Instructions to use reasonwang/SkillGym-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reasonwang/SkillGym-Qwen3.5-9B with Transformers:

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

pipe = pipeline("text-generation", model="reasonwang/SkillGym-Qwen3.5-9B")
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("reasonwang/SkillGym-Qwen3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("reasonwang/SkillGym-Qwen3.5-9B", 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 reasonwang/SkillGym-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reasonwang/SkillGym-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reasonwang/SkillGym-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reasonwang/SkillGym-Qwen3.5-9B

SGLang

How to use reasonwang/SkillGym-Qwen3.5-9B 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 "reasonwang/SkillGym-Qwen3.5-9B" \
    --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": "reasonwang/SkillGym-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "reasonwang/SkillGym-Qwen3.5-9B" \
        --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": "reasonwang/SkillGym-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reasonwang/SkillGym-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/reasonwang/SkillGym-Qwen3.5-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SkillGym-Qwen3.5-9B

SkillGym-Qwen3.5-9B is Qwen3.5-9B finetuned on SkillGym trajectories to use agent skills. It is one of the models released with the paper SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation.

An agent skill is a folder of instructions, reference documents, and scripts that an agent can consult while solving a task. This model is trained to read the relevant skill and apply it, through tool calls or shell commands, in a sandboxed workspace.

Paper: https://arxiv.org/abs/2609.37539
Code: https://github.com/Reason-Wang/SkillGym
Collection: https://huggingface.co/collections/reasonwang/skillgym

Results

Scores are on a 0-100 scale, higher is better. SkillGym is task success on the SkillGym test set. SkillEval and SkillsBench report the mean and standard deviation over three runs. Skill-Use-Bench reports the skill-use (SU) score. All models are evaluated with skills available, using MiniSwe-Agent as the harness.

Model	Size	SkillGym	SkillEval	SkillsBench	Skill-Use-Bench
MiniCPM5	2B	33.2	63.0 ± 0.6	10.8 ± 2.7	24.1
+ SkillGym SFT	2B	33.2	68.1 ± 2.0	6.1 ± 0.8	44.7
Ministral-3	8B	17.5	55.7 ± 3.1	3.9 ± 2.7	4.3
+ SkillGym SFT	8B	49.5	77.5 ± 0.4	16.9 ± 1.3	55.1
Qwen3.5	4B	33.8	62.8 ± 1.5	10.1 ± 1.3	8.8
+ SkillGym SFT	4B	47.0	70.8 ± 1.6	14.3 ± 1.0	48.7
Qwen3.5	9B	41.3	65.7 ± 1.1	14.8 ± 3.5	14.8
+ SkillGym SFT (this model)	9B	59.5	74.7 ± 1.2	22.4 ± 4.2	49.6
Qwen3.5	27B	55.3	76.2 ± 1.1	32.6 ± 2.3	28.3
+ SkillGym SFT	27B	62.8	80.1 ± 1.5	47.4 ± 5.1	74.2
Qwen3.5	122B/10B	53.0	69.8 ± 1.1	30.1 ± 2.5	16.8
+ SkillGym SFT	122B/10B	65.0	80.0 ± 0.3	53.6 ± 5.2	72.2

Training


Base model	Qwen/Qwen3.5-9B
Data	reasonwang/skillgym-sft, 19k verified successful trajectories from three teacher models
Method	Supervised finetuning, 2 epochs
Learning rate	1e-5 with linear decay, AdamW
Batch size	128
Max sequence length	65,536 tokens
Reasoning	Trained with reasoning traces

Usage

The model keeps the chat template of its base model and can be served with any OpenAI-compatible server, for example vLLM.

vllm serve reasonwang/SkillGym-Qwen3.5-9B

In our evaluation we use temperature 0.6, top_p 0.95, top_k 20. The skill names and descriptions are listed in the system prompt, and the agent reads the skill files itself.

Limitations

The model is an agent policy for tasks with skills and tools. It is not tuned as a general chat assistant.
Long episodes can exhaust the context budget before the task is finished.
Reasoning occasionally repeats itself and runs to the per-turn token limit.

Citation

@article{wang2026skillgym,
  title   = {{SkillGym}: Training Skill-Use Agents with Automatic Verifiable Environment Generation},
  author  = {Wang, Renxi and Hee, Mingshan and Koto, Fajri and Baldwin, Timothy and Li, Haonan},
  journal = {arXiv preprint arXiv:2609.37539},
  year    = {2026}
}