Upload train.py with huggingface_hub

train.py

@@ -0,0 +1,77 @@
+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets"]
+# ///
+"""
+Obsidian Bases SLM Training Script
+Fine-tunes Qwen 3 0.6B to generate .base files from natural language.
+"""
+
+import os
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load dataset from Hub
+dataset = load_dataset("ssdavid/obsidian-bases-query-v1", split="train")
+
+# Format for SFT - convert to messages format
+def format_example(example):
+    return {
+        "messages": [
+            {"role": "user", "content": example["instruction"]},
+            {"role": "assistant", "content": example["output"]}
+        ]
+    }
+
+dataset = dataset.map(format_example)
+
+# Split for evaluation
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+
+# 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"],
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Training config
+training_args = SFTConfig(
+    output_dir="obsidian-bases-slm",
+    push_to_hub=True,
+    hub_model_id="ssdavid/obsidian-bases-slm",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    warmup_ratio=0.1,
+    logging_steps=10,
+    eval_strategy="steps",
+    eval_steps=50,
+    save_strategy="steps",
+    save_steps=100,
+    max_length=512,
+    report_to="trackio",
+    project="obsidian-bases-slm",
+    run_name="qwen3-0.6b-bases-v1",
+)
+
+# Create trainer
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Train
+trainer.train()
+
+# Push to Hub
+trainer.push_to_hub()
+print("Training complete! Model pushed to ssdavid/obsidian-bases-slm")