Instructions to use phanviethoang1512/Qwen3-8B-InfoPO-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phanviethoang1512/Qwen3-8B-InfoPO-Agent with Transformers:

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

pipe = pipeline("text-generation", model="phanviethoang1512/Qwen3-8B-InfoPO-Agent")
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("phanviethoang1512/Qwen3-8B-InfoPO-Agent")
model = AutoModelForCausalLM.from_pretrained("phanviethoang1512/Qwen3-8B-InfoPO-Agent", 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 phanviethoang1512/Qwen3-8B-InfoPO-Agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "phanviethoang1512/Qwen3-8B-InfoPO-Agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "phanviethoang1512/Qwen3-8B-InfoPO-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/phanviethoang1512/Qwen3-8B-InfoPO-Agent

SGLang

How to use phanviethoang1512/Qwen3-8B-InfoPO-Agent 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 "phanviethoang1512/Qwen3-8B-InfoPO-Agent" \
    --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": "phanviethoang1512/Qwen3-8B-InfoPO-Agent",
		"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 "phanviethoang1512/Qwen3-8B-InfoPO-Agent" \
        --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": "phanviethoang1512/Qwen3-8B-InfoPO-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use phanviethoang1512/Qwen3-8B-InfoPO-Agent with Docker Model Runner:
```
docker model run hf.co/phanviethoang1512/Qwen3-8B-InfoPO-Agent
```

Qwen3-8B-InfoPO-Agent

The InfoPO baseline from MIMESIS: Qwen3-8B trained with InfoPO, which assigns turn-level credit according to the information gained from interaction, with MIMESIS-9B as the training user simulator.

Paper · Code · Project page · Collection

Results

Mean task score of the five Qwen3-8B agents from the paper across eight UserRL environments, three of them held out from training (IntentionGym, TelepathyGym, SearchGym), under nine evaluation user models that were not used in training.

Agent	Training user	Objective	Mean score
Base	none	SFT	18.49
UserRL	GPT-5.5	GRPO	26.10
InfoPO (this model)	MIMESIS-9B	InfoPO	26.65
UserRL+	MIMESIS-9B	GRPO	29.54
CSD	MIMESIS-9B	GRPO + CSD	31.09

InfoPO and CSD use the same training user, so they differ in their optimization objectives rather than in simulator choice. Each score is the mean over the nine evaluation users; per-user and per-environment scores are in Appendix D of the paper.

Training

RL starts from the Base checkpoint, Qwen3-8B after a supervised warm start, and trains on five UserRL environments: TravelGym, TurtleGym, FunctionGym, TauGym and PersuadeGym. The full configuration is in Appendix D of the paper, and the training scripts are in the agent directory of the code release.

Usage

The model loads like Qwen3-8B. To evaluate it in the eight environments, install the agent code, serve a user model as described there, and run from the agent directory:

bash scripts/run_eval.sh phanviethoang1512/Qwen3-8B-InfoPO-Agent infopo

Citation

@article{phan2026mimesis,
  title   = {{MIMESIS}: Learning User Simulators as Training Environments for Interactive Agents},
  author  = {Phan, Hoang and Huynh, Dat and Zhmoginov, Andrey and Zeng, Qi and Mu, Wancen and Cao, Yue and Bi, Shengjie and He, Yun and Oh, Changdae and Lei, Deren},
  journal = {arXiv preprint arXiv:2610.09484},
  year    = {2026}
}