Instructions to use redashes/Qwen3.8-27B-BF16-SSMFIX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redashes/Qwen3.8-27B-BF16-SSMFIX with Transformers:

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

pipe = pipeline("image-text-to-text", model="redashes/Qwen3.8-27B-BF16-SSMFIX")
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("redashes/Qwen3.8-27B-BF16-SSMFIX")
model = AutoModelForMultimodalLM.from_pretrained("redashes/Qwen3.8-27B-BF16-SSMFIX", 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 redashes/Qwen3.8-27B-BF16-SSMFIX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redashes/Qwen3.8-27B-BF16-SSMFIX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"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/redashes/Qwen3.8-27B-BF16-SSMFIX

SGLang

How to use redashes/Qwen3.8-27B-BF16-SSMFIX 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 "redashes/Qwen3.8-27B-BF16-SSMFIX" \
    --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": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"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 "redashes/Qwen3.8-27B-BF16-SSMFIX" \
        --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": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"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 redashes/Qwen3.8-27B-BF16-SSMFIX with Docker Model Runner:
```
docker model run hf.co/redashes/Qwen3.8-27B-BF16-SSMFIX
```

can you make gguf?

by Gavin-chen - opened 18 days ago

Discussion

Gavin-chen

18 days ago

I need 4bit or 3bit version.

bfkxnr

18 days ago

See: https://huggingface.co/redashes/Qwen3.8-27B-BF16-SSMFIX/discussions/2

Luis23333

17 days ago

I made a 3bit version. tested and definitely better, let me know your feedback: https://huggingface.co/Luis23333/Qwen3.8-27B-SSMFIX-UD-Q3_K_XL-GGUF

redashes

Owner 17 days ago

here is
https://huggingface.co/Luis23333/Qwen3.8-27B-SSMFIX-UD-Q3_K_XL-GGUF

redashes

Owner 17 days ago

Thanks for the interest! Quick update so everyone knows where things stand:

We're currently working on an uncensored version of this model.
It will go through a full project evaluation before we release it.
Regarding GGUF: we only have a single RTX PRO 6000 and GPU resources are pretty tight right now, so we don't have plans to publish GGUF quantizations ourselves for the time being.
Once the uncensored version is uploaded, other authors are welcome to download it and quantize it on their own hardware.

Stay tuned! 🚀

akierum

16 days ago

How Unsloth missed this problem and why do not update their release?

bfkxnr

16 days ago

How Unsloth missed this problem and why do not update their release?

Unsloth delivers quantized GGUFs, not fine-tunes. The redashes weights is a fine-tune. Just take these weights and make a UD GGUF yourself, though I am not sure if Unsloth have published their imatrix file yet for UD3..

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