{
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  "path": "/t/using-a-hugging-face-model-offline-to-support-code-generation-in-vscode/174627#post_3",
  "publishedAt": "2026-03-26T12:52:49.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "https://huggingface.co/docs/transformers/installation",
    "https://fastapi.tiangolo.com/tutorial/"
  ],
  "textContent": "Hi there,\n\nThanks 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.\n\nHere’s a breakdown of what’s happening and some guidance:\n\n1. Why VSCode extensions are giving login prompts\n\nMost VSCode AI extensions (Continue, LM Studio, CodeGPT, AI Tools, OpenClaw) are designed to work with **cloud-hosted AI services** , which usually require:\n\n  * API keys / OAuth logins (Google, GitHub, Hugging Face tokens)\n\n  * Specific API endpoints with expected authentication\n\n\n\n\nIf 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.\n\nSome 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`.\n\n2. Using a local Hugging Face model with VSCode\n\nSince you want **fully offline, private sessions** , the cleanest approach is usually **not to rely on prebuilt VSCode extensions**. Instead, you can:\n\n  * Run your **local API** (uvicorn server) as you have.\n\n  * Write a **small Python wrapper** in VSCode that sends prompts to your `/api/generate` or `/api/chatbot` endpoint and reads the response.\n\n  * You can even attach this to a **VSCode task** or a **Jupyter notebook cell** to interactively test prompts.\n\n\n\n\nThis avoids the login/authentication issues entirely, and you have full control over the request/response format.\n\nA minimal example might look like this:\n\n\n    import requests\n\n    url = \"http://localhost:11434/api/generate\"\n    payload = {\"prompt\": \"Hello, can you explain Python functions?\", \"max_tokens\": 100}\n\n    response = requests.post(url, json=payload)\n    data = response.json()\n\n    print(data.get(\"response\"))\n\n\nThis directly queries your local Hugging Face model, waits for the dictionary with `\"response\"`, and prints it—no Google logins, no tokens, fully offline.\n\n* * *\n\n### 3. Why some VSCode integrations may never fully support Hugging Face offline\n\n  * Many extensions are **tightly coupled to cloud APIs** for features like token usage tracking, conversation history, and context management.\n\n  * Hugging Face local models **don’t provide the same cloud API endpoints** , so extensions can’t natively talk to them without **custom adapters**.\n\n\n\n\nUnless the extension explicitly supports a **custom HTTP endpoint with your JSON structure** , you’ll keep running into these issues.\n\n* * *\n\n###  Recommended path forward\n\n  1. Keep your **uvicorn server + local Hugging Face model** as you have.\n\n  2. Use a **custom Python script or notebook in VSCode** to interact with the model.\n\n  3. Optionally, write a **lightweight VSCode extension** yourself to call your API if you want editor integration—this is doable without external login.\n\n\n\n\n* * *\n\nFor a step-by-step guide, Hugging Face has a **great tutorial for running models locally via Python** :\n\nhttps://huggingface.co/docs/transformers/installation\n\nAnd for building **custom APIs to interface with VSCode or other clients** :\n\nhttps://fastapi.tiangolo.com/tutorial/\n\n* * *\n\n**TL;DR:**\n\nVSCode 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.\n\nYou already have everything set up you just need a lightweight wrapper in VSCode to send prompts and handle responses.",
  "title": "Using a Hugging Face Model offline to support code generation in VSCode"
}