Autarch Will Control The Internet

data/training/train_lora.py

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+#!/usr/bin/env python3
+"""AUTARCH LoRA Training Script (Transformers + PEFT)"""
+import json
+import torch
+from datasets import Dataset
+from transformers import (
+    AutoModelForCausalLM, AutoTokenizer, TrainingArguments,
+    BitsAndBytesConfig,
+)
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+from trl import SFTTrainer
+
+# Quantization config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+) if True else None
+
+print("Loading base model: models/Hal_v2.gguf")
+model = AutoModelForCausalLM.from_pretrained(
+    "models/Hal_v2.gguf",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=False,
+)
+tokenizer = AutoTokenizer.from_pretrained("models/Hal_v2.gguf", trust_remote_code=False)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+if True:
+    model = prepare_model_for_kbit_training(model)
+
+# LoRA config
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                     "gate_proj", "up_proj", "down_proj"],
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+# Load dataset
+samples = []
+with open("C:\she\autarch\data\training\autarch_dataset_20260302_202634.jsonl", "r") as f:
+    for line in f:
+        samples.append(json.loads(line))
+
+def format_sample(sample):
+    if "conversations" in sample:
+        msgs = sample["conversations"]
+        text = ""
+        for msg in msgs:
+            role = "user" if msg["from"] == "human" else "assistant"
+            text += f"<|im_start|>{role}\n{msg['value']}<|im_end|>\n"
+        return {"text": text}
+    else:
+        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"}
+
+dataset = Dataset.from_list([format_sample(s) for s in samples])
+print(f"Dataset: {len(dataset)} samples")
+
+# Train
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=2048,
+    args=TrainingArguments(
+        output_dir="C:\she\autarch\data\training\output",
+        num_train_epochs=3,
+        per_device_train_batch_size=4,
+        gradient_accumulation_steps=4,
+        learning_rate=0.0002,
+        warmup_ratio=0.03,
+        save_steps=50,
+        logging_steps=10,
+        fp16=True,
+        optim="adamw_8bit",
+        report_to="none",
+    ),
+)
+
+print("Starting training...")
+trainer.train()
+print("Training complete!")
+
+# Save
+model.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
+tokenizer.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
+print(f"LoRA adapter saved to C:\she\autarch\data\training\output/lora_adapter")

data/training/training.log

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+C:\she\autarch\data\training\train_lora.py:50: SyntaxWarning: invalid escape sequence '\s'
+  with open("C:\she\autarch\data\training\autarch_dataset_20260302_202634.jsonl", "r") as f:
+C:\she\autarch\data\training\train_lora.py:76: SyntaxWarning: invalid escape sequence '\s'
+  output_dir="C:\she\autarch\data\training\output",
+C:\she\autarch\data\training\train_lora.py:95: SyntaxWarning: invalid escape sequence '\s'
+  model.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
+C:\she\autarch\data\training\train_lora.py:96: SyntaxWarning: invalid escape sequence '\s'
+  tokenizer.save_pretrained("C:\she\autarch\data\training\output/lora_adapter")
+C:\she\autarch\data\training\train_lora.py:97: SyntaxWarning: invalid escape sequence '\s'
+  print(f"LoRA adapter saved to C:\she\autarch\data\training\output/lora_adapter")
+Traceback (most recent call last):
+  File "C:\she\autarch\data\training\train_lora.py", line 5, in <module>
+    from datasets import Dataset
+ModuleNotFoundError: No module named 'datasets'