Instructions to use tiny-random/qwen3.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/qwen3.8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/qwen3.8")
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("tiny-random/qwen3.8")
model = AutoModelForMultimodalLM.from_pretrained("tiny-random/qwen3.8", 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 tiny-random/qwen3.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/qwen3.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/qwen3.8",
		"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/tiny-random/qwen3.8

SGLang

How to use tiny-random/qwen3.8 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 "tiny-random/qwen3.8" \
    --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": "tiny-random/qwen3.8",
		"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 "tiny-random/qwen3.8" \
        --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": "tiny-random/qwen3.8",
		"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 tiny-random/qwen3.8 with Docker Model Runner:
```
docker model run hf.co/tiny-random/qwen3.8
```

yujiepan commited on 4 days ago

Commit

d7ec583

verified ·

1 Parent(s): 9aaa9ba

Mark GPU-tested and standalone creation examples as verified

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -32,7 +32,7 @@ Structural comparison with `Qwen/Qwen3.8-27B`:
 ### Example usage:
-- SGLang
   - Runs with TP=2, the embedded NEXTN/MTP layer, and CUDA Graph.
   - Verified for text, image-plus-text and video-plus-text on two NVIDIA H20 GPUs;
@@ -57,7 +57,7 @@ sglang serve \
   --disable-custom-all-reduce
 ```
-- Transformers
 ```python
 import torch
@@ -81,7 +81,7 @@ output = model.generate(**inputs, do_sample=False, max_new_tokens=10)
 print(processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
 ```
-### Codes to create this repo:
 <details><summary>Click to expand</summary>

 ### Example usage:
+- SGLang (✅ verified)
   - Runs with TP=2, the embedded NEXTN/MTP layer, and CUDA Graph.
   - Verified for text, image-plus-text and video-plus-text on two NVIDIA H20 GPUs;
   --disable-custom-all-reduce
 ```
+- Transformers (✅ verified)
 ```python
 import torch
 print(processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
 ```
+### Codes to create this repo (✅ verified):
 <details><summary>Click to expand</summary>