Instructions to use google/gemma-3-1b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3-1b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/gemma-3-1b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-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]:]))

Inference
Local Apps Settings

vLLM

How to use google/gemma-3-1b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3-1b-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": "google/gemma-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-3-1b-it

SGLang

How to use google/gemma-3-1b-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 "google/gemma-3-1b-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": "google/gemma-3-1b-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 "google/gemma-3-1b-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": "google/gemma-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/gemma-3-1b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3-1b-it
```

AttributeError: 'HybridCache' object has no attribute 'float'

by naruto-soop - opened Mar 13, 2025

Discussion

naruto-soop

Mar 13, 2025

Hi, I've encountered an error during evaluation phase:

Traceback (most recent call last):
    train_stats = trainer.train()
                  ^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/trl/trainer/sft_trainer.py", line 361, in train
    output = super().train(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 2250, in train
    return inner_training_loop(
           ^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 2625, in _inner_training_loop
    self._maybe_log_save_evaluate(
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 3098, in _maybe_log_save_evaluate
    metrics = self._evaluate(trial, ignore_keys_for_eval)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 3052, in _evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 4118, in evaluate
    output = eval_loop(
             ^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 4312, in evaluation_loop
    losses, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 4528, in prediction_step
    loss, outputs = self.compute_loss(model, inputs, return_outputs=True)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/transformers/trainer.py", line 3772, in compute_loss
    outputs = model(**inputs)
              ^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 823, in forward
    return model_forward(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 811, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 790, in convert_to_fp32
    return recursively_apply(_convert_to_fp32, tensor, test_type=_is_fp16_bf16_tensor)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 119, in recursively_apply
    {
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 120, in <dictcomp>
    k: recursively_apply(
       ^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 127, in recursively_apply
    return func(data, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/envs/ME/lib/python3.11/site-packages/accelerate/utils/operations.py", line 782, in _convert_to_fp32
    return tensor.float()
           ^^^^^^^^^^^^
AttributeError: 'HybridCache' object has no attribute 'float'

YoungjaeDev

Mar 16, 2025

feliciaAI

Mar 18, 2025

same

lkv

Google org May 14, 2025

Hi @feliciaAI and @YoungjaeDev ,

Sorry for late response. Could you please refer this similar issue. if you have any concerns let us know will assist you.

Thank you.

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