Instructions to use iromu/Gemma3-1B-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iromu/Gemma3-1B-tools with Transformers:

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

pipe = pipeline("text-generation", model="iromu/Gemma3-1B-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iromu/Gemma3-1B-tools")
model = AutoModelForCausalLM.from_pretrained("iromu/Gemma3-1B-tools", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iromu/Gemma3-1B-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iromu/Gemma3-1B-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iromu/Gemma3-1B-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iromu/Gemma3-1B-tools

SGLang

How to use iromu/Gemma3-1B-tools 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 "iromu/Gemma3-1B-tools" \
    --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": "iromu/Gemma3-1B-tools",
		"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 "iromu/Gemma3-1B-tools" \
        --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": "iromu/Gemma3-1B-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iromu/Gemma3-1B-tools with Docker Model Runner:
```
docker model run hf.co/iromu/Gemma3-1B-tools
```

Gemma3 1B Tools

Gemma 3 1B fine-tuned with LoRA for tool calling and agent-style interactions.

Base model

This model was fine-tuned from:

google/gemma-3-1b-it

Training

Training was performed using NVIDIA NeMo AutoModel with LoRA/PEFT.

LoRA configuration

LoRA dimension: 32
LoRA alpha: 32
Dropout: 0.05
Target modules: *.proj (all *_proj linear layers)

Training configuration

Max sequence length: 4096
Learning rate: 5e-5 (cosine decay, 15 warmup steps, min 1e-6)
Weight decay: 0.01
Global batch size: 64 (micro batch 2 x 32 accumulation)
Training steps: 336 (4 epochs)
Mixed precision: bf16
Validation loss: 0.579 → 0.4715 (final epoch)

Dataset

Training used the sft_tools split of the r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation dataset.

Tool-calling format

This model was trained with a custom chat template (bundled as chat_template.jinja). It renders the tool schemas into a developer turn and emits tool calls as:

<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>

Serving stacks must render prompts with this template (e.g. via the tokenizer's apply_chat_template) for tool calling to work.

Intended use

Structured tool/function calling
Agent-style multi-step interactions
Small-footprint on-device or edge deployment

It is not intended to be a general replacement for larger Gemma models.

Usage

Serve the model:

trtllm-serve serve iromu/Gemma3-1B-tools --port 8000

Load it with llama.cpp:

llama-cli -hf iromu/Gemma3-1B-tools-GGUF:Q4_K_M

Validation matrix

Tool-calling validation on the sft_tools validation split (greedy decoding, 384 max new tokens). Throughput is single-stream greedy decode, not serving throughput.

Pretrained base (google/gemma-3-1b-it): 2.0% exact-args match (1/50). Fine-tuned (BF16): 66.0% exact-args match (33/50) (+64pp vs base).

GGUF-BF16: 20/50 (40.0%) exact, 66.0 tok/s — 61% of BF16.
GGUF-Q4_K_M: 10/50 (20.0%) exact, 89.5 tok/s — 30% of BF16.
GGUF-Q5_K_M: 24/50 (48.0%) exact, 60.7 tok/s — 73% of BF16.
GGUF-Q8_0: 22/50 (44.0%) exact, 50.2 tok/s — 67% of BF16.
NVFP4: 2/50 (4.0%) exact, 169.0 tok/s — 6% of BF16.

Model	Quant	n	Tool call emitted	Names match	Exact args match	Δ exact vs BASE	tok/s
Gemma3-1B-tools	BASE (google/gemma-3-1b-it)	50	6/50 (12.0%)	1/50 (2.0%)	1/50 (2.0%)	—	68.5
Gemma3-1B-tools	BF16	50	50/50 (100.0%)	41/50 (82.0%)	33/50 (66.0%)	+64pp	47.1
Gemma3-1B-tools	GGUF-BF16	50	50/50 (100.0%)	36/50 (72.0%)	20/50 (40.0%)	+38pp	66.0
Gemma3-1B-tools	GGUF-Q4_K_M	50	50/50 (100.0%)	19/50 (38.0%)	10/50 (20.0%)	+18pp	89.5
Gemma3-1B-tools	GGUF-Q5_K_M	50	50/50 (100.0%)	34/50 (68.0%)	24/50 (48.0%)	+46pp	60.7
Gemma3-1B-tools	GGUF-Q8_0	50	50/50 (100.0%)	37/50 (74.0%)	22/50 (44.0%)	+42pp	50.2
Gemma3-1B-tools	NVFP4	50	34/50 (68.0%)	2/50 (4.0%)	2/50 (4.0%)	+2pp	169.0

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Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for iromu/Gemma3-1B-tools

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Adapter

(194)

this model

iromu
/

Gemma3-1B-tools

Gemma3 1B Tools

Base model

Training

LoRA configuration

Training configuration

Dataset

Tool-calling format

Intended use

Usage

Validation matrix

Model tree for iromu/Gemma3-1B-tools

Dataset used to train iromu/Gemma3-1B-tools