Instructions to use xorslider/FoodExtract-gemma-3-270m-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xorslider/FoodExtract-gemma-3-270m-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="xorslider/FoodExtract-gemma-3-270m-finetuned")
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("xorslider/FoodExtract-gemma-3-270m-finetuned")
model = AutoModelForCausalLM.from_pretrained("xorslider/FoodExtract-gemma-3-270m-finetuned", 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 xorslider/FoodExtract-gemma-3-270m-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xorslider/FoodExtract-gemma-3-270m-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xorslider/FoodExtract-gemma-3-270m-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xorslider/FoodExtract-gemma-3-270m-finetuned

SGLang

How to use xorslider/FoodExtract-gemma-3-270m-finetuned 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 "xorslider/FoodExtract-gemma-3-270m-finetuned" \
    --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": "xorslider/FoodExtract-gemma-3-270m-finetuned",
		"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 "xorslider/FoodExtract-gemma-3-270m-finetuned" \
        --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": "xorslider/FoodExtract-gemma-3-270m-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xorslider/FoodExtract-gemma-3-270m-finetuned with Docker Model Runner:
```
docker model run hf.co/xorslider/FoodExtract-gemma-3-270m-finetuned
```

FoodExtract: Fine-Tuned Gemma 3 270M

A fine-tuned version of google/gemma-3-270m-it trained to extract food and drink items from raw text.

What It Does

Given any text input, this model outputs a structured extraction:
food_or_drink: 1
tags: fi, di
foods: eggs, bacon, toast
drinks: orange juice
If the input contains no food or drink items:
food_or_drink: 0
tags:
foods:
drinks:

Tag Reference

Tag	Meaning
np	nutrition_panel
il	ingredient_list
me	menu
re	recipe
fi	food_items
di	drink_items
fa	food_advertisement
fp	food_packaging

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "xorslider/FoodExtract-gemma-3-270m-finetuned"
model     = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe      = pipeline("text-generation", model=model, tokenizer=tokenizer)
input_text = "A bowl of ramen with soft-boiled eggs, nori, and a side of green tea."
formatted  = pipe.tokenizer.apply_chat_template(
    [&#123"role": "user", "content": input_text&#125],
    tokenize=False,
    add_generation_prompt=True
)
output = pipe(formatted, max_new_tokens=128)
print(output[0]["generated_text"][len(formatted):])

Training

Base model: google/gemma-3–270m-it
Dataset: mrdbourke/FoodExtract-1k (1,136 training samples)
Method: Full supervised fine-tuning (SFT) via Hugging Face TRL
Epochs: 3
Batch size: 16
Learning rate: 5e-5

Downloads last month: 369

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for xorslider/FoodExtract-gemma-3-270m-finetuned

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

(1178)

this model