Instructions to use Stee201/gemma3-1b-finance-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Stee201/gemma3-1b-finance-it 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/gemma3-1b-finance-it") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Stee201/gemma3-1b-finance-it 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/gemma3-1b-finance-it:Q8_0 # Run inference directly in the terminal: llama cli -hf Stee201/gemma3-1b-finance-it:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Stee201/gemma3-1b-finance-it:Q8_0 # Run inference directly in the terminal: llama cli -hf Stee201/gemma3-1b-finance-it:Q8_0
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/gemma3-1b-finance-it:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Stee201/gemma3-1b-finance-it:Q8_0
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/gemma3-1b-finance-it:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Stee201/gemma3-1b-finance-it:Q8_0
Use Docker
docker model run hf.co/Stee201/gemma3-1b-finance-it:Q8_0
- LM Studio
- Jan
- Ollama
How to use Stee201/gemma3-1b-finance-it with Ollama:
ollama run hf.co/Stee201/gemma3-1b-finance-it:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use Stee201/gemma3-1b-finance-it with Docker Model Runner:
docker model run hf.co/Stee201/gemma3-1b-finance-it:Q8_0
- Lemonade
How to use Stee201/gemma3-1b-finance-it with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Stee201/gemma3-1b-finance-it:Q8_0
Run and chat with the model
lemonade run user.gemma3-1b-finance-it-Q8_0
List all available models
lemonade list
- Atomic Chat
gemma3-1b-finance-it
LoRA adapter for google/gemma-3-1b-it, fine-tuned for Italian personal-finance question answering grounded in material published by financial market authorities.
What it expects
This adapter is trained for retrieval-augmented use, and it will not behave correctly without the retrieved passages: the training examples always contain six passages in the system prompt, so the adapter must be prompted the same way. The system prompt has the form
Sei un assistente di finanza personale. L'utente ha conoscenze di finanza intermedie.
RULES: ...
DOCUMENT [passage-id]:
<passage text>
... six passages in total ...
followed by the user's question. The persona sentence states the reader's level, and it is what selects the register: the adapter was trained on three of them (basic, intermediate, advanced), so the same question returns an answer written at the requested level.
Training
LoRA rank 8, alpha 16, dropout 0.05, on the attention and feed-forward projections, loss on answer tokens only. 4 epochs, learning rate 5e-5, checkpoint selected by lowest validation loss.
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