| import os | |
| from huggingface_hub import login, upload_folder | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| # ------------------------------- | |
| # 1️⃣ Login safely | |
| # ------------------------------- | |
| # Use environment variable for your token | |
| token = os.environ.get("HF_TOKEN") | |
| if not token: | |
| token = input("Enter your Hugging Face token (won't be saved): ") | |
| login(token=token) | |
| # ------------------------------- | |
| # 2️⃣ Upload the model folder | |
| # ------------------------------- | |
| # This folder should contain pytorch_model.bin, config.json, tokenizer.json | |
| upload_folder( | |
| folder_path=".", # current folder | |
| repo_id="picklefried706/NEON", # your HF repo | |
| repo_type="model" | |
| ) | |
| print("✅ Upload complete!") | |
| # ------------------------------- | |
| # 3️⃣ Test your model | |
| # ------------------------------- | |
| model_name = "picklefried706/NEON" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") | |
| chat = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| prompt = "User: Hello! How are you?\nAssistant:" | |
| response = chat(prompt, max_new_tokens=150) | |
| print("\n--- Model Response ---") | |
| print(response[0]['generated_text']) | |