Upload scripts/train-sakthai-cpu.py with huggingface_hub

scripts/train-sakthai-cpu.py

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+#!/usr/bin/env python3
+"""
+SakThai CPU training — Qwen2.5-0.5B + LoRA + completion-only loss.
+Auto-detects CPU/GPU. Designed to work on this local machine (no CUDA).
+"""
+import os, torch
+from datasets import load_dataset, concatenate_datasets
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import LoraConfig, get_peft_model
+from trl import SFTConfig, SFTTrainer
+
+HF_USER = "Nanthasit"
+BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
+MAX_SEQ_LEN = 1024
+LIMIT = 20  # small subset for CPU testing; set to None for full data
+EPOCHS = 1
+
+is_cpu = not torch.cuda.is_available()
+print(f"Device: {'CPU' if is_cpu else 'GPU'}")
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, torch_dtype=torch.float32,
+)
+model.config.use_cache = False
+
+lora_config = LoraConfig(
+    r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                    "gate_proj", "up_proj", "down_proj"],
+    use_rslora=True,
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+def to_text(ex):
+    msgs = ex["messages"]
+    tools = ex.get("tools") or None
+    text = tokenizer.apply_chat_template(
+        msgs, tools=tools, tokenize=False, add_generation_prompt=False,
+    )
+    return {"text": text}
+
+main = load_dataset(f"{HF_USER}/sakthai-combined-v7", split="train")
+if LIMIT:
+    main = main.select(range(min(LIMIT, len(main))))
+train_data = main.map(to_text, remove_columns=main.column_names)
+try:
+    supp = load_dataset(f"{HF_USER}/sakthai-irrelevance-supplement", split="train")
+    if LIMIT:
+        supp = supp.select(range(min(LIMIT, len(supp))))
+    supp_text = supp.map(to_text, remove_columns=supp.column_names)
+    train_data = concatenate_datasets([train_data, supp_text])
+except Exception as e:
+    print("irrelevance-supplement skipped:", e)
+
+eval_raw = load_dataset(f"{HF_USER}/sakthai-combined-v7", split="test")
+if LIMIT:
+    eval_raw = eval_raw.select(range(min(max(5, LIMIT // 4), len(eval_raw))))
+eval_data = eval_raw.map(to_text, remove_columns=eval_raw.column_names)
+print(f"train={len(train_data)}  eval={len(eval_data)}")
+
+args = SFTConfig(
+    output_dir="./sakthai-0.5b-cpu",
+    num_train_epochs=EPOCHS,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=1,
+    learning_rate=2e-4, lr_scheduler_type="cosine", warmup_ratio=0.03,
+    logging_steps=1, eval_strategy="steps", eval_steps=5,
+    save_strategy="no",
+    load_best_model_at_end=False,
+    fp16=False, bf16=False,
+    report_to="none",
+    dataset_text_field="text",
+    max_seq_length=MAX_SEQ_LEN,
+    completion_only_loss=True,
+)
+trainer = SFTTrainer(
+    model=model, processing_class=tokenizer, args=args,
+    train_dataset=train_data, eval_dataset=eval_data,
+)
+trainer.train()
+trainer.save_model("./sakthai-0.5b-cpu-final")
+tokenizer.save_pretrained("./sakthai-0.5b-cpu-final")
+print(f"\nDone. Model saved to ./sakthai-0.5b-cpu-final")