Upload train_intent_router.py with huggingface_hub

train_intent_router.py

@@ -0,0 +1,63 @@
+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "datasets", "trackio", "accelerate", "bitsandbytes"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import json
+
+# Load dataset
+dataset = load_dataset("ArchibaldAI/agent-intent-router")
+
+print(f"Train: {len(dataset['train'])} examples")
+print(f"Test: {len(dataset['test'])} examples")
+print(f"Sample: {dataset['train'][0]}")
+
+# Model: SmolLM2-360M - tiny, fast, perfect for classification
+model_name = "HuggingFaceTB/SmolLM2-360M-Instruct"
+output_name = "ArchibaldAI/agent-intent-router-v1"
+
+# LoRA config - lightweight fine-tuning
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+# Training config
+training_args = SFTConfig(
+    output_dir="./intent-router",
+    push_to_hub=True,
+    hub_model_id=output_name,
+    num_train_epochs=5,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    learning_rate=2e-4,
+    warmup_ratio=0.1,
+    logging_steps=10,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    report_to="trackio",
+    run_name="intent-router-v1",
+    max_length=256,  # Short sequences for classification
+    bf16=True,
+)
+
+# Train
+trainer = SFTTrainer(
+    model=model_name,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+trainer.train()
+trainer.push_to_hub()
+print(f"\n✅ Model pushed to {output_name}")