Upload scripts/train-sakthai-coder-browser.py with huggingface_hub

scripts/train-sakthai-coder-browser.py

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+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#   "transformers>=4.44",
+#   "trl>=0.19,<0.20",
+#   "peft>=0.7",
+#   "datasets",
+#   "accelerate",
+#   "bitsandbytes>=0.43",
+#   "huggingface_hub",
+# ]
+# ///
+"""
+Train Qwen2.5-Coder-1.5B for browser automation (web agent).
+Same QLoRA + rsLoRA recipe as sakthai-plus-1.5b.
+
+Generates synthetic browser automation training data on the fly.
+
+Usage:
+  hf jobs uv run --flavor a10g-small --timeout 6h --secrets HF_TOKEN train-sakthai-coder-browser.py
+"""
+import os
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+from trl import SFTConfig, SFTTrainer
+
+BASE_MODEL   = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
+HF_USER      = "Nanthasit"
+ADAPTER_REPO = f"{HF_USER}/sakthai-coder-browser-lora"
+MERGED_REPO  = f"{HF_USER}/sakthai-coder-browser"
+MAX_SEQ_LEN  = 4096
+HF_TOKEN     = os.environ.get("HF_TOKEN")
+assert HF_TOKEN, "Set HF_TOKEN secret"
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Use generated dataset from Hub
+from datasets import load_dataset
+train_raw = load_dataset(f"{HF_USER}/sakthai-coder-browser", split="train")
+
+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}
+
+train_data = train_raw.map(to_text, remove_columns=train_raw.column_names)
+eval_data = train_data.select(range(max(1, int(len(train_data) * 0.1))))
+
+print(f"Dataset: train={len(train_data)} eval={len(eval_data)}")
+
+# ── Model & Training ──────────────────────────────────────────
+bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
+
+model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, quantization_config=bnb,
+    device_map="auto", torch_dtype=torch.bfloat16)
+model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
+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()
+
+args = SFTConfig(
+    output_dir="./sakthai-coder-browser-lora",
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=1,
+    eval_accumulation_steps=1,
+    gradient_accumulation_steps=8,
+    gradient_checkpointing=True,
+    optim="adamw_8bit",
+    learning_rate=2e-4, lr_scheduler_type="cosine", warmup_ratio=0.03,
+    logging_steps=5, eval_strategy="steps", eval_steps=10,
+    save_strategy="steps", save_steps=20, save_total_limit=2,
+    load_best_model_at_end=True, metric_for_best_model="eval_loss",
+    bf16=True, tf32=True, report_to="none",
+    dataset_text_field="text",
+    max_seq_length=MAX_SEQ_LEN,
+    completion_only_loss=True,
+    push_to_hub=True,
+    hub_model_id=ADAPTER_REPO,
+    hub_strategy="every_save",
+)
+
+trainer = SFTTrainer(model=model, processing_class=tokenizer, args=args,
+    train_dataset=train_data, eval_dataset=eval_data)
+trainer.train()
+trainer.save_model("./sakthai-coder-browser-lora-best")
+tokenizer.save_pretrained("./sakthai-coder-browser-lora-best")
+
+from huggingface_hub import login
+login(token=HF_TOKEN)
+trainer.model.push_to_hub(ADAPTER_REPO)
+tokenizer.push_to_hub(ADAPTER_REPO)
+
+# Merge
+from peft import PeftModel
+del model, trainer
+torch.cuda.empty_cache()
+
+base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto")
+merged = PeftModel.from_pretrained(base, "./sakthai-coder-browser-lora-best").merge_and_unload()
+merged.push_to_hub(MERGED_REPO)
+tokenizer.push_to_hub(MERGED_REPO)
+print(f"Done. Adapter: {ADAPTER_REPO}  Merged: {MERGED_REPO}")