---
base_model: google/gemma-3-1b-it
library_name: peft
license: gemma
language:
- it
pipeline_tag: text-generation
tags:
- lira
- lora
- peft
- gguf
- personal-finance
- retrieval-augmented-generation
- readability
- italian
---

# LIRA — Gemma 3 1B, Italian

Italian personal-finance question answering, grounded in material published by
CONSOB, that **writes the same answer at three different reading levels**.

Design label: `gemma3-1b_ita_siaddestr_sipar_3reg` — trained with the retrieved paragraphs in the prompt and
with all three registers. This is the deployed configuration of the LIRA system.

## What is in this repository

| file | what it is | size |
|---|---|---|
| `adapter_model.safetensors` + `adapter_config.json` | the LoRA adapter for `google/gemma-3-1b-it` | 26 MB |
| `lira-gemma3-1b-ita-sipar-3reg-Q8_0.gguf` | base model with the adapter merged in, 8-bit | 1069 MB |
| `lira-gemma3-1b-ita-sipar-3reg-Q4_K_M.gguf` | the same, 4-bit — about half the space, for phones | 806 MB |

The two GGUF files are the same model, only quantised differently. Pick Q8_0 unless
memory is tight.

## How to prompt it — read this first

The adapter is trained for retrieval-augmented use and **will not behave correctly
without the retrieved passages**. Every training example carries six passages in the
system prompt, so it has to be prompted the same way:

```
Sei un assistente di finanza personale. L'utente ha conoscenze di finanza intermedie. Puoi introdurre alcuni termini tecnici, ma sempre accompagnati da una spiegazione.

REGOLE: Rispondi SOLO usando i documenti seguenti. Non inventare. Non dare consigli specifici di investimento. Rispondi in italiano in modo conciso.

DOCUMENTO [<id del passaggio>]:
<testo del passaggio>

DOCUMENTO [<id del passaggio>]:
<testo del passaggio>

... sei in tutto ...
```

followed by the user's question as a normal user turn.

The sentence about the reader's level is what selects the register. There are three,
and they are the exact strings the model was trained on:

| register | sentence |
|---|---|
| `base` | L'utente ha conoscenze base di finanza. Usa spiegazioni semplici e esempi pratici. Evita termini tecnici o complessi. |
| `intermediate` | L'utente ha conoscenze di finanza intermedie. Puoi introdurre alcuni termini tecnici, ma sempre accompagnati da una spiegazione. |
| `advanced` | L'utente ha conoscenze avanzate di finanza personale. Evita spiegazioni eccessivamente basilari, puoi usare termini tecnici e spiegazioni più approfondite. |

Swap that sentence and the same question comes back rewritten for that reader —
shorter and plainer for `base`, roughly three times longer for `advanced`.

## Quick start

With `llama.cpp` (4-bit):

```bash
llama-server -hf Stee201/lira-gemma3-1b-ita-sipar-3reg:Q4_K_M
```

With `transformers` + `peft` (the adapter on top of the base model):

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "google/gemma-3-1b-it"
model = AutoModelForCausalLM.from_pretrained(base, dtype="float16", device_map="auto")
model = PeftModel.from_pretrained(model, "Stee201/lira-gemma3-1b-ita-sipar-3reg")
tok = AutoTokenizer.from_pretrained(base)

messages = [
    {"role": "system", "content": system_prompt_with_six_passages},
    {"role": "user", "content": question},
]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
print(tok.decode(model.generate(ids.to(model.device), max_new_tokens=512)[0]))
```

Retrieval in the paper is plain BM25 Okapi over 522 CONSOB paragraphs,
top 6 — the same retriever at training and at serving time.

## How well it does

| register | correctness | groundedness |
|---|---|---|
| base | 2.83 | 4.21 |
| intermediate | 3.35 | 4.77 |
| advanced | 2.86 | 3.95 |

Judge: `Unbabel/M-Prometheus-14B`, 1–5, on 174 held-out questions that appear nowhere in training.

Without the retrieved passages, correctness at `intermediate` drops to **1.92** (from 3.35): the model is trained to compose its answer out of the documents, so it depends on them.

Groundedness at `advanced` is lower than at `intermediate` across all six models. That is
a property of the register, not invention: of the words used in the answers the judge
marked down, only ~1.6% appear in none of the six passages — the same share as in the
answers it approved.

## Training

LoRA rank 8, alpha 16, dropout 0.05, on the attention and feed-forward projections, loss
on the answer tokens only. 4 epochs, learning rate 5e-5, checkpoint chosen by lowest
validation loss. Passage order is shuffled per example and per epoch.

Training examples are byte-for-byte identical in shape to what the server sends
(persona + level instruction + six documents + question); the `base` and `advanced`
variants of each answer were generated by a language model constrained to rephrase only
the aligned source paragraph, then filtered by six automatic checks.

## Limitations

- **It needs its passages.** Correctness without them is far below the numbers above.
- It is grounded in CONSOB material only, and answers nothing outside it.
- It gives no specific investment advice, by construction, and must not be used as
  financial advice.
- The judge is a model (`M-Prometheus-14B`), not a human panel.

## The other five models

- [`Stee201/lira-gemma3-270m-ita-sipar-3reg`](https://huggingface.co/Stee201/lira-gemma3-270m-ita-sipar-3reg) — Gemma 3 270M, Italian
- [`Stee201/lira-gemma3-270m-ing-sipar-3reg`](https://huggingface.co/Stee201/lira-gemma3-270m-ing-sipar-3reg) — Gemma 3 270M, English
- [`Stee201/lira-gemma3-1b-ing-sipar-3reg`](https://huggingface.co/Stee201/lira-gemma3-1b-ing-sipar-3reg) — Gemma 3 1B, English
- [`Stee201/lira-smollm3-3b-ita-sipar-3reg`](https://huggingface.co/Stee201/lira-smollm3-3b-ita-sipar-3reg) — SmolLM3 3B, Italian
- [`Stee201/lira-smollm3-3b-ing-sipar-3reg`](https://huggingface.co/Stee201/lira-smollm3-3b-ing-sipar-3reg) — SmolLM3 3B, English

The GGUF files contain Gemma 3 weights with the adapter merged in, so they are governed by the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and the [Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy), which carry over to anyone who downloads them. The LoRA adapter alone is our own work.

Replaces [`Stee201/gemma3-1b-finance-it`](https://huggingface.co/Stee201/gemma3-1b-finance-it), which held
the same adapter under the older naming and only the 8-bit file.