OpenAI API with HF

2025-02-27 16:43 UTC gpt-4o Open in ChatGPT ↗

To use an OpenAI API key with Hugging Face’s serverless inference (Inference Endpoints or Transformers API), you need to distinguish between the two services:

Hugging Face Inference Endpoints or Hosted API: You can deploy models from Hugging Face and use their API, which does not require OpenAI’s API key.
OpenAI API with Hugging Face: If you want to use OpenAI’s models (e.g., gpt-4, gpt-3.5-turbo) via Hugging Face’s interface, you’ll need to configure Hugging Face to use OpenAI’s API.

✅ Using OpenAI API with Hugging Face’s `transformers`

If you want to load OpenAI models using Hugging Face’s transformers library, you can do this:

Step 1: Install dependencies

pip install openai transformers

Step 2: Set up OpenAI API key

You need to set your OpenAI API key as an environment variable or pass it directly in Python.

import openai
import os

# Option 1: Set API key as an environment variable
os.environ["OPENAI_API_KEY"] = "your_openai_api_key"

# Option 2: Set API key directly in code (Not recommended for production)
openai.api_key = "your_openai_api_key"

Step 3: Use OpenAI models in Hugging Face

You can use OpenAI models via transformers with the pipeline method:

from transformers import pipeline

# Load OpenAI model via Hugging Face Transformers
openai_pipeline = pipeline("text-generation", model="openai/gpt-4")

# Run inference
result = openai_pipeline("What is the capital of France?")
print(result)

OR you can directly use OpenAI’s API:

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a joke"}]
)

print(response["choices"][0]["message"]["content"])

✅ Using OpenAI API with Hugging Face’s `Inference Endpoints`

If you’re trying to deploy OpenAI models via Hugging Face’s Inference Endpoints, that’s not possible because Hugging Face does not host OpenAI models. Instead, you would need to call OpenAI’s API directly from your own backend.

Alternative Approach: Using OpenAI API via FastAPI & Hugging Face

If you want to wrap OpenAI’s API and expose it via Hugging Face’s serverless inference, you can:

Create a simple FastAPI server that interacts with OpenAI’s API.
Deploy it on Hugging Face Spaces (using Gradio or Flask/FastAPI backend).

Example using FastAPI:

from fastapi import FastAPI
import openai
import os

app = FastAPI()

# Load OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY", "your_openai_api_key")

@app.get("/generate")
def generate_text(prompt: str):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return {"response": response["choices"][0]["message"]["content"]}

You can deploy this as an API and use it within Hugging Face.

🔥 Conclusion

To use OpenAI models, you need to call OpenAI’s API directly. Hugging Face does not provide OpenAI models for serverless inference.
If using Hugging Face’s transformers, you can use pipeline("text-generation", model="openai/gpt-4").
If deploying OpenAI models, create a custom API and host it using Hugging Face Spaces or your own server.

Let me know if you need more help! 🚀