Alex
1 month ago
3 changed files with 185 additions and 80 deletions
@ -0,0 +1,152 @@ |
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import torch |
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from datasets import load_dataset |
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForCausalLM, |
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TrainingArguments, |
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) |
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from peft import ( |
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LoraConfig, |
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get_peft_model, |
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prepare_model_for_kbit_training, |
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) |
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from trl import SFTTrainer |
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import os |
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os.environ["TORCHDYNAMO_DISABLE"] = "1" |
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|
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# ---------------------------- |
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# Model configuration |
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# ---------------------------- |
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MODEL_NAME = "Qwen/Qwen2.5-14B-Instruct" |
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|
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print("=== Starting fine-tuning script ===") |
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|
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print(f"{80 * '_'}\n[1/7] Loading tokenizer...") |
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tokenizer = AutoTokenizer.from_pretrained( |
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MODEL_NAME, |
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trust_remote_code=True |
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) |
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|
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# Ensure padding token is defined |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.model_max_length = 1024 |
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|
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print("Tokenizer loaded and configured.") |
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|
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print(f"{80 * '_'}\n[2/7] Loading model in 4-bit mode (QLoRA)...") |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_NAME, |
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load_in_4bit=True, |
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device_map="auto", |
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torch_dtype=torch.float16, # OK for weights |
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trust_remote_code=True, |
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) |
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print("Model loaded.") |
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|
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print(f"{80 * '_'}\n[3/7] Preparing model for k-bit training...") |
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model = prepare_model_for_kbit_training(model) |
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print("Model prepared for k-bit training.") |
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|
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# ---------------------------- |
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# LoRA configuration |
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# ---------------------------- |
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print(f"{80 * '_'}\n[4/7] Configuring LoRA adapters...") |
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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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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=[ |
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"q_proj", |
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"k_proj", |
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"v_proj", |
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"o_proj", |
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"gate_proj", |
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"up_proj", |
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"down_proj", |
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], |
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) |
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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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print("LoRA adapters attached to the model.") |
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|
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# ---------------------------- |
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# Dataset loading |
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# ---------------------------- |
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print(f"{80 * '_'}\n[5/7] Loading dataset from JSON file...") |
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dataset = load_dataset( |
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"json", |
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data_files="traductions.json" |
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) |
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print(f"Dataset loaded with {len(dataset['train'])} samples.") |
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|
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print("Formatting dataset for Ukrainian → French translation...") |
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|
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def format_prompt(example): |
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prompt = ( |
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"Translate the following Ukrainian text into French.\n\n" |
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f"Ukrainian: {example['text']}\n" |
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f"French: {example['translation']}" |
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) |
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return {"text": prompt} |
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|
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dataset = dataset.map(format_prompt, remove_columns=dataset["train"].column_names) |
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print("Dataset formatting completed.") |
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|
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# ---------------------------- |
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# Training arguments |
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# ---------------------------- |
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print(f"{80 * '_'}\n[6/7] Initializing training arguments...") |
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training_args = TrainingArguments( |
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output_dir="./qwen-uk-fr-lora", |
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per_device_train_batch_size=1, |
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gradient_accumulation_steps=8, |
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learning_rate=2e-4, |
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num_train_epochs=3, |
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|
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fp16=False, |
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bf16=False, |
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|
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logging_steps=10, |
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save_steps=500, |
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save_total_limit=2, |
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|
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# Use 32-bit optimizer |
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optim="paged_adamw_32bit", |
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report_to="none", |
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) |
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print("Training arguments ready.") |
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|
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# ---------------------------- |
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# Trainer |
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# ---------------------------- |
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print("Initializing SFTTrainer...") |
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trainer = SFTTrainer( |
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model=model, |
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train_dataset=dataset["train"], |
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processing_class=tokenizer, |
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args=training_args, |
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) |
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print("Trainer initialized.") |
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|
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# ---------------------------- |
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# Train |
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# ---------------------------- |
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print(f"{80 * '_'}\n[7/7] Starting training...") |
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trainer.train() |
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print("Training completed successfully.") |
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|
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# ---------------------------- |
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# Save LoRA adapter |
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# ---------------------------- |
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print("Saving LoRA adapter and tokenizer...") |
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trainer.model.save_pretrained("./qwen-uk-fr-lora") |
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tokenizer.save_pretrained("./qwen-uk-fr-lora") |
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|
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print("=== Fine-tuning finished ===") |
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print("LoRA adapter saved in ./qwen-uk-fr-lora") |
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@ -1,8 +1,16 @@ |
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certifi==2026.1.4 |
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charset-normalizer==3.4.4 |
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idna==3.11 |
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pillow==12.1.0 |
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PyPDF2==3.0.1 |
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reportlab==4.4.7 |
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requests==2.32.5 |
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urllib3==2.6.2 |
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certifi |
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charset-normalizer |
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idna |
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pillow |
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PyPDF2 |
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reportlab |
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requests |
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urllib3 |
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|
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torch |
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transformers |
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datasets |
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peft |
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bitsandbytes |
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accelerate |
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trl |
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