Upload train_summary.py with huggingface_hub

train_summary.py

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+# /// script
+# dependencies = [
+#   "trl",
+#   "peft",
+#   "trackio",
+#   "transformers",
+#   "datasets",
+#   "bitsandbytes",
+#   "accelerate"
+# ]
+# ///
+
+import os
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoTokenizer, BitsAndBytesConfig
+import torch
+import trackio
+
+# Configuration
+model_id = "Qwen/Qwen2.5-7B-Instruct"
+dataset_id = "daekeun-ml/naver-news-summarization-ko"
+output_dir = "Qwen2.5-7B-Summarize-Ko"
+hub_model_id = f"epinfomax/{output_dir}"
+
+print(f"Starting training for {model_id} on {dataset_id}")
+
+# 1. Load and Format Dataset
+dataset = load_dataset(dataset_id, split="train")
+
+def format_to_messages(example):
+    # Map 'document' -> input, 'summary' -> output
+    return {
+        "messages": [
+            {"role": "user", "content": f"Summarize the following document:\n\n{example['document']}"},
+            {"role": "assistant", "content": example['summary']}
+        ]
+    }
+
+print("Formatting dataset...")
+dataset = dataset.map(format_to_messages, remove_columns=dataset.column_names)
+# Create a small eval split
+dataset = dataset.train_test_split(test_size=0.05, seed=42)
+
+print(f"Train size: {len(dataset['train'])}, Eval size: {len(dataset['test'])}")
+
+# 2. Model & Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Quantization Config (4-bit)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+
+# 3. LoRA Config
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
+)
+
+# 4. Trainer
+training_args = SFTConfig(
+    output_dir=output_dir,
+    num_train_epochs=3,
+    per_device_train_batch_size=4, # Adjust based on A10G memory
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    logging_steps=25,
+    eval_strategy="steps",
+    eval_steps=100,
+    save_strategy="steps",
+    save_steps=100,
+    push_to_hub=True,
+    hub_model_id=hub_model_id,
+    report_to="trackio",
+    project="BizFlow-Summarizer",
+    run_name="Qwen-7B-SFT-Run1",
+    fp16=True,
+    max_seq_length=1024, # Truncate to save memory/time
+    dataset_text_field="messages", # Use the messages column
+    packing=False # Qwen might be sensitive to packing with chat template? Better safe.
+)
+
+trainer = SFTTrainer(
+    model=model_id,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+    peft_config=peft_config,
+    args=training_args,
+    processing_class=tokenizer,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Pushing to hub...")
+trainer.push_to_hub()
+print("Done!")