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

File size: 2,463 Bytes

---
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`.