add training scripts: v3, qwen3-8b, qlora-7b

train_v3.py

@@ -0,0 +1,97 @@
+"""BT-7274 LoRA v3 — balanced tool-call + direct-response.
+
+Usage:
+    source ~/lora-train/bin/activate
+    cd ~/Projects/lora
+    python train_v3.py
+"""
+
+from unsloth import FastLanguageModel
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from datasets import load_dataset
+
+# --- Config ---
+MODEL = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit"
+MAX_SEQ = 4096
+RANK = 16
+ALPHA = 16
+DATA = "./bt7274_v3.jsonl"
+OUT = "./bt7274-lora-v3"
+EPOCHS = 3
+BATCH = 1
+GRAD_ACCUM = 8
+LR = 2e-4
+
+# --- Load model (4-bit quantized) ---
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=MODEL,
+    max_seq_length=MAX_SEQ,
+    load_in_4bit=True,
+    dtype=None,
+)
+
+# --- 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,
+)
+
+# --- 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}")
+
+# --- Train ---
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=ds,
+    args=TrainingArguments(
+        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",
+    ),
+    dataset_text_field="text",
+    max_seq_length=MAX_SEQ,
+    packing=False,
+)
+
+trainer.train()
+
+# --- Save LoRA adapter ---
+model.save_pretrained(OUT)
+tokenizer.save_pretrained(OUT)
+print(f"\nSaved LoRA adapter to {OUT}/")