.3 %
GSM8K	100	97.00	98.98	2.0 %
MATH-500	100	77.00	79.38	3.0 %
BBH	150	88.67	97.01	10.7 %
HumanEval	164	82.32	91.22	9.8 %
IFEval strict	100	79.00	91.57	17.0 %

Read the "ex-truncation" column as the actual capability. MC-class tasks were run with a 4096-token sampling budget which is not always enough for Qwen3.8's default <think> block to close on hard items — those items were counted as wrong but the failure is a sampling budget artifact, not a capability loss.

Sanity check against Qwen upstream

Qwen's public evaluation for Qwen3.8-27B publishes VL benchmarks as image tables under different eval configurations. We do not cross-compare these because our thinking mode is on. The numbers we ran that overlap with upstream's set are consistent with a healthy 27 B W4A16.

Vision & tool calling

Vision: 7/7 on a synthetic-image battery (solid colours, shape counting, OCR, grid counting, dense colour count, 1808×4000 resize test).
Tool calling: 7/11 on a mixed suite of 8 standard cases + 1 multi-tool composition + 2 boundary cases (nothing-should-be-called).

Safety

99/100 refusals on mlabonne/harmful_behaviors test split, refusal-prefix regex, thinking OFF (matches upstream Qwen's method).

Known limitations

Identity confusion. Asked "who are you?", the model sometimes claims to be Claude or GPT. Inherited from training data. Set a system prompt to enforce identity if it matters.
Fine visual detail is what Qwen3.8-27B provides; the vision tower is unchanged by quantization. Dedicated Qwen3-VL-* models will do better on OCR of dense small text and small-object counting.
Single images above ~2M pixels must be either downsampled by the server (--mm-processor-kwargs '{"max_pixels": 2097152}') or by the client.

Intended use

Local inference and research.

License

Apache 2.0, inherited from Qwen/Qwen3.8-27B. Qwen's acceptable use policy applies.

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Safetensors

Model size

27B params

Tensor type

I32

BF16

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Base model

Qwen/Qwen3.8-27B

Quantized

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