Instructions to use planeB/user_gemma_3_27b_it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use planeB/user_gemma_3_27b_it with Transformers:

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

pipe = pipeline("image-text-to-text", model="planeB/user_gemma_3_27b_it")
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("planeB/user_gemma_3_27b_it")
model = AutoModelForMultimodalLM.from_pretrained("planeB/user_gemma_3_27b_it", 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 planeB/user_gemma_3_27b_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_27b_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_27b_it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/planeB/user_gemma_3_27b_it

SGLang

How to use planeB/user_gemma_3_27b_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_27b_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_27b_it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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_27b_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_27b_it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use planeB/user_gemma_3_27b_it with Docker Model Runner:
```
docker model run hf.co/planeB/user_gemma_3_27b_it
```

Model Card for user_gemma_3_27b_it

This model is a fine-tuned version of None. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.24.0
Transformers: 4.57.1
Pytorch: 2.9.0
Datasets: 4.2.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

@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}
}

Downloads last month: 26

Safetensors

Model size

27B params

Tensor type

F32

Collection including planeB/user_gemma_3_27b_it

Gemma 3 - User Simulators

Collection

5 items • Updated Dec 13, 2025