train.py

# /// 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")