Instructions to use simonlqy/SkillGate-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simonlqy/SkillGate-9B with Transformers:

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

pipe = pipeline("text-generation", model="simonlqy/SkillGate-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("simonlqy/SkillGate-9B")
model = AutoModelForMultimodalLM.from_pretrained("simonlqy/SkillGate-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 simonlqy/SkillGate-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simonlqy/SkillGate-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": "simonlqy/SkillGate-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simonlqy/SkillGate-9B

SGLang

How to use simonlqy/SkillGate-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 "simonlqy/SkillGate-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": "simonlqy/SkillGate-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 "simonlqy/SkillGate-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": "simonlqy/SkillGate-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simonlqy/SkillGate-9B with Docker Model Runner:
```
docker model run hf.co/simonlqy/SkillGate-9B
```

SkillGate-9B / README.md

simonlqy

Link SkillGate arXiv paper

2b39083 verified 2 days ago

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	---
	license: other
	license_name: qwen
	license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
	base_model: Qwen/Qwen3.5-9B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- agent
	- tool-use
	- skill-selection
	- reinforcement-learning
	- grpo
	---

	# SkillGate-9B

	Policy from "SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents."

	Agent frameworks expose skills — instruction files with a name, a one-line
	description and a body — by progressive disclosure: the agent sees only names and
	descriptions and must open a file to learn what is inside. With thousands of skills in
	a library, which one to read becomes a decision the policy makes mid-episode, and
	outcome-rewarded RL cannot teach it: the tokens naming the chosen skill carry a median
	0.14% of their trajectory's loss weight, and two in five of them receive a negative
	advantage because execution afterwards failed.

	SkillGate partitions one trajectory's token support into two disjoint credit channels:
	outcome credit reaches only execution tokens (the whole skill-read call is removed from
	the task loss), while an action-local advantage reaches exactly the skill-naming
	tokens, positive only when the trajectory's single read is the correct skill.

	## Model

	\| \| \|
	\|---\|---\|
	\| Base \| Qwen3.5-9B \|
	\| Training \| 100 steps on-policy GRPO, 491 tasks, 8 rollouts/prompt, global batch 128, lr 1e-6, KL 3e-5, selector coefficient 0.20 \|
	\| Checkpoint \| `iter_0000099`, the final step (`selection_role: final`) \|
	\| Architecture \| `Qwen3_5ForConditionalGeneration` \|

	## Results (385-trial protocol, 5 agentic benchmarks, 16-candidate slate)

	\| Method \| Overall \| Oracle read \| Misleading read \|
	\|---\|---:\|---:\|---:\|
	\| SFT (RL init) \| 40.8 \| 37.9 \| 61.8 \|
	\| SkillRL (outcome reward only) \| 47.0 \| 54.3 \| 69.6 \|
	\| SkillGate \| 53.2 \| 83.9 \| 21.8 \|

	Same initialisation, data, steps and hyperparameters as the outcome-only row; the only
	difference is which tokens the gradient reaches.

	## Intended use

	Research on agentic skill/tool selection. The model expects the OpenClaw-style prompt
	profile and tool schema used in the paper; see the repository for the exact system
	prompt and the frozen skill slates.

	## Links

	- Paper: [arXiv:2608.18852](https://arxiv.org/abs/2608.18852)
	- Code: https://github.com/DeepExperience/SkillGate

	## License

	Derived from Qwen3.5-9B and distributed under the Qwen license; see `license_link`.