File size: 4,069 Bytes
b25b8f2 | 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 | import os
import torch
from datasets import load_dataset
from unsloth import FastLanguageModel
from trl import SFTTrainer
from transformers import TrainingArguments
def main():
print("Initializing Unsloth QLoRA Fine-Tuning Pipeline for MVM²...")
# Configuration
max_seq_length = 2048 # Good default for math problems
dtype = None # Auto detects Float16, Bfloat16
load_in_4bit = True # Use 4-bit quantization to fit on consumer GPUs (e.g. RTX 3090, 4090, T4)
model_name = "unsloth/llama-3-8b-Instruct-bnb-4bit" # Excellent 8B model pre-quantized
# 1. Load Model with Unsloth (up to 2x faster, 70% less VRAM)
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
# 2. Add LoRA Adapters
# We only train 1-5% of the weights, targeting the specific layers that handle reasoning
model = FastLanguageModel.get_peft_model(
model,
r = 16, # Rank
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Optimization: 0 is faster
bias = "none",
use_gradient_checkpointing = "unsloth",
random_state = 3407,
use_rslora = False,
loftq_config = None,
)
# 3. Load the generated MVM2 dataset
dataset_path = "models/local_mvm2_adapter/mvm2_training_data.jsonl"
if not os.path.exists(dataset_path):
raise FileNotFoundError(f"Missing dataset {dataset_path}. Run generate_math_dataset.py first!")
dataset = load_dataset('json', data_files=dataset_path, split='train')
# Format the messages using the model's chat template
def format_chatml(examples):
texts = []
for messages in examples["messages"]:
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
texts.append(text)
return {"text": texts}
dataset = dataset.map(format_chatml, batched=True)
# 4. Supervised Fine-Tuning (SFT) Trainer
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 5,
max_steps = 60, # Set to roughly 1-2 epochs based on dataset size for real training
learning_rate = 2e-4,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
# 5. Start Training
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
print("\nStarting QLoRA Fine-Tuning...")
trainer_stats = trainer.train()
# 6. Save Model
save_path = "models/local_mvm2_adapter/lora_model"
print(f"\nSaving LoRA adapters to {save_path}...")
model.save_pretrained(save_path) # Local saving
tokenizer.save_pretrained(save_path)
print("\n✅ Fine-Tuning Complete! You can now run the MVM2 Engine completely offline by switching 'use_local_model=True' in llm_agent.py.")
if __name__ == "__main__":
main()
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