Using a Hugging Face Model offline to support code generation in VSCode

Hi there,

Thanks for the detailed explanation it really helps clarify exactly what you’re trying to do. From what you’ve described, it sounds like your setup is solid on the Hugging Face offline model side , and the main challenge is getting VSCode extensions to talk to your local API without requiring logins or external accounts.

Here’s a breakdown of what’s happening and some guidance:

1. Why VSCode extensions are giving login prompts

Most VSCode AI extensions (Continue, LM Studio, CodeGPT, AI Tools, OpenClaw) are designed to work with cloud-hosted AI services , which usually require:

• API keys / OAuth logins (Google, GitHub, Hugging Face tokens)

• Specific API endpoints with expected authentication

If you try to point them at a local model, they often still try to reach the cloud or validate a token , which is why you’re hitting walls or errors.

Some extensions, like AI Tools, let you specify a custom endpoint , but they often expect the endpoint to return specific JSON formats (tags, metadata, etc.), which is why you had to spoof /api/tags.

1. Using a local Hugging Face model with VSCode

Since you want fully offline, private sessions , the cleanest approach is usually not to rely on prebuilt VSCode extensions. Instead, you can:

• Run your local API (uvicorn server) as you have.

• Write a small Python wrapper in VSCode that sends prompts to your /api/generate or /api/chatbot endpoint and reads the response.

• You can even attach this to a VSCode task or a Jupyter notebook cell to interactively test prompts.

This avoids the login/authentication issues entirely, and you have full control over the request/response format.

A minimal example might look like this:

import requests

url = "http://localhost:11434/api/generate"
payload = {"prompt": "Hello, can you explain Python functions?", "max_tokens": 100}

response = requests.post(url, json=payload)
data = response.json()

print(data.get("response"))

This directly queries your local Hugging Face model, waits for the dictionary with "response", and prints it—no Google logins, no tokens, fully offline.

3. Why some VSCode integrations may never fully support Hugging Face offline

• Many extensions are tightly coupled to cloud APIs for features like token usage tracking, conversation history, and context management.

• Hugging Face local models don’t provide the same cloud API endpoints , so extensions can’t natively talk to them without custom adapters.

Unless the extension explicitly supports a custom HTTP endpoint with your JSON structure , you’ll keep running into these issues.

Recommended path forward

1. Keep your uvicorn server + local Hugging Face model as you have.

2. Use a custom Python script or notebook in VSCode to interact with the model.

3. Optionally, write a lightweight VSCode extension yourself to call your API if you want editor integration—this is doable without external login.

For a step-by-step guide, Hugging Face has a great tutorial for running models locally via Python :

https://huggingface.co/docs/transformers/installation

And for building custom APIs to interface with VSCode or other clients :

https://fastapi.tiangolo.com/tutorial/

TL;DR:

VSCode AI extensions often expect cloud APIs with logins. For fully offline Hugging Face models, the most reliable approach is to talk to your local API directly via Python , rather than forcing the extensions to work.

You already have everything set up you just need a lightweight wrapper in VSCode to send prompts and handle responses.