Instructions to use berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound with Transformers:

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

pipe = pipeline("image-text-to-text", model="berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound")
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("berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound", 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 berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound",
		"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/berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound

SGLang

How to use berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound 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 "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound" \
    --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": "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound",
		"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 "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound" \
        --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": "berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound",
		"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 berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound with Docker Model Runner:
```
docker model run hf.co/berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound
```

Qwen3.8-27B-Uncensored INT8 AutoRound (W8A16, linear attention BF16, group size 128)

INT8 weight-only quantization of orcarouter/Qwen3.8-27B-Uncensored with AutoRound (SignRound), following the recipe of Minachist/Qwen3.8-27B-INT8-AutoRound branch linear-attn-bf16-gs128, with two changes: linear attention is excluded from tuning (not swapped back to BF16 after the fact), and 500 iters instead of 250.

Tensors	Precision
`self_attn.{q,k,v,o}_proj` (16 full-attention layers), `mlp.{gate,up,down}_proj` (64 layers), MTP block projections	INT8 symmetric, group_size 128
`linear_attn.{in_proj_qkv,in_proj_z,out_proj,in_proj_a,in_proj_b}` (48 GDN layers), `embed_tokens`, `lm_head`, `mtp.fc`, norms, vision tower	BF16

263 INT8 linears / 354 BF16 linears. Format: auto_round:auto_gptq packing (vLLM loads it via GPTQ-Marlin with BF16 activations).

Recipe

AutoRound main @ b9f3d0079d014c73a1ff009800c597b9bc3f2a36 (version string 0.15.0), transformers 5.15.1, torch 2.13.0+cu130, one RTX PRO 6000 Blackwell. scheme="W8A16" (bits 8, group_size 128, sym), iters=500, nsamples=1024, seqlen=2048, batch_size=4, gradient_accumulate_steps=2, low_gpu_mem_usage=False, seed=42. Calibration: 256 samples built from NeelNanda/pile-10k + 768 from codeparrot/github-code-clean (documents concatenated so every sample is >= 2048 tokens, then truncated to 2048). Every layer is named in full in layer_config (avoids AutoRound's shared-dict regex aliasing bug). Tuning took 1.26 h.

KL divergence vs the BF16 source

Teacher-forced top-24 logprobs on one 128,000-token wikitext-103 stream (rows 100k+ of the train split), one sequence, BF16 KV cache, vLLM 0.27.1, KL(P_bf16 || Q_int8) in nats over the truncated top-24. These numbers are only comparable to other models scored with the same script, stream and teacher.

depth	n	KL mean	KL p50	KL p99	top-1 agreement	ΔNLL
0k-4k	3,999	0.00189	0.00056	0.0239	97.67%	+0.0038
4k-16k	12,000	0.00363	0.00078	0.0338	97.51%	+0.0011
16k-48k	32,000	0.00264	0.00085	0.0293	97.22%	+0.0019
48k-128k	80,000	0.00320	0.00088	0.0338	97.28%	+0.0022

Own NLL: BF16 1.8244, INT8 1.8265. For reference, the same script on Qwen/Qwen3.8-27B gives FP8 (Qwen/Qwen3.8-27B-FP8) KL 0.0048 / top-1 96.5% and Minachist's INT8 0.0029 / 97.2%.

Serving

vllm serve <this-repo> --tensor-parallel-size 2 --max-model-len 262144 --trust-remote-code

Tested with vLLM 0.27.1 (Using MarlinLinearKernel for AutoGPTQLinearMethod). MTP speculative decoding: --speculative-config '{"method":"mtp","num_speculative_tokens":3}'.

Downloads last month: 829

Safetensors

Model size

13B params

Tensor type

I32

BF16

F16

Model tree for berkerdooo/Qwen3.8-27B-Uncensored-INT8-AutoRound

Base model

Qwen/Qwen3.8-27B

Finetuned

orcarouter/Qwen3.8-27B-Uncensored

Quantized

(27)

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