Upload train_smol_discharge.py with huggingface_hub

train_smol_discharge.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+#     "bitsandbytes",
+# ]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from trl import SFTTrainer, SFTConfig
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")
+
+CHAT_TEMPLATE = "{% for message in messages %}{% if message['role'] == 'system' %}<|im_start|>system\n{{ message['content'] }}<|im_end|>\n{% elif message['role'] == 'user' %}<|im_start|>user\n{{ message['content'] }}<|im_end|>\n{% elif message['role'] == 'assistant' %}<|im_start|>assistant\n{{ message['content'] }}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+
+tokenizer.chat_template = CHAT_TEMPLATE
+special_tokens = {"additional_special_tokens": ["<|im_start|>", "<|im_end|>"]}
+tokenizer.add_special_tokens(special_tokens)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    "HuggingFaceTB/SmolLM3-3B-Base",
+    torch_dtype="auto",
+    device_map="auto",
+)
+model.resize_token_embeddings(len(tokenizer))
+
+print("Loading dataset...")
+train_dataset = load_dataset("chrisvoncsefalvay/smol-discharge-notes-sft", split="train")
+eval_dataset = load_dataset("chrisvoncsefalvay/smol-discharge-notes-sft", split="validation")
+print(f"Train: {len(train_dataset)} examples")
+print(f"Eval: {len(eval_dataset)} examples")
+
+config = SFTConfig(
+    output_dir="smollm3-discharge-notes-sft",
+    push_to_hub=True,
+    hub_model_id="chrisvoncsefalvay/smollm3-discharge-notes-sft",
+    hub_strategy="every_save",
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=4,
+    gradient_accumulation_steps=2,
+    learning_rate=2e-5,
+    max_length=2048,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=50,
+    save_total_limit=2,
+    eval_strategy="steps",
+    eval_steps=50,
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    gradient_checkpointing=True,
+    bf16=True,
+    report_to="trackio",
+    project="clinical-action-processing",
+    run_name="smollm3-3b-discharge-sft-a100",
+)
+
+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"],
+)
+
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model=model,
+    processing_class=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Pushing to Hub...")
+trainer.push_to_hub()
+
+print("Complete!")