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

Libraries

How to use PYTHAI/Qwen3.8-27B-fork with Transformers:

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

pipe = pipeline("image-text-to-text", model="PYTHAI/Qwen3.8-27B-fork")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PYTHAI/Qwen3.8-27B-fork")
model = AutoModelForMultimodalLM.from_pretrained("PYTHAI/Qwen3.8-27B-fork", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PYTHAI/Qwen3.8-27B-fork with vLLM:

Install from pip and serve model

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

SGLang

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

Qwen3.8-27B-fork

File size: 2,667 Bytes

3bdd8ce

{
 "kind": "licence-locked pointer fork",
 "source_repo": "Qwen/Qwen3.8-27B",
 "source_url": "https://huggingface.co/Qwen/Qwen3.8-27B",
 "source_commit": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
 "forked_at_utc": "2026-09-13T23:55:35Z",
 "licence_tag": "apache-2.0",
 "licence_file": {
  "path": "LICENSE",
  "bytes": 11544,
  "sha256": "bbedc3fda3305820b977265f01b8619d87570a6739de3a5582c3464840f1e57a"
 },
 "weights_copied": false,
 "weights_not_copied": {
  "files": 18,
  "bytes": 55563006776
 },
 "parameters": 27781427952,
 "load_weights_with": "from_pretrained('Qwen/Qwen3.8-27B', revision='1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0')",
 "files": [
  {
   "path": "LICENSE",
   "bytes": 11544,
   "sha256": "bbedc3fda3305820b977265f01b8619d87570a6739de3a5582c3464840f1e57a"
  },
  {
   "path": "README.md",
   "bytes": 65012,
   "sha256": "57e4bdb258ee1a7d2635c5174ebd4e56abe392505cdb5f8bbb356b0dc4293641"
  },
  {
   "path": "chat_template.jinja",
   "bytes": 8952,
   "sha256": "c3cf9e34abf4f9e36c2d72165aa9c132d3e2a725b6c2586aaa3a8af9d7a81041"
  },
  {
   "path": "config.json",
   "bytes": 4312,
   "sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab"
  },
  {
   "path": "crc32.txt",
   "bytes": 238,
   "sha256": "b42dd291f5f98b05807e458f5b47849969a9b3326acbf0c97d28b05826740b83"
  },
  {
   "path": "generation_config.json",
   "bytes": 202,
   "sha256": "e70c136c1b78ddc1fb0905bac8e733a4dc448d4f852a5dd75143fffc70be550e"
  },
  {
   "path": "merges.txt",
   "bytes": 3353259,
   "sha256": "a9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d"
  },
  {
   "path": "model.safetensors.index.json",
   "bytes": 112216,
   "sha256": "77042094076611b69791a610065f28b7013b8c621795fa86ddccc8bac7d1b9df"
  },
  {
   "path": "preprocessor_config.json",
   "bytes": 390,
   "sha256": "27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516"
  },
  {
   "path": "tokenizer.json",
   "bytes": 12809320,
   "sha256": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3"
  },
  {
   "path": "tokenizer_config.json",
   "bytes": 17928,
   "sha256": "b11349aafa7cdc6a320767cf7ceb29ed82f7eda5d65e8e0819e76f0ce947bf27"
  },
  {
   "path": "video_preprocessor_config.json",
   "bytes": 385,
   "sha256": "7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13"
  },
  {
   "path": "vocab.json",
   "bytes": 6722759,
   "sha256": "ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003"
  }
 ],
 "why": "preserve the licence terms of this exact commit and pin the revision; the weights stay at the source and are addressed by commit sha",
 "forked_by": "mindX (PYTHAI) via the node token"
}