初始化项目，由ModelHub XC社区提供模型

Model: karthik-2905/AL1-model-B
Source: Original Platform

mlb/train_sft.py

@@ -0,0 +1,71 @@
+import os, argparse
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+os.environ.setdefault("HF_HOME", os.path.join(HERE, "hf_cache"))
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTConfig, SFTTrainer
+
+MODEL = "Qwen/Qwen3-0.6B"
+TRAIN = os.path.join(HERE, "data", "sft_train.jsonl")
+VAL   = os.path.join(HERE, "data", "sft_val.jsonl")
+
+QWEN_TARGETS = ["q_proj", "k_proj", "v_proj", "o_proj",
+                "gate_proj", "up_proj", "down_proj"]
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--lr", type=float, default=2e-4)
+    ap.add_argument("--epochs", type=float, default=3)
+    ap.add_argument("--frac", type=float, default=1.0)      # 0.1 for LR sweep
+    ap.add_argument("--dora", action="store_true")          # run 1 = off
+    ap.add_argument("--out", default=os.path.join(HERE, "adapters", "sft-lora"))
+    ap.add_argument("--eval_steps", type=int, default=50)
+    ap.add_argument("--logging_steps", type=int, default=10)
+    ap.add_argument("--batch", type=int, default=8)
+    ap.add_argument("--grad_accum", type=int, default=4)
+    ap.add_argument("--no_grad_ckpt", action="store_true")
+    a = ap.parse_args()
+
+    train = load_dataset("json", data_files=TRAIN, split="train")
+    val   = load_dataset("json", data_files=VAL,   split="train")
+    if a.frac < 1.0:
+        train = train.select(range(int(len(train) * a.frac)))
+
+    peft = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05,
+                      bias="none", task_type="CAUSAL_LM",
+                      target_modules=QWEN_TARGETS, use_dora=a.dora)
+
+    cfg = SFTConfig(
+        output_dir=a.out,
+        model_init_kwargs={"dtype": "bfloat16"},
+        max_length=512,
+        packing=False,
+        assistant_only_loss=True,
+        use_liger_kernel=False,
+        per_device_train_batch_size=a.batch,
+        per_device_eval_batch_size=a.batch,
+        gradient_accumulation_steps=a.grad_accum,
+        num_train_epochs=a.epochs,
+        learning_rate=a.lr,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.03,
+        bf16=True,
+        gradient_checkpointing=not a.no_grad_ckpt,
+        logging_steps=a.logging_steps,
+        eval_strategy="steps",
+        eval_steps=a.eval_steps,
+        save_strategy="epoch",
+        report_to="none",
+    )
+
+    trainer = SFTTrainer(model=MODEL, args=cfg,
+                         train_dataset=train, eval_dataset=val,
+                         peft_config=peft)
+    trainer.train()
+    trainer.save_model(a.out)
+    print("saved adapter ->", a.out)
+    print("final metrics:", trainer.state.log_history[-1])
+
+if __name__ == "__main__":
+    main()