Instructions to use thedeba/gemma3-1b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thedeba/gemma3-1b-it with Transformers:

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

pipe = pipeline("text-generation", model="thedeba/gemma3-1b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thedeba/gemma3-1b-it")
model = AutoModelForCausalLM.from_pretrained("thedeba/gemma3-1b-it")
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 thedeba/gemma3-1b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thedeba/gemma3-1b-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": "thedeba/gemma3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thedeba/gemma3-1b-it

SGLang

How to use thedeba/gemma3-1b-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 "thedeba/gemma3-1b-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": "thedeba/gemma3-1b-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 "thedeba/gemma3-1b-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": "thedeba/gemma3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thedeba/gemma3-1b-it with Docker Model Runner:
```
docker model run hf.co/thedeba/gemma3-1b-it
```

gemma3-1b-it

Commit History

Upload tokenizer

1a04a2d
verified

Debashish Roy commited on Aug 15, 2025

Upload Gemma3ForCausalLM

0867269
verified

Debashish Roy commited on Aug 15, 2025

initial commit

2a05dcb
verified

Debashish Roy commited on Aug 15, 2025

Commit History

Upload tokenizer 1a04a2d verified

Upload Gemma3ForCausalLM 0867269 verified

initial commit 2a05dcb verified

Upload tokenizer

1a04a2d
verified

Upload Gemma3ForCausalLM

0867269
verified

initial commit

2a05dcb
verified