Upload scripts/train_7b_cloud.py with huggingface_hub

scripts/train_7b_cloud.py

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+"""AutoTrain cloud script - trains Qwen 7B LoRA and pushes to HuggingFace Hub."""
+import os
+import torch
+from datasets import load_dataset, concatenate_datasets
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig
+from peft import LoraConfig, get_peft_model
+from trl import SFTTrainer
+from huggingface_hub import login
+
+HF_TOKEN = os.environ["HF_TOKEN"]
+MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"
+REPO_ID = "devsomosahub/agent-os-adapter-7b"
+OUTPUT_DIR = "./output"
+
+login(token=HF_TOKEN)
+
+print("Loading dataset from Hub...")
+ds = load_dataset("devsomosahub/agent-os-dataset", data_files="train.jsonl", split="train")
+
+def format_example(example):
+    text = f"<|im_start|>system\nYou are a command adapter. Output ONLY valid JSON. No explanation.<|im_end|>\n<|im_start|>user\n{example['input']}<|im_end|>\n<|im_start|>assistant\n{example['output']}<|im_end|>"
+    return {"text": text}
+
+ds = ds.map(format_example)
+ds = concatenate_datasets([ds, ds, ds, ds])
+print(f"Dataset: {len(ds)} examples")
+
+print("Loading model (Q4)...")
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, quantization_config=bnb_config, device_map="auto", trust_remote_code=True)
+
+lora_config = LoraConfig(
+    r=32, lora_alpha=64,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05, bias="none", task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+print("Starting training...")
+training_args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=7,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=2,
+    learning_rate=2e-4,
+    fp16=True,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=500,
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    report_to="none",
+    push_to_hub=True,
+    hub_model_id=REPO_ID,
+    hub_token=HF_TOKEN,
+)
+
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=ds,
+    args=training_args,
+    processing_class=tokenizer,
+)
+
+trainer.train()
+
+print("Pushing to Hub...")
+trainer.push_to_hub()
+model.push_to_hub(REPO_ID, token=HF_TOKEN)
+tokenizer.push_to_hub(REPO_ID, token=HF_TOKEN)
+print(f"DONE! Model at https://huggingface.co/{REPO_ID}")