Upload train_sft_qwen25.py with huggingface_hub

train_sft_qwen25.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.44.0",
+#     "accelerate>=0.34.0",
+#     "datasets>=2.19.0",
+#     "trackio",
+# ]
+# ///
+"""
+SFT demo for Qwen/Qwen2.5-0.5B on the Capybara dataset with LoRA and Trackio.
+Designed for Hugging Face Jobs (uv) with Hub push enabled.
+"""
+import os
+import random
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from peft import LoraConfig
+from trl import SFTConfig, SFTTrainer
+import trackio
+
+MODEL_ID = os.environ.get("MODEL_ID", "Qwen/Qwen2.5-0.5B")
+DATASET_ID = os.environ.get("DATASET_ID", "trl-lib/Capybara")
+HUB_MODEL_ID = os.environ.get("HUB_MODEL_ID", "davidsmts/qwen2_5-0.5b-capybara-sft")
+RUN_NAME = os.environ.get("RUN_NAME", "qwen25-0.5b-capybara-demo")
+PROJECT = os.environ.get("TRACKIO_PROJECT", "qwen-sft-demo")
+SPACE_ID = os.environ.get("TRACKIO_SPACE", "davidsmts/trackio")
+MAX_TRAIN_SAMPLES = int(os.environ.get("MAX_TRAIN_SAMPLES", "200"))
+SEED = int(os.environ.get("SEED", "42"))
+
+random.seed(SEED)
+
+print("Loading dataset...")
+dataset = load_dataset(DATASET_ID, split="train")
+print(f"Loaded {len(dataset)} examples")
+
+if MAX_TRAIN_SAMPLES and len(dataset) > MAX_TRAIN_SAMPLES:
+    dataset = dataset.shuffle(seed=SEED).select(range(MAX_TRAIN_SAMPLES))
+    print(f"Subsampled to {len(dataset)} examples for quick demo")
+
+print("Creating train/test split...")
+split = dataset.train_test_split(test_size=0.1, seed=SEED)
+train_dataset = split["train"]
+eval_dataset = split["test"]
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+def formatting_func(example):
+    # Capybara provides conversation-style "messages"; apply chat template.
+    return tokenizer.apply_chat_template(example["messages"], tokenize=False, add_generation_prompt=False)
+
+print("Initializing Trackio...")
+trackio.init(
+    project=PROJECT,
+    name=RUN_NAME,
+    space_id=SPACE_ID,
+    config={
+        "model": MODEL_ID,
+        "dataset": DATASET_ID,
+        "lr": 2e-5,
+        "epochs": 1,
+        "max_train_samples": MAX_TRAIN_SAMPLES,
+    },
+)
+
+print("Building LoRA config...")
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "v_proj"],
+)
+
+print("Setting trainer args...")
+training_args = SFTConfig(
+    output_dir="./outputs",
+    push_to_hub=True,
+    hub_model_id=HUB_MODEL_ID,
+    hub_strategy="every_save",
+    num_train_epochs=1,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-5,
+    max_length=1024,
+    logging_steps=5,
+    save_strategy="steps",
+    save_steps=50,
+    save_total_limit=2,
+    eval_strategy="steps",
+    eval_steps=50,
+    warmup_ratio=0.03,
+    lr_scheduler_type="cosine",
+    gradient_checkpointing=True,
+    fp16=True,
+    report_to="trackio",
+    project=PROJECT,
+    run_name=RUN_NAME,
+)
+
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model=MODEL_ID,
+    tokenizer=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    formatting_func=formatting_func,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Saving and pushing to hub...")
+trainer.push_to_hub()
+trackio.finish()
+
+print(f"Done! Model pushed to https://huggingface.co/{HUB_MODEL_ID}")
+print(f"Track metrics at https://huggingface.co/spaces/{SPACE_ID}")