Instructions to use Stee201/lira-gemma3-1b-ita-sipar-3reg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it")
model = PeftModel.from_pretrained(base_model, "Stee201/lira-gemma3-1b-ita-sipar-3reg")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Use Docker

docker model run hf.co/Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

LM Studio
Jan

vLLM

How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Stee201/lira-gemma3-1b-ita-sipar-3reg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Stee201/lira-gemma3-1b-ita-sipar-3reg",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Ollama
How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with Ollama:
```
ollama run hf.co/Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with Docker Model Runner:
```
docker model run hf.co/Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
```

Lemonade

How to use Stee201/lira-gemma3-1b-ita-sipar-3reg with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M

Run and chat with the model

lemonade run user.lira-gemma3-1b-ita-sipar-3reg-Q4_K_M

List all available models

lemonade list

Atomic Chat

lira-gemma3-1b-ita-sipar-3reg / tokenizer.json

Stee201

LoRA adapter and tokenizer

9e0ff4a verified 8 days ago

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