Maestrale Chat V0.4 Beta

🚀 Maestrale chat beta ༄

Maestrale chat beta ༄ is a language model tailored for the Italian language, offering enhanced capabilities in various aspects such as truthfulness, math, and reasoning. It uses the ChatML prompt format and is suitable for a range of applications.

📦 Model Information

✨ Features

• Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus, merged with occiglot.
• Fine-Tuning: SFT performed on 1.7M convs/instructions for 2 epochs.
• DPO: Aligned with DPO on multiple datasets.
• v0.4 Enhancements: Agent, improved truthfullness, improved Math & Reasoning capabilities, Mermaid mindmaps, more latin translations, poems, etc.

📊 Scores

💻 Usage Examples

Basic Usage

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
    )

Advanced Usage

Mindmaps

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

SQL

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

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.

📄 License

This model is licensed under cc-by-nc-4.0.