a3ec1a2556
- Threat Monitor: 7-tab monitoring page (live, connections, network intel, threats, packet capture, DDoS mitigation, counter-attack) with real-time SSE streaming and optimized data collection (heartbeat, cached subprocess calls, bulk process name cache) - Drill-down popups: Every live monitor stat is clickable, opening a popup with detailed data (connections list with per-connection detail view, GeoIP lookup, process kill, bandwidth, ARP spoof, port scan, DDoS status) - Hal agent mode: Chat routes rewritten to use Agent system with create_module tool, SSE streaming of thought/action/result steps - Windows defense module with full security audit - LLM trainer module and routes - Defense landing page with platform-specific sub-pages - Clean up stale files (get-pip.py, download.png, custom_adultsites.json) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""AUTARCH LoRA Training Script (Transformers + PEFT)"""
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import json
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import torch
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from datasets import Dataset
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from transformers import (
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AutoModelForCausalLM, AutoTokenizer, TrainingArguments,
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BitsAndBytesConfig,
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)
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
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from trl import SFTTrainer
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# Quantization config
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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) if True else None
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print("Loading base model: models/Hal_v2.gguf")
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model = AutoModelForCausalLM.from_pretrained(
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"models/Hal_v2.gguf",
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quantization_config=bnb_config,
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device_map="auto",
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trust_remote_code=False,
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)
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tokenizer = AutoTokenizer.from_pretrained("models/Hal_v2.gguf", trust_remote_code=False)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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if True:
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model = prepare_model_for_kbit_training(model)
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# LoRA config
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lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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lora_dropout=0.05,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"],
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# Load dataset
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samples = []
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with open("C:\she\autarch\data\training\autarch_dataset_20260302_202634.jsonl", "r") as f:
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for line in f:
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samples.append(json.loads(line))
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def format_sample(sample):
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if "conversations" in sample:
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msgs = sample["conversations"]
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text = ""
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for msg in msgs:
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role = "user" if msg["from"] == "human" else "assistant"
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text += f"<|im_start|>{role}\n{msg['value']}<|im_end|>\n"
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return {"text": text}
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else:
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return {"text": f"<|im_start|>user\n{sample['instruction']}\n{sample.get('input','')}<|im_end|>\n<|im_start|>assistant\n{sample['output']}<|im_end|>\n"}
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dataset = Dataset.from_list([format_sample(s) for s in samples])
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print(f"Dataset: {len(dataset)} samples")
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# Train
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=dataset,
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dataset_text_field="text",
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max_seq_length=2048,
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args=TrainingArguments(
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output_dir="C:\she\autarch\data\training\output",
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num_train_epochs=3,
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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learning_rate=0.0002,
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warmup_ratio=0.03,
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save_steps=50,
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logging_steps=10,
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fp16=True,
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optim="adamw_8bit",
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report_to="none",
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),
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)
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print("Starting training...")
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trainer.train()
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print("Training complete!")
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# Save
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model.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
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tokenizer.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
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print(f"LoRA adapter saved to C:\she\autarch\data\training\output/lora_adapter")
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