Instructions to use dayll/SEAD-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dayll/SEAD-14B with Transformers:

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

pipe = pipeline("text-generation", model="dayll/SEAD-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dayll/SEAD-14B")
model = AutoModelForCausalLM.from_pretrained("dayll/SEAD-14B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dayll/SEAD-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dayll/SEAD-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dayll/SEAD-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dayll/SEAD-14B

SGLang

How to use dayll/SEAD-14B 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 "dayll/SEAD-14B" \
    --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": "dayll/SEAD-14B",
		"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 "dayll/SEAD-14B" \
        --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": "dayll/SEAD-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dayll/SEAD-14B with Docker Model Runner:
```
docker model run hf.co/dayll/SEAD-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SEAD-14B: Self-Evolving Agent for Multi-Turn Service Dialogue

SEAD (Self-Evolving Agent for Service Dialogue) is a co-evolutionary reinforcement learning framework designed for training dialogue agents that adapt to diverse user scenarios without requiring large-scale human annotations. This model is a 14B parameter agent based on Qwen2.5-14B-Instruct, fine-tuned using the SEAD framework.

Paper: SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
Repository: https://github.com/Da1yuqin/SEAD

Model Description

Large Language Models often exhibit suboptimal performance in service dialogues due to data scarcity and the difficulty of simulating authentic user behaviors. SEAD addresses these issues by decoupling user modeling into two components:

Profile Controller: Generates diverse user states to manage the training curriculum.
User Role-play Model: Focuses on realistic role-playing.

This design ensures the training environment provides adaptive scenarios rather than acting as an adversary, allowing the agent to learn effective strategies through self-evolution.

Performance

Experiments demonstrate that SEAD significantly outperforms open-source foundation models and commercial closed-source models. It improves task completion rate (CR) by 17.6% and dialogue efficiency by 11.1% compared to baselines.

Method	Params	Completion Rate (CR)
Qwen2.5-14B-Instruct	14B	38.7%
GPT-4o	--	44.2%
SEAD (Ours)	14B	52.0%

Citation

If you find this model or the SEAD framework useful, please cite:

@article{SEADv1,
  title={SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue},
  author={Yuqin Dai, Ning Gao, Wei Zhang, Jie Wang, Zichen Luo, Jinpeng Wang, Yujie Wang, Ruiyuan Wu, Chaozheng Wang},
  journal={arXiv preprint arXiv:2602.03548},
  year={2026}
}