Instructions to use planeB/user_gemma_3_270m_it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use planeB/user_gemma_3_270m_it with Transformers:

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

pipe = pipeline("text-generation", model="planeB/user_gemma_3_270m_it")
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("planeB/user_gemma_3_270m_it")
model = AutoModelForCausalLM.from_pretrained("planeB/user_gemma_3_270m_it", 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 planeB/user_gemma_3_270m_it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "planeB/user_gemma_3_270m_it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "planeB/user_gemma_3_270m_it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/planeB/user_gemma_3_270m_it

SGLang

How to use planeB/user_gemma_3_270m_it 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 "planeB/user_gemma_3_270m_it" \
    --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": "planeB/user_gemma_3_270m_it",
		"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 "planeB/user_gemma_3_270m_it" \
        --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": "planeB/user_gemma_3_270m_it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use planeB/user_gemma_3_270m_it with Docker Model Runner:
```
docker model run hf.co/planeB/user_gemma_3_270m_it
```

user_gemma_3_270m_it

A user simulator for evaluating agentic conversational recommender systems. SFT fine-tune of google/gemma-3-270m-it on the teacher–student corpus described in Simulating Diverse User Behavioral Stereotypes for Evaluating Agentic Conversational Recommenders (RecSys '26).

Given a behavioral stereotype, a user's interaction history, and a target item, the model generates the user turns of a dialogue with a recommender system. It exists to evaluate recommenders against simulated users who express vague or evolving preferences, rather than perfectly specified queries.

Behavioral stereotypes

Prompt label	Paper label	Behavior
`SPECIFIC`	Direct	states explicit constraints from the first turn
`VAGUE`	Vague-Reactive	stays vague, reacts without adding constraints
`VAGUE_TO_SPECIFIC`	Vague-Proactive	starts vague, then learns to supply explicit filters

Prompt format

The model was trained on a single format and needs it to behave as intended:

<bos><start_of_turn>user
You are an advanced user simulator. Your objective is to generate realistic and coherent user responses in a dialogue with a recommendation system based on the user persona, interaction history and target item provided.
[...]
# Category
Electronics

# User Persona
VAGUE_TO_SPECIFIC

# Interaction history
## Item Name: ...
## Description: ...
## Visual Description: ...
## Review: ...

# Target item
## Item Name: ...
## Description: ...
## Visual Description: ...
## Review: ...

Generate the first user message based on the above information.<end_of_turn>
<start_of_turn>model

Product images enter only as the textual ## Visual Description: the simulator does not consume pixels.

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("planeB/user_gemma_3_270m_it")
model = AutoModelForCausalLM.from_pretrained("planeB/user_gemma_3_270m_it", device_map="auto")

prompt = build_prompt(category, persona, history, target)   # in the format above
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device),
                     max_new_tokens=128)
print(tok.decode(out[0][len(tok(prompt).input_ids):], skip_special_tokens=True))

Fully materialized prompts, identical to the training ones, are in the corpus_sft_simulator config of swap-uniba/ecommerce-user-sim.

Training data

A teacher–student corpus of 10,644 dialogues across 10 Amazon domains, yielding 50,495 SFT examples. The released checkpoints were trained on exactly this corpus, published as the corpus_sft_simulator config. Data, evaluation protocol, and pipeline: swap-uniba/ecommerce-user-sim. Code: https://github.com/swapUniba/Simulating-Diverse-User-Behavioral-Stereotypes

LoRA: rank 128, alpha 32, dropout 0.1, 7 target modules

Framework versions

TRL 0.24.0 · Transformers 4.57.1 · PyTorch 2.9.0 · Datasets 4.2.0 · Tokenizers 0.22.1

Other checkpoints

Model	Repository
Gemma-3 270M	`planeB/user_gemma_3_270m_it`
Gemma-3 1B	`planeB/user_gemma_3_1b_it`
Gemma-3 4B	`planeB/user_gemma_3_4b_it`
Gemma-3 12B	`planeB/user_gemma_3_12b_it`
Gemma-3 27B	`planeB/user_gemma_3_27b_it`

License

Weights derive from Gemma-3 and are subject to the Gemma license terms. The training data is released separately under CC BY-NC 4.0.

Citation

@inproceedings{PetruzzelliMartinaSimulating,
  author    = {Petruzzelli, Alessandro and Martina, Alessandro Francesco Maria and Musto, Cataldo and de Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  editor    = {Konstan, Joseph A. and Karypis, George and Adomavicius, Gediminas and Chen, Minmin and Goethals, Bart and Willemsen, Martijn C.},
  title     = {{Simulating Diverse User Behavioral Stereotypes for Evaluating Agentic Conversational Recommenders}},
  booktitle = {Proceedings of the 20th {ACM} Conference on Recommender Systems, RecSys 2026, Minneapolis, Minnesota, USA, September 28-October 2, 2026},
  publisher = {{ACM}},
  year      = {2026},
  doi       = {10.1145/3773078.3831840},
  url       = {https://doi.org/10.1145/3773078.3831840}
}

The simulator is also used in:

@article{BeyondLookup,
  author     = {Petruzzelli, Alessandro and Martina, Alessandro Francesco Maria and Musto, Cataldo and de Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  journal    = {Information Systems Frontiers},
  title      = {Beyond the Lookup: Simulating Realistic User Uncertainty for the Evaluation of Conversational Agentic Recommenders},
  year       = {2026},
  url        = {https://doi.org/10.1007/s10796-026-10787-3}
}