Delete train.py

train.py

@@ -1,92 +0,0 @@
-# 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")