Upload your custom model to 🤗 Hub

Your fine-tuned or LoRA-adapted model is now ready — so how do you share it with teammates, the community, or your own apps? Easy: upload it to the Hugging Face Hub, the world’s biggest open LLM model library.

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✅ Why Upload to the Hub?

✔️ Free hosting for models, datasets, and demo Spaces. ✔️ Easy to version, update, and share with a link. ✔️ Load your model from anywhere with from_pretrained(). ✔️ Collaborate — add teammates or make it public. ✔️ Optional: Deploy instantly with Inference API or Spaces.

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✅ Step 1️⃣ — Log In to Your Account

Make sure you’re logged in from your terminal:

huggingface-cli login

Paste your token when asked. (You can create a new token under Settings ➜ Access Tokens.)

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✅ Step 2️⃣ — Choose a Model Name

Decide what to call it:

• Be descriptive: my-python-helper-llm

• Use lowercase and dashes for readability.

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✅ Step 3️⃣ — Use transformers to Push

If you used Trainer or PEFT:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "my-python-helper-llm"

# Load your fine-tuned model
model = AutoModelForCausalLM.from_pretrained("./path_to_your_finetuned_model")
tokenizer = AutoTokenizer.from_pretrained("base_model_name")

# Push to Hub
model.push_to_hub(model_name)
tokenizer.push_to_hub(model_name)

✅ Done! Your model now lives at: https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME

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✅ If You Used LoRA/PEFT

If you fine-tuned with LoRA, you’ll usually push just the adapter:

from peft import PeftModel

peft_model = PeftModel.from_pretrained("base_model_name", "path_to_lora_adapter")
peft_model.push_to_hub("my-python-helper-lora")

When someone wants to use it:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("base_model_name")
model = PeftModel.from_pretrained(base, "YOUR_USERNAME/my-python-helper-lora")

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✅ Step 4️⃣ — Add a README.md

A good model card includes:

• What the model does (and doesn’t do)

• How it was trained (data, steps, license)

• Example usage

• Limitations and disclaimers

The Hub will auto-create a basic README. Edit it in the web UI!

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✅ Step 5️⃣ — Make It Public (or Private)

By default, models are public. You can make them private for team use:

huggingface-cli repo create my-python-helper-llm --private

Or toggle visibility in the web interface.

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✅ Step 6️⃣ — Test It!

Try loading your model from scratch to verify it works:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/my-python-helper-llm")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/my-python-helper-llm")

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✅ Good Practices

✔️ Add tags like RAG, LoRA, instruction-tuned to help others find your model. ✔️ Include an example inference.py or Gradio link. ✔️ Pin important files (config.json, tokenizer.json).

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🗝️ Key Takeaway

Uploading to the 🤗 Hub turns your local model into a cloud-ready, plug-and-play asset — shareable, versioned, and reusable in any script or app.

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➡️ Next: Learn how to serve your model with an Inference API or deploy it with accelerate or FastAPI for live production!