File size: 2,114 Bytes
86b5bad | 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 | """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}")
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