v3 dataset (582 ex, 14% direct) + Qwen3.5-27B training script

train_qwen35_27b.py

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+"""BT-7274 LoRA — Qwen3.5-27B, bf16 LoRA (NOT QLoRA).
+
+Unsloth docs: use FastLanguageModel for text-only training on Qwen3.5 VL models.
+bf16 LoRA 27B ≈ 56GB VRAM — fits on H100 80GB.
+
+Usage:
+    pip install --upgrade unsloth unsloth_zoo
+    python train_qwen35_27b.py
+"""
+
+from unsloth import FastLanguageModel
+from trl import SFTTrainer, SFTConfig
+from datasets import load_dataset
+import torch
+
+# --- Config ---
+MODEL = "Qwen/Qwen3.5-27B"
+MAX_SEQ = 4096
+RANK = 16
+ALPHA = 16
+DATA = "./bt7274_v3.jsonl"
+OUT = "./bt7274-qwen35-27b-lora-v1"
+EPOCHS = 3
+BATCH = 1
+GRAD_ACCUM = 8
+LR = 1e-4  # slightly lower for larger model
+
+# --- Load model (bf16, NOT 4-bit) ---
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=MODEL,
+    max_seq_length=MAX_SEQ,
+    load_in_4bit=False,      # QLoRA not recommended for Qwen3.5
+    load_in_16bit=True,      # bf16 LoRA
+    full_finetuning=False,
+    dtype=torch.bfloat16,
+)
+
+# --- LoRA adapter ---
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=RANK,
+    lora_alpha=ALPHA,
+    lora_dropout=0,
+    target_modules=[
+        "q_proj", "k_proj", "v_proj", "o_proj",
+        "gate_proj", "up_proj", "down_proj",
+    ],
+    bias="none",
+    use_gradient_checkpointing="unsloth",
+    random_state=42,
+    max_seq_length=MAX_SEQ,
+)
+
+# --- Dataset ---
+ds = load_dataset("json", data_files=DATA, split="train")
+
+def to_chatml(ex):
+    text = tokenizer.apply_chat_template(
+        ex["messages"], tokenize=False, add_generation_prompt=False
+    )
+    return {"text": text}
+
+ds = ds.map(to_chatml)
+
+steps = (len(ds) * EPOCHS) // (BATCH * GRAD_ACCUM)
+print(f"Dataset: {len(ds)} examples")
+print(f"Epochs: {EPOCHS}, effective batch: {BATCH * GRAD_ACCUM}")
+print(f"Estimated steps: {steps}")
+print(f"LoRA: r={RANK}, alpha={ALPHA}")
+print(f"Max seq: {MAX_SEQ}")
+print(f"Model: {MODEL}")
+
+# --- Train ---
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=ds,
+    args=SFTConfig(
+        output_dir=OUT,
+        per_device_train_batch_size=BATCH,
+        gradient_accumulation_steps=GRAD_ACCUM,
+        num_train_epochs=EPOCHS,
+        learning_rate=LR,
+        bf16=True,
+        logging_steps=5,
+        save_steps=50,
+        save_total_limit=2,
+        warmup_steps=10,
+        optim="adamw_8bit",
+        seed=42,
+        report_to="none",
+        max_seq_length=MAX_SEQ,
+        dataset_num_proc=1,
+    ),
+)
+
+trainer.train()
+
+# --- Save LoRA adapter ---
+model.save_pretrained(OUT)
+tokenizer.save_pretrained(OUT)
+print(f"\nSaved LoRA adapter to {OUT}/")