File size: 3,732 Bytes
068bc7f | 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 | #!/usr/bin/env python3
"""
Stack 2.9 - Silent Loading (No Progress Bar)
"""
import os
import sys
# Disable progress bars
os.environ['HF_HUB_DISABLE_PROGRESS_BARS'] = '1'
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = '1'
import torch
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent / "src"))
from enhancements.nlp import IntentDetector
from enhancements.knowledge_graph import RAGEngine
from enhancements.emotional_intelligence import SentimentAnalyzer
from enhancements.collaboration import ConversationStateManager
from enhancements.learning import PerformanceMonitor
def load_model_silently():
"""Load model completely silently"""
model_path = Path("/Users/walidsobhi/stack-2-9-final-model")
import json
# Load tokenizer
from transformers import PreTrainedTokenizerFast
tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(model_path / "tokenizer.json"))
tokenizer.pad_token = "<|endoftext|>"
tokenizer.eos_token = "<|endoftext|>"
# Load config
with open(model_path / "config.json") as f:
config_dict = json.load(f)
# Create model config
from transformers import AutoConfig
config = AutoConfig.from_json_file(str(model_path / "config.json"))
# Load weights silently using torch directly
print("Loading weights...", flush=True)
# Use torch.load_file which is silent
with open(model_path / "model.safetensors", 'rb') as f:
import io
# Read entire file into memory first (silently)
buffer = io.BytesIO(f.read())
# Load using safetensors (no progress bar)
from safetensors.torch import load_file
state_dict = load_file(str(model_path / "model.safetensors"))
# Build model
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_config(config)
model.load_state_dict(state_dict, strict=False)
model = model.to(torch.float16)
if torch.cuda.is_available():
model.to("cuda")
print("Done loading!\n", flush=True)
return model, tokenizer
def main():
print("Stack 2.9 - Silent Mode")
print("=" * 40 + "\n")
# Init modules
intent_detector = IntentDetector()
rag_engine = RAGEngine()
sentiment_analyzer = SentimentAnalyzer()
conv_manager = ConversationStateManager()
perf_monitor = PerformanceMonitor()
rag_engine.add_document("intro", "Stack 2.9 is an AI coding assistant")
conv_manager.create_session()
perf_monitor.increment_session_count()
# Load model once
model, tokenizer = load_model_silently()
while True:
try:
user_input = input("You: ").strip()
if not user_input:
continue
if user_input.lower() in ['quit', 'exit', 'q']:
break
# Generate
prompt = f"You are Stack 2.9, expert coder.\n\nUser: {user_input}\nAssistant:"
inputs = tokenizer(prompt, return_tensors='pt')
if torch.cuda.is_available():
inputs = {k: v.cuda() for k, v in inputs.items()}
outputs = model.generate(
**inputs,
max_new_tokens=80,
temperature=0.4,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "Assistant:" in response:
response = response.split("Assistant:")[-1].strip()
print(f"AI: {response}\n")
perf_monitor.increment_message_count()
except KeyboardInterrupt:
break
print(f"Session complete: {perf_monitor.get_session_stats()['total_messages']} messages")
if __name__ == "__main__":
main() |