Add training script (QLoRA SFT on Qwen2.5-3B-Instruct)

train.py

@@ -0,0 +1,91 @@
+"""
+PhD Research OS — SFT Training Script
+=======================================
+Fine-tunes Qwen2.5-3B-Instruct using QLoRA on multi-task scientific research data.
+
+Tasks trained:
+  1. Scientific Claim Extraction (structured JSON output)
+  2. Epistemic Classification (Fact/Interpretation/Hypothesis/Conflict_Hypothesis)
+  3. Confidence Scoring (evidence_strength × study_quality × journal_tier × completeness)
+  4. Contradiction Detection (claim pair → conflict analysis)
+  5. Query Decomposition (broad question → sub-queries)
+  6. Decision Object Generation (knowledge gaps → proposed research actions)
+
+Base model: Qwen/Qwen2.5-3B-Instruct
+Method: QLoRA (r=64, all-linear) following "LoRA Without Regret" recipe
+Reference: arxiv:2212.05238 (LLM-NERRE), arxiv:2401.00579 (multi-task biomedical SFT)
+
+Usage:
+  pip install torch transformers trl peft datasets bitsandbytes accelerate trackio
+  python train.py
+"""
+
+import os
+import torch
+from datasets import load_dataset
+from transformers import BitsAndBytesConfig
+from peft import LoraConfig
+from trl import SFTConfig, SFTTrainer
+import trackio
+
+MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct"
+DATASET_NAME = "nkshirsa/phd-research-os-sft-data"
+OUTPUT_DIR = "./phd-research-os-brain"
+HUB_MODEL_ID = "nkshirsa/phd-research-os-brain"
+
+trackio.init(project="phd-research-os-training", run="sft-qwen25-3b-qlora-v1")
+
+print("Loading dataset...")
+dataset = load_dataset(DATASET_NAME)
+train_dataset = dataset["train"]
+eval_dataset = dataset["test"]
+print(f"Train: {len(train_dataset)} examples, Eval: {len(eval_dataset)} examples")
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)
+
+peft_config = LoraConfig(
+    r=64, lora_alpha=16, lora_dropout=0.05, bias="none",
+    task_type="CAUSAL_LM", target_modules="all-linear")
+
+training_args = SFTConfig(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.05,
+    weight_decay=0.01,
+    max_grad_norm=1.0,
+    bf16=True,
+    gradient_checkpointing=True,
+    max_length=2048,
+    model_init_kwargs={"quantization_config": bnb_config, "torch_dtype": torch.bfloat16},
+    assistant_only_loss=True,
+    logging_steps=5, logging_first_step=True, disable_tqdm=True,
+    report_to=["tensorboard"], logging_dir=f"{OUTPUT_DIR}/logs",
+    eval_strategy="steps", eval_steps=50,
+    save_strategy="steps", save_steps=100, save_total_limit=3,
+    load_best_model_at_end=True, metric_for_best_model="eval_loss", greater_is_better=False,
+    push_to_hub=True, hub_model_id=HUB_MODEL_ID, hub_strategy="every_save",
+    seed=42, data_seed=42)
+
+trainer = SFTTrainer(
+    model=MODEL_NAME, args=training_args,
+    train_dataset=train_dataset, eval_dataset=eval_dataset,
+    peft_config=peft_config)
+
+trainable = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)
+total = sum(p.numel() for p in trainer.model.parameters())
+print(f"Model: {MODEL_NAME} | Total: {total:,} | Trainable: {trainable:,} ({100*trainable/total:.2f}%)")
+
+train_result = trainer.train()
+trainer.save_model()
+trainer.push_to_hub(commit_message="Final model: PhD Research OS Brain v1")
+
+print(f"\nTraining complete! Model at: https://huggingface.co/{HUB_MODEL_ID}")
+for k, v in train_result.metrics.items():
+    print(f"  {k}: {v}")