Upload train_ffmpeg.py with huggingface_hub

train_ffmpeg.py

@@ -0,0 +1,74 @@
+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers", "datasets", "accelerate", "bitsandbytes"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load the dataset
+dataset = load_dataset("kingjux/ffmpeg-commands-cot", split="train")
+print(f"Loaded {len(dataset)} training examples")
+
+# LoRA config for efficient fine-tuning
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+# Training config
+training_args = SFTConfig(
+    output_dir="ffmpeg-command-generator",
+
+    # Training params
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    warmup_ratio=0.1,
+
+    # Logging and saving
+    logging_steps=5,
+    save_strategy="epoch",
+
+    # Hub settings
+    push_to_hub=True,
+    hub_model_id="kingjux/ffmpeg-command-generator",
+    hub_strategy="every_save",
+
+    # Trackio monitoring
+    report_to="trackio",
+    run_name="ffmpeg-sft-30examples",
+
+    # Memory optimization
+    gradient_checkpointing=True,
+    bf16=True,
+
+    # Other
+    seed=42,
+    max_seq_length=1024,
+)
+
+# Create trainer
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B-Instruct",
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Train
+print("Starting training...")
+trainer.train()
+
+# Save and push
+print("Pushing to Hub...")
+trainer.save_model()
+trainer.push_to_hub()
+
+print("Training complete!")