Instructions to use YuyaoGe/SkillForge_Search_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YuyaoGe/SkillForge_Search_7B with Transformers:

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

pipe = pipeline("text-generation", model="YuyaoGe/SkillForge_Search_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YuyaoGe/SkillForge_Search_7B")
model = AutoModelForCausalLM.from_pretrained("YuyaoGe/SkillForge_Search_7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YuyaoGe/SkillForge_Search_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YuyaoGe/SkillForge_Search_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuyaoGe/SkillForge_Search_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YuyaoGe/SkillForge_Search_7B

SGLang

How to use YuyaoGe/SkillForge_Search_7B 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 "YuyaoGe/SkillForge_Search_7B" \
    --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": "YuyaoGe/SkillForge_Search_7B",
		"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 "YuyaoGe/SkillForge_Search_7B" \
        --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": "YuyaoGe/SkillForge_Search_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YuyaoGe/SkillForge_Search_7B with Docker Model Runner:
```
docker model run hf.co/YuyaoGe/SkillForge_Search_7B
```

SkillForge_Search_7B / README.md

Yuyao Ge

Add the model card and the overview figure

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	---
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- agentic-rl
	- llm-agent
	- skill-library
	- grpo
	- search-qa
	---

	# SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles

	[![arXiv](https://img.shields.io/badge/arXiv-2610.09832-b31b1b)](https://arxiv.org/abs/2610.09832)
	[![PDF](https://img.shields.io/badge/PDF-full%20paper-white)](https://arxiv.org/pdf/2610.09832)
	[![Code](https://img.shields.io/badge/Code-SkillForge-181717?logo=github)](https://github.com/YuyaoGe/SkillForge)
	[![NeurIPS 2026](https://img.shields.io/badge/NeurIPS-2026-1783ff)](https://geyuyao.com/skillforge/)
	[![Project page](https://img.shields.io/badge/project-SkillForge-b5613c)](https://geyuyao.com/skillforge/)
	[![SkillForge collection](https://img.shields.io/badge/%F0%9F%A4%97-collection-ffc107)](https://huggingface.co/collections/YuyaoGe/skillforge)
	[![License: MIT](https://img.shields.io/badge/license-MIT-f5de53)](https://huggingface.co/YuyaoGe/SkillForge_Search_7B/blob/main/README.md)

	---

	![SkillForge pipeline: seed skills are pre-retired under the base model, the survivors seed retirement-aware cold-start, then skills and the policy co-evolve through trial, active, stable and retired states.](assets/overview.png)

	## Introduction

	This is the final SkillForge policy for Search-Augmented QA: a full fine-tune of
	`Qwen2.5-7B-Instruct` trained with GRPO while its skill library was simultaneously
	forged — retired, stabilized, demoted and mutated — under the fitness-driven
	lifecycle described in the paper.

	Most memory-augmented agents keep the library append-only. A skill that was
	correct at step 20 encodes a procedure the policy has outgrown by step 120, and it is
	still being retrieved into the context. SkillForge instead scores each skill against
	the policy's own rollouts and moves it between four states — `trial`, `active`,
	`stable`, `retired` — so the library and the model co-evolve.

	On Search-Augmented QA this reaches 48.7% overall accuracy across 51,713 test
	samples, the best of any method compared, against 46.8% for SkillRL and 45.2%
	for ZeroSearch.

	> What is in this repository. The policy weights and tokenizer only. The evolved
	> skill library is not shipped here: the agent is the policy plus the retrieved
	> skills injected into its context. Skill contents, fitness trajectories and
	> retirement events are documented in SkillFurnace (Appendix C of the paper) and in
	> the paper's case studies.

	The same method, two more environments: [ALFWorld](https://huggingface.co/YuyaoGe/SkillForge_Alfworld_7B) and [WebShop](https://huggingface.co/YuyaoGe/SkillForge_Webshop_7B). All three sit in the
	[SkillForge collection](https://huggingface.co/collections/YuyaoGe/skillforge), alongside the paper.

	## Results

	Search-Augmented QA, per dataset (%). \* = in-domain, \\ = out-of-domain. The
	`Overall` column is the sample-weighted micro-average over the same 51,713-sample
	union for every method.

