Instructions to use shorecode/gemma-3-svg-generator-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shorecode/gemma-3-svg-generator-lora with Transformers:

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

pipe = pipeline("text-generation", model="shorecode/gemma-3-svg-generator-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shorecode/gemma-3-svg-generator-lora")
model = AutoModelForCausalLM.from_pretrained("shorecode/gemma-3-svg-generator-lora")
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 shorecode/gemma-3-svg-generator-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/shorecode/gemma-3-svg-generator-lora

SGLang

How to use shorecode/gemma-3-svg-generator-lora 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 "shorecode/gemma-3-svg-generator-lora" \
    --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": "shorecode/gemma-3-svg-generator-lora",
		"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 "shorecode/gemma-3-svg-generator-lora" \
        --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": "shorecode/gemma-3-svg-generator-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shorecode/gemma-3-svg-generator-lora with Docker Model Runner:
```
docker model run hf.co/shorecode/gemma-3-svg-generator-lora
```

Access Gemma on Hugging Face

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately.

Model Card for gemma-3-svg-generator

This model has been discontinued. The conclusion is that it's likely necessary to train a tokenizer for this task, which implies starting a model from scratch.

This model is a fine-tuned version of google/gemma-3-1b-it. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "Generate code for a SVG (Scalable Vector Graphics) of a cat"
generator = pipeline("text-generation", model="shorecode/gemma-3-svg-generator", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=512, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.25.0
Transformers: 4.57.1
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for shorecode/gemma-3-svg-generator-lora

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Finetuned

(551)

this model