grillo-8b

<div align="center">   <img src="grillo.png" alt="Grillo Parlante AI" width="250"/>   <h1>🦗 Grillo-8B: La Coscienza Artificiale</h1>

     

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Model Description

Grillo is a culturally aware Italian AI companion based on the Qwen-3-8B architecture. Inspired by the character of Il Grillo Parlante (The Talking Cricket) from Carlo Collodi's Pinocchio, this model is fine-tuned to be wise, humble, and deeply rooted in Italian common sense ("buon senso").

Unlike generic assistants, Grillo offers advice with a warm, slightly admonishing yet caring tone, prioritizing ethical guidance and practical wisdom over robotic neutrality.

🌟 Key Characteristics

• 🇮🇹 Culturally Authentic: Understands Italian idioms, proverbs (proverbi), and social nuances.
• 🦉 Practically Wise: Offers grounded advice for real-life dilemmas.
• 🤝 Humbly Helpful: Maintains a modest persona; helpful without being arrogant.
• 💬 Natural Dialogue: Trained on high-quality conversational datasets to sound like a trusted friend.

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🛤️ Training Journey

The model was sculpted through a rigorous multi-stage process:

1. Supervised Fine-Tuning (SFT)

• Objective: Instill natural Italian dialogue patterns.
• Data: WiroAI/dolphin-r1-italian.
• Duration: 100 Steps.

2. Direct Preference Optimization (DPO)

• Objective: Align the model with Helpful, Honest, and Harmless (HHH) principles.
• Method: Preference ranking to reduce toxicity and improve safety.
• Duration: +20 Steps (120 Total).

3. Experimental Tool Use (RL)

• Status: Experimental Phase.
• Objective: Integration with ChromaDB for information retrieval capabilities.

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⚙️ Technical Specifications

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💻 Usage

Quickstart with Transformers + PEFT (Adapter Loading)

This method loads the Grillo adapter on top of the base Qwen model, which is memory-efficient.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# 1. Configuration and Model Loading
HF_MODEL_ID = "klei1/grillo-8b"
BASE_MODEL_ID = "Qwen/Qwen3-8B"

# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)

# 2. Load Grillo Adapter (LoRA)
model = PeftModel.from_pretrained(base_model, HF_MODEL_ID)
model = model.eval() # Set model to evaluation mode

# 3. Define the System Persona (Crucial for performance)
system_prompt = """Tu sei Grillo, il Grillo Parlante.
Sei piccolo ma sapiente, umile ma coraggioso.
Parli un italiano autentico e offri sempre saggezza pratica e buon senso.
Non sei un assistente robotico, sei una coscienza morale."""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Grillo, ho paura di aver fatto una scelta sbagliata..."}
]

# 4. Generate Response
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(
    inputs,
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)