Upload hf_train_lr2e4.py with huggingface_hub

hf_train_lr2e4.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.14.0", "trackio", "bitsandbytes", "accelerate"]
+# ///
+
+"""
+Underwood SFT Training - Learning Rate 2e-4
+Fine-tunes Gemma 3 4B with QLoRA on strategic advisor conversations
+"""
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+import trackio
+
+# QLoRA config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-3-4b-it",
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="eager",
+)
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-4b-it")
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+# Load dataset
+dataset = load_dataset("AmiDwivedi/underwood-conversations")
+
+# LoRA config (matching local setup)
+lora_config = LoraConfig(
+    r=128,
+    lora_alpha=256,
+    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"],
+)
+
+# Training config
+training_args = SFTConfig(
+    output_dir="underwood-lr2e4",
+    num_train_epochs=10,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-4,
+    weight_decay=0.01,
+    warmup_ratio=0.03,
+    lr_scheduler_type="cosine",
+    logging_steps=10,
+    eval_strategy="steps",
+    eval_steps=50,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=2,
+    bf16=True,
+    max_length=2048,
+    packing=False,
+    gradient_checkpointing=True,
+    push_to_hub=True,
+    hub_model_id="AmiDwivedi/underwood-lr2e4",
+    hub_strategy="every_save",
+    report_to="trackio",
+    run_name="underwood-lr2e4",
+)
+
+# Create trainer
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+    peft_config=lora_config,
+    processing_class=tokenizer,
+)
+
+# Train
+trainer.train()
+trainer.push_to_hub()
+print("Training complete! Model pushed to AmiDwivedi/underwood-lr2e4")