Upload train.py

train.py

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+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 16,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+prompt_context = """Bạn là một tư vấn viên hữu ích về luật.
+### Instruction and Input:
+Dựa vào ngữ cảnh/tài liệu sau:
+{}
+Hãy trả lời câu hỏi: {}
+
+### Câu trả lời:
+{}
+"""
+
+prompt = """Bạn là một tư vấn viên hữu ích về luật. 
+### Instruction and Input:
+Hãy trả lời câu hỏi: {}
+{}
+
+### Câu trả lời:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
+def formatting_prompts_func(examples):
+    instructions = examples["context"]
+    inputs       = examples["input"]
+    outputs      = examples["output"]
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        # Must add EOS_TOKEN, otherwise your generation will go on forever!
+        if instructions:
+            text = prompt_context.format(instruction, input, output) + EOS_TOKEN
+        else:
+            text = prompt.format(input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+
+
+from datasets import load_dataset
+dataset = load_dataset("json", data_files="/root/unsloth/train_data.jsonl", split="train")
+dataset = dataset.map(formatting_prompts_func, batched = True,)
+
+
+from trl import SFTTrainer
+from transformers import TrainingArguments, DataCollatorForSeq2Seq
+from unsloth import is_bfloat16_supported
+
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from unsloth import is_bfloat16_supported
+
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=max_seq_length,
+    dataset_num_proc=16,
+    packing=False,  # Can make training 5x faster for short sequences.
+    args=TrainingArguments(
+        per_device_train_batch_size=16,
+        gradient_accumulation_steps=4,
+        num_train_epochs=1,  # Set this for 1 full training run.
+        learning_rate=2e-4,
+        fp16=not is_bfloat16_supported(),
+        bf16=is_bfloat16_supported(),
+        logging_steps=1,
+        optim="adamw_8bit",
+        weight_decay=0.01,
+        lr_scheduler_type="linear",
+        seed=3407,
+        output_dir="outputs",
+        report_to="none",  # Use this for WandB, etc.
+        # Save every 1000 steps
+        save_steps=500,
+        save_total_limit=3,  # Keep only the last 3 checkpoints
+    ),
+)
+
+trainer_stats = trainer.train()
+
+model.save_pretrained("lora_model") # Local saving
+tokenizer.save_pretrained("lora_model")
+
+model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
+model.save_pretrained_merged("model", tokenizer, save_method = "lora",)