	\| Method \| NQ\* \| TriviaQA\\ \| PopQA\\ \| HotpotQA\* \| 2Wiki\\ \| MuSiQue\\ \| Bamboogle\\ \| Overall \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| RAG \| 27.4 \| 58.2 \| 17.8 \| 25.8 \| 23.2 \| 9.4 \| 16.8 \| 29.4 \|
	\| Search-R1 \| 39.3 \| 61.0 \| 39.7 \| 37.0 \| 40.1 \| 14.6 \| 36.8 \| 42.9 \|
	\| ZeroSearch \| 43.6 \| 61.8 \| 51.5 \| 34.6 \| 35.2 \| 18.4 \| 27.8 \| 45.2 \|
	\| EvolveR \| 43.5 \| 63.4 \| 44.6 \| 38.2 \| 42.0 \| 15.6 \| 54.4 \| 45.8 \|
	\| SkillRL \| 45.9 \| 63.3 \| 45.9 \| 43.2 \| 40.3 \| 20.2 \| 73.8 \| 46.8 \|
	\| SkillForge \| 48.2 \| 65.0 \| 50.1 \| 43.9 \| 40.4 \| 20.3 \| 77.2 \| 48.7 \|

	The largest single gains are out-of-domain: +3.4 on Bamboogle and +5.9 over
	ZeroSearch on MuSiQue. ZeroSearch keeps PopQA, where it leads by 1.4.

	### The library this model ended up with

	\| \| Seed library \| Final library \|
	\|---\|---\|---\|
	\| Total skills \| 41 \| 85 \|
	\| General \| 10 \| — \|
	\| Task-specific \| 20 \| — \|
	\| Common-mistake \| 11 \| — \|

	The run saturates its skill cap (`S_max = 85`). This environment retires the
	fewest skills of the three (59 events against ALFWorld's 138), and the two categories
	that stand out here — redundant with the policy and contradicting the environment —
	account for almost a quarter of them.

	## How it was trained

	1. Pre-retirement. The seed library, inherited from the SkillRL release, is scored
	under 400 rollout episodes of the base model with skills injected. Anything whose
	proto-fitness falls below `delta_pre = 0.3` is retired before training starts.
	2. Retirement-aware cold start. The base model is fine-tuned with cross-entropy on
	the rollouts that survived, with trajectories that leaned on a since-retired skill
	filtered out.
	3. Skill-policy co-evolution. GRPO takes over and, every 10 steps, the forging
	cycle reads each skill's runtime fitness and promotes, demotes, retires or mutates
	it. Mutation is LLM-guided (Kimi-K2.5 as teacher); at most 5 mutations and 3
	retirements per cycle; retrieval is top-6 by task-type match.

	\| Hyperparameter \| Value \|
	\|---\|---\|
	\| Base model \| `Qwen2.5-7B-Instruct` \|
	\| Optimizer \| GRPO, via `verl` \|
	\| Learning rate \| 1e-6 \|
	\| Batch size / group size \| 16 / 8 \|
	\| Clip epsilon / KL beta \| 0.2 / 0.001 \|
	\| Training steps \| 200 \|
	\| Sampling temperature (train and eval) \| 1.0 \|
	\| Hardware \| 64 x NVIDIA H200 \|

	## Quick Start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "YuyaoGe/SkillForge_Search_7B"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

	# Skills are retrieved (top-6, by task-type match) and injected into the context.
	# The exact prompt template and the action space are in the paper.
	messages = [
	{"role": "system", "content": "<retrieved skills for this task type>"},
	{"role": "user", "content": "<observation>\n\n> "},
	]
	ids = tok.apply_chat_template(messages, add_generation_prompt=True,
	return_tensors="pt").to(model.device)
	out = model.generate(ids, max_new_tokens=256, do_sample=False)
	print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))
	```

	The training loop, the skill lifecycle and the environment harnesses are in
	[YuyaoGe/SkillForge](https://github.com/YuyaoGe/SkillForge) — but that is the code, not the runtime library: this
	policy still needs its skills retrieved and injected at call time.

	This is a research artifact: a 7B text policy for a text environment. It emits search
	queries and answers in the Search-R1 action format; the retrieval backend is not part
	of this repository.

	## Citation

	```bibtex
	@misc{ge2026skillforgecoevolvingskillsagents,
	title={SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles},
	author={Yuyao Ge and Yiwei Wang and Yuchen He and Baolong Bi and Lingrui Mei and Jiayu Yao and Lizhe Chen and Shenghua Liu},
	year={2026},
	eprint={2610.09832},
	archivePrefix={arXiv},
	primaryClass={cs.AI},
	url={https://arxiv.org/abs/2610.09832},
	}
	```

	## Acknowledgments

	Training runs on [`verl`](https://github.com/volcengine/verl) for the GRPO loop, with
	seed skill libraries inherited from the SkillRL release.