Endpoint deployment script for merged model

deploy-endpoint.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""Deploy SakThai Engine LoRA Adapter to HF Inference Endpoint
+
+Run this AFTER the Kaggle notebook finishes training.
+The adapter will be at: Nanthasit/sakthai-1.5b-lora-kaggle
+
+Steps:
+  1. Merge LoRA into base model
+  2. Fix config.json (remove null quantization_config)
+  3. Upload merged model to HF Hub
+  4. Create Inference Endpoint
+"""
+
+import json, os, torch
+from huggingface_hub import HfApi, login, hf_hub_download
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# ─── Config ─────────────────────────────────────────────
+HF_TOKEN = os.environ["HF_TOKEN"]
+BASE_MODEL = "Qwen/Qwen2.5-1.5B-Instruct"
+ADAPTER_REPO = "Nanthasit/sakthai-1.5b-lora-kaggle"    # <- from Kaggle training
+MERGED_REPO = "Nanthasit/sakthai-1.5b-merged-kaggle"    # <- upload target
+ENDPOINT_NAME = "sakthai-1-5b-endpoint-v2"               # <- new endpoint
+
+# ─── Auth ───────────────────────────────────────────────
+login(token=HF_TOKEN, add_to_git_credential=True)
+api = HfApi()
+
+# ─── 1. Merge LoRA ──────────────────────────────────────
+print("Loading base model...")
+base = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+print("Loading adapter...")
+peft = PeftModel.from_pretrained(base, ADAPTER_REPO)
+merged = peft.merge_and_unload()
+
+print("Saving merged model...")
+merged.save_pretrained("./merged-output")
+tokenizer.save_pretrained("./merged-output")
+
+# ─── 2. Fix config.json ────────────────────────────────
+config_path = "./merged-output/config.json"
+config = json.loads(open(config_path).read())
+config.pop("quantization_config", None)  # <- required fix!
+config["torch_dtype"] = "bfloat16"
+with open(config_path, "w") as f:
+    json.dump(config, f, indent=2)
+print("config.json fixed")
+
+# ─── 3. Upload to Hub ───────────────────────────────────
+api.create_repo(repo_id=MERGED_REPO, repo_type="model", exist_ok=True)
+api.upload_folder(
+    folder_path="./merged-output",
+    repo_id=MERGED_REPO,
+    commit_message="Merged model from Kaggle LoRA training",
+)
+print(f"Merged model: https://huggingface.co/{MERGED_REPO}")
+
+# ─── 4. Create Inference Endpoint ───────────────────────
+endpoint = api.create_inference_endpoint(
+    name=ENDPOINT_NAME,
+    repository=MERGED_REPO,
+    accelerator="gpu",
+    instance_type="nvidia-a10g",
+    instance_size="x1",
+    region="us-east-1",
+    vendor="aws",
+    framework="pytorch",
+    task="text-generation",
+    min_replica=0,                  # scale-to-zero
+    max_replica=1,
+    scale_to_zero_timeout=15,
+)
+print(f"Endpoint: {endpoint.url}")
+print("✅ Done! Ready for inference.")