Instructions to use ValiantLabs/Qwen3.5-27B-Guardpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ValiantLabs/Qwen3.5-27B-Guardpoint with Transformers:

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

pipe = pipeline("image-text-to-text", model="ValiantLabs/Qwen3.5-27B-Guardpoint")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ValiantLabs/Qwen3.5-27B-Guardpoint")
model = AutoModelForCausalLM.from_pretrained("ValiantLabs/Qwen3.5-27B-Guardpoint")
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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ValiantLabs/Qwen3.5-27B-Guardpoint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ValiantLabs/Qwen3.5-27B-Guardpoint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Qwen3.5-27B-Guardpoint",
		"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/ValiantLabs/Qwen3.5-27B-Guardpoint

SGLang

How to use ValiantLabs/Qwen3.5-27B-Guardpoint 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 "ValiantLabs/Qwen3.5-27B-Guardpoint" \
    --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": "ValiantLabs/Qwen3.5-27B-Guardpoint",
		"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 "ValiantLabs/Qwen3.5-27B-Guardpoint" \
        --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": "ValiantLabs/Qwen3.5-27B-Guardpoint",
		"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 ValiantLabs/Qwen3.5-27B-Guardpoint with Docker Model Runner:
```
docker model run hf.co/ValiantLabs/Qwen3.5-27B-Guardpoint
```

Upload folder using huggingface_hub

by sequelbox - opened Mar 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+1243

-860

Upload folder using huggingface_hub69e4acc9

sequelbox

Valiant Labs org Mar 12

No description provided.

Delete model-00001-of-00002.safetensorsb14b0ec2

Delete model-00002-of-00002.safetensors44dbe9d3

sequelbox

Valiant Labs org Mar 12

This PR merges the untrained 'visual' and 'mtp' base weights from Qwen/Qwen3.5-27B. This does not modify the trained weights, but improves the user experience by directly 'unlocking' the original Qwen 3.5 multimodal capabilities instead of forcing the user to swap or merge them.

We're not expecting any additional changes to the weights from this point, but if anything is found it will be addressed.

sequelbox changed pull request status to merged Mar 12

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