---
language:
- it
license: cc-by-nc-4.0
tags:
- sft
- it
- mistral
- chatml
- axolotl
prompt_template: <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|>
  <|im_start|>assistant
model-index:
- name: maestrale-chat-v0.4-beta
  results: []
---
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</div>
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# Maestrale chat beta ༄

By @efederici and @mferraretto

## Model description

- **Language Model**: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus, merged with [occiglot](https://huggingface.co/occiglot/occiglot-7b-eu5). 
- **Fine-Tuning**: SFT performed on 1.7M convs/instructions for 2 epochs.
- **DPO**: Aligned with DPO on multiple datasets.

**v0.4**
- Agent
- Improved truthfullness
- Improved Math & Reasoning capabilities
- Mermaid mindmaps
- More latin translations, poems, ...

This model uses ChatML prompt format:
```
<|im_start|>system
Sei un assistente utile.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Scores

|   Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|------------|------:|------|-----:|--------|-----:|---|-----:|
|hellaswag_it|      1|none  |     0|acc     |0.5270|±  |0.0052|
|            |       |none  |     0|acc_norm|0.7037|±  |0.0048|
|arc_it      |      1|none  |     0|acc     |0.1771|±  |0.0112|
|            |       |none  |     0|acc_norm|0.5218|±  |0.0146|
|m_mmlu_it   |      0|none  |     5|acc     |0.5623|±  |0.0043|

## Usage:

```python
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    GenerationConfig,
    TextStreamer
)
import torch

tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.4-beta")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.4-beta", load_in_8bit=True, device_map="auto")

gen = GenerationConfig(
    do_sample=True,
    temperature=0.7,
    repetition_penalty=1.2,
    top_k=50,
    top_p=0.95,
    max_new_tokens=500,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
)

streamer = TextStreamer(tokenizer, skip_prompt=True)

messages = [
    {"role": "system", "content": "Sei un assistente utile."},
    {"role": "user", "content": "{prompt}"}
]

with torch.no_grad():
    temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(temp, return_tensors="pt").to("cuda")

    _ = model.generate(
        **inputs,
        streamer=streamer,
        generation_config=gen
    )
```

## Examples

### Mindmaps

```python
messages = [
  {"role": "system", "content": "Fornisci una mindmap Mermaid sull'argomento in input."},
  {"role": "user", "content": "Argomento: [argomento]"}
]
```

### SQL

```python
schema = "[db schema]"
messages = [
  {"role": "system", "content": f"Sei un assistente SQL e il tuo compito è convertire la domanda dell'utente in codice SQL valido rispetto allo schema del database fornito.\n\nSchema:\n```sql\n{schema}\n```"},
  {"role": "user", "content": "Conta il numero di X prodotti dall'azienda Y"}
]
```

### Article from index

```python
messages = [
  {"role": "system", "content": "Sei un assistente utile."},
  {"role": "user", "content": (
    "Scrivi un articolo a partire dal titolo e dall'indice dei contenuti.\n\n"
    "Titolo: [titolo]\n\n"
    "Indice:\n\n"
    "1. Introduzione\n"
    "2. [heading]\n"
    "..."
  )}
]
```

## Intended uses & limitations

It's a beta version; it's quite `safe`, and it can refuse to answer to toxic questions.

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)