Create train.py

train.py

@@ -0,0 +1,92 @@
+# train.py
+
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoTokenizer, AutoModelForCausalLM, TrainingArguments,
+    Trainer, BitsAndBytesConfig
+)
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+
+# Load dataset from URL
+dataset = load_dataset(
+    "json",
+    data_files="https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset/resolve/main/bitext_customer_support.jsonl",
+    split="train[:100]"  # limit for fast training in Spaces
+)
+
+def format_example(example):
+    return {
+        "text": f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"
+    }
+
+dataset = dataset.map(format_example)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+def tokenize(example):
+    tokens = tokenizer(
+        example["text"],
+        padding="max_length",
+        truncation=True,
+        max_length=512
+    )
+    tokens["labels"] = tokens["input_ids"].copy()
+    return tokens
+
+tokenized_dataset = dataset.map(tokenize, batched=True)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+model.gradient_checkpointing_enable()
+model = prepare_model_for_kbit_training(model)
+
+lora_config = LoraConfig(
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "v_proj"]
+)
+
+model = get_peft_model(model, lora_config)
+
+training_args = TrainingArguments(
+    output_dir="trained_model",
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    num_train_epochs=1,
+    learning_rate=2e-4,
+    logging_dir="./logs",
+    save_strategy="no",
+    bf16=True,
+    optim="paged_adamw_8bit",
+    report_to="none"
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+    tokenizer=tokenizer
+)
+
+trainer.train()
+
+model.save_pretrained("trained_model")
+tokenizer.save_pretrained("trained_model")