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#!/usr/bin/env python3
"""
RESON-LLAMA Chat con MEMORIA CONVERSAZIONALE - PULIZIA MINIMALE
"""
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch
import warnings
import re
warnings.filterwarnings("ignore", category=UserWarning)
conversation_turns = []
MAX_MEMORY_TURNS = 4
def load_reson_model(model_path=r"C:\Users\dacan\OneDrive\Desktop\Meta\Reson4.5\Reson4.5"):
print(f"🧠 Caricamento RESON-LLAMA da {model_path}...")
base_model_name = "meta-llama/Llama-2-7b-chat-hf"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=bnb_config,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
use_cache=False,
low_cpu_mem_usage=True
)
model = PeftModel.from_pretrained(base_model, model_path)
print("✅ RESON-LLAMA V4 caricato con memoria!")
return model, tokenizer
def minimal_clean_response(response):
"""Pulizia MINIMALE - rimuove tutto tra parentesi quadre"""
# Rimuovi QUALSIASI cosa tra parentesi quadre [...]
cleaned = re.sub(r'\[.*?\]', '', response)
# Pulizia spazi multipli
cleaned = re.sub(r'[ \t]+', ' ', cleaned)
cleaned = re.sub(r' *\n *', '\n', cleaned)
cleaned = re.sub(r'\n{3,}', '\n\n', cleaned)
cleaned = cleaned.strip()
return cleaned
def format_conversation_prompt(conversation_turns, current_question):
prompt_parts = []
for turn in conversation_turns[-MAX_MEMORY_TURNS:]:
prompt_parts.append(f"[INST] {turn['question']} [/INST] {turn['answer']}")
prompt_parts.append(f"[INST] {current_question} [/INST]")
full_prompt = " ".join(prompt_parts)
return full_prompt
def generate_response(model, tokenizer, prompt):
inputs = tokenizer(
prompt,
return_tensors="pt",
padding=True,
truncation=True,
max_length=2048
)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
input_length = inputs['input_ids'].shape[1]
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=300,
temperature=0.60,
do_sample=True,
top_p=0.94,
top_k=40,
min_p=0.05,
repetition_penalty=1.15,
no_repeat_ngram_size=3,
min_length=60,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
use_cache=True
)
new_tokens = outputs[0][input_length:]
raw_response = tokenizer.decode(new_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False).strip()
# Pulizia minimale - mantieni tutto il contenuto interessante
clean_response = minimal_clean_response(raw_response)
return clean_response
def chat_with_memory(model, tokenizer):
global conversation_turns
conversation_turns = []
print("\n🧠 RESON-LLAMA V4 CHAT CON MEMORIA")
print("Comandi: 'quit' = esci, 'clear' = cancella memoria")
while True:
try:
user_input = input(f"\n🧑 Tu: ").strip()
if user_input.lower() == 'quit':
print("👋 Arrivederci!")
break
elif user_input.lower() == 'clear':
conversation_turns = []
print("🧠 Memoria cancellata!")
continue
if not user_input:
continue
print("🧠 RESON sta riflettendo...")
prompt = format_conversation_prompt(conversation_turns, user_input)
response = generate_response(model, tokenizer, prompt)
print(f"\n🤖 RESON: {response}")
conversation_turns.append({
'question': user_input,
'answer': response
})
if len(conversation_turns) > MAX_MEMORY_TURNS:
conversation_turns = conversation_turns[-MAX_MEMORY_TURNS:]
except KeyboardInterrupt:
print("\n👋 Chat interrotta!")
break
except Exception as e:
print(f"❌ Errore: {e}")
def main():
print("🧠 RESON-LLAMA V4 CON MEMORIA")
model, tokenizer = load_reson_model()
chat_with_memory(model, tokenizer)
if __name__ == "__main__":
main() |