Upload train_gptoss.sh with huggingface_hub

train_gptoss.sh

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+#!/bin/bash
+set -e
+pip install torch transformers peft trl datasets accelerate sentencepiece huggingface_hub -q 2>&1 | tail -1
+
+TOKEN="${HF_TOKEN:?Set HF_TOKEN first}"
+REPO="${HF_REPO_ID:-HeeBive/ZeroSec}"
+
+python3 << 'PYEOF' > training.log 2>&1
+import os, torch, shutil, json
+os.environ["TORCH_COMPILE_DISABLE"] = "1"
+TOKEN = os.environ.get("HF_TOKEN")
+REPO = os.environ.get("HF_REPO_ID", "HeeBive/ZeroSec")
+from huggingface_hub import hf_hub_download, HfApi
+from datasets import Dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
+from peft import LoraConfig, get_peft_model
+from trl import SFTTrainer
+
+MODEL = "openai/gpt-oss-20b"
+OUT = "./adapter"
+
+print("DL data...")
+p = hf_hub_download("HeeBive/ZeroSec-7B", "training_data.jsonl", repo_type="model", token=TOKEN)
+raw = [json.loads(l) for l in open(p) if l.strip()]
+print(f"{len(raw)} samples")
+def f(e): return {"text": f"<|im_start|>user\n{e['instruction']}<|im_end|>\n<|im_start|>assistant\n{e['response']}<|im_end|>"}
+ds = Dataset.from_list(raw).map(f)
+tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
+if tok.pad_token is None: tok.pad_token = tok.eos_token
+ds = ds.map(lambda x: tok(x["text"], truncation=True, max_length=512), batched=True, remove_columns=ds.column_names)
+
+print("Load model...")
+m = AutoModelForCausalLM.from_pretrained(MODEL, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+for p in m.parameters(): p.requires_grad = False
+m.gradient_checkpointing_enable()
+m = get_peft_model(m, LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj","k_proj","v_proj","o_proj"], task_type="CAUSAL_LM", bias="none"))
+args = TrainingArguments(output_dir=OUT, per_device_train_batch_size=1, gradient_accumulation_steps=8, num_train_epochs=1, learning_rate=1e-4, bf16=True, logging_steps=5, optim="adamw_torch", report_to="none")
+SFTTrainer(model=m, processing_class=tok, train_dataset=ds, args=args).train()
+m.save_pretrained(OUT); tok.save_pretrained(OUT)
+print("Uploading...")
+HfApi(token=TOKEN).upload_folder(folder_path=OUT, repo_id=REPO, repo_type="model", token=TOKEN)
+print(f"✅ {REPO}")
+PYEOF