Upload scripts/train_7b_local.py with huggingface_hub

scripts/train_7b_local.py

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+"""Fine-tune Qwen 2.5 7B Instruct Q4 for command adapter."""
+import json, torch
+from datasets import Dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig
+from peft import LoraConfig, get_peft_model
+from trl import SFTTrainer
+
+MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"
+OUTPUT_DIR = "./adapter-model-7b"
+
+print("Loading dataset...")
+examples = []
+with open("dataset_v3.jsonl") as f:
+    for line in f:
+        d = json.loads(line)
+        text = f"<|im_start|>system\nYou are a command adapter. Output ONLY valid JSON. No explanation.<|im_end|>\n<|im_start|>user\n{d['input']}<|im_end|>\n<|im_start|>assistant\n{d['output']}<|im_end|>"
+        examples.append({"text": text})
+examples = examples * 4
+dataset = Dataset.from_list(examples)
+print(f"Dataset: {len(examples)} examples")
+
+print("Loading model (Q4)...")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, quantization_config=bnb_config, device_map="auto", trust_remote_code=True)
+
+lora_config = LoraConfig(
+    r=32, lora_alpha=64,
+    target_modules=["q_proj","v_proj","k_proj","o_proj","gate_proj","up_proj","down_proj"],
+    lora_dropout=0.05, bias="none", task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+print("Training...")
+args = TrainingArguments(
+    output_dir=OUTPUT_DIR, num_train_epochs=5,
+    per_device_train_batch_size=2, gradient_accumulation_steps=4,
+    learning_rate=2e-4, fp16=True, logging_steps=20,
+    save_strategy="epoch", warmup_ratio=0.1,
+    lr_scheduler_type="cosine", report_to="none",
+)
+trainer = SFTTrainer(model=model, train_dataset=dataset, args=args, processing_class=tokenizer)
+trainer.train()
+
+print("Saving...")
+model.save_pretrained(OUTPUT_DIR)
+tokenizer.save_pretrained(OUTPUT_DIR)
+print(f"Done! Saved to {OUTPUT_DIR}")