File size: 4,978 Bytes
08153b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/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() |