Instructions to use Zhongzhi1228/Qwen3.5-27B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zhongzhi1228/Qwen3.5-27B-RL with Transformers:

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

pipe = pipeline("image-text-to-text", model="Zhongzhi1228/Qwen3.5-27B-RL")
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("Zhongzhi1228/Qwen3.5-27B-RL")
model = AutoModelForMultimodalLM.from_pretrained("Zhongzhi1228/Qwen3.5-27B-RL", 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 Zhongzhi1228/Qwen3.5-27B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zhongzhi1228/Qwen3.5-27B-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zhongzhi1228/Qwen3.5-27B-RL",
		"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/Zhongzhi1228/Qwen3.5-27B-RL

SGLang

How to use Zhongzhi1228/Qwen3.5-27B-RL 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 "Zhongzhi1228/Qwen3.5-27B-RL" \
    --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": "Zhongzhi1228/Qwen3.5-27B-RL",
		"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 "Zhongzhi1228/Qwen3.5-27B-RL" \
        --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": "Zhongzhi1228/Qwen3.5-27B-RL",
		"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 Zhongzhi1228/Qwen3.5-27B-RL with Docker Model Runner:
```
docker model run hf.co/Zhongzhi1228/Qwen3.5-27B-RL
```

Qwen3.5-27B-RL

Qwen3.5-27B-RL is a reinforcement-learning checkpoint derived from Qwen/Qwen3.5-27B and associated with the Recursive Task Synthesis project.

Model details

Architecture: Qwen3.5 causal language model with vision encoder
Parameters: 27.8B
Weight dtype: BF16
Context length: 262,144 tokens
Format: Hugging Face Transformers, sharded Safetensors

Related resources

Usage

from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "Zhongzhi1228/Qwen3.5-27B-RL"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

License

The fine-tuned checkpoint weights and original repository contributions are released under the Creative Commons Attribution 4.0 International License. See LICENSE for the complete legal text.

This checkpoint is derived from Qwen/Qwen3.5-27B, which is licensed under Apache License 2.0. The unmodified base-model license is preserved in LICENSE.base-model and continues to apply to base-model components. The CC BY 4.0 grant does not supersede applicable base-model or other third-party terms.

The associated training-data repositories document their own upstream attribution and modification notices.

This license covers the original contributions and adaptations that the publisher has authority to license. It does not grant rights over third-party materials beyond what is permitted by their original terms. No endorsement by an upstream author or organization is implied.

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Safetensors

Model size

28B params

Tensor type

BF16

Model tree for Zhongzhi1228/Qwen3.5-27B-RL

Base model

Qwen/Qwen3.5-27B

Finetuned

(327)

this model

Collection including Zhongzhi1228/Qwen3.5-27B-RL

Recursive Synthesis for Long-Horizon Terminal Tasks

Collection

CC BY 4.0 datasets and SFT/RL checkpoints for recursive task synthesis; base-model and third-party terms still apply. • 6 items • Updated Aug 10 • 18