hugging-face（拥抱脸）

Troubleshooting

If requests fail, run zero doctor check-connector --env-name HUGGING_FACE_TOKEN or zero doctor check-connector --url https://huggingface.co/api/whoami-v2 --method GET

How to Use

All examples below assume you have HUGGING_FACE_TOKEN set.

The base URLs are:

• Hub API: https://huggingface.co/api
• Inference API: https://router.huggingface.co

1. Verify Account (whoami)

Check your token and account information:

curl -s "https://huggingface.co/api/whoami-v2" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '{name: .name, email: .email, type: .type}'

2. Search Models

Search for models with filters:

curl -s "https://huggingface.co/api/models?search=llama&sort=downloads&direction=-1&limit=5" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

Filter by pipeline task:

curl -s "https://huggingface.co/api/models?pipeline_tag=text-generation&sort=trending&limit=5" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

Common query parameters:

• search - Search term
• pipeline_tag - Filter by task (text-generation, text-to-image, fill-mask, etc.)
• sort - Sort by: downloads, likes, trending, created_at, lastModified
• direction - Sort direction: -1 (descending), 1 (ascending)
• limit - Number of results (default 30)
• author - Filter by author/organization (e.g. meta-llama)
• filter - Filter by tags (e.g. pytorch, en)

3. Get Model Details

Get detailed information about a specific model:

curl -s "https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '{id, downloads, likes, pipeline_tag, tags: .tags[:5]}'

4. Search Datasets

Search for datasets:

curl -s "https://huggingface.co/api/datasets?search=squad&sort=downloads&direction=-1&limit=5" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

5. Get Dataset Details

Get detailed information about a specific dataset:

curl -s "https://huggingface.co/api/datasets/squad" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '{id, downloads, likes, tags: .tags[:5]}'

6. Search Spaces

Search for Spaces:

curl -s "https://huggingface.co/api/spaces?search=chatbot&sort=likes&direction=-1&limit=5" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

7. List Repository Files

List files in a model repository:

curl -s "https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct/tree/main" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[] | {path: .rfilename, size}'

For datasets, replace models with datasets:

curl -s "https://huggingface.co/api/datasets/squad/tree/main" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[] | {path: .rfilename, size}'

8. Run Serverless Inference (Text Generation)

Run text generation using the Inference API with an OpenAI-compatible endpoint:

Write to /tmp/hugging_face_request.json:

{
  "model": "meta-llama/Llama-3.1-8B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ],
  "max_tokens": 100
}

Then run:

curl -s "https://router.huggingface.co/hf-inference/v1/chat/completions" --header "Content-Type: application/json" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" -d @/tmp/hugging_face_request.json | jq -r '.choices[0].message.content'

9. Run Serverless Inference (Text-to-Image)

Generate an image from text:

curl -s "https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" --header "Content-Type: application/json" -d '{"inputs": "A cute cat wearing sunglasses"}' --output /tmp/hugging_face_image.png

The response is the raw image binary saved to the output file.

10. Run Serverless Inference (Embeddings)

Generate text embeddings:

Write to /tmp/hugging_face_request.json:

{
  "inputs": "Hello, how are you?"
}

Then run:

curl -s "https://router.huggingface.co/hf-inference/models/sentence-transformers/all-MiniLM-L6-v2" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" --header "Content-Type: application/json" -d @/tmp/hugging_face_request.json | jq '.[0][:5]'

11. Run Serverless Inference (Text Classification)

Classify text using sentiment analysis or other classification models:

Write to /tmp/hugging_face_request.json:

{
  "inputs": "I love using Hugging Face!"
}

Then run:

curl -s "https://router.huggingface.co/hf-inference/models/distilbert-base-uncased-finetuned-sst-2-english" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" --header "Content-Type: application/json" -d @/tmp/hugging_face_request.json | jq .

12. List Models with Inference Provider Support

Find models available for serverless inference:

curl -s "https://huggingface.co/api/models?inference_provider=all&pipeline_tag=text-generation&sort=trending&limit=10" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

Filter by a specific provider:

curl -s "https://huggingface.co/api/models?inference_provider=hf-inference&pipeline_tag=text-to-image&limit=5" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.[].id'

13. Get Model Inference Providers

Check which inference providers serve a specific model:

curl -s "https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMapping" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" | jq '.inferenceProviderMapping'

14. Create a Repository

Create a new model repository:

Write to /tmp/hugging_face_request.json:

{
  "name": "my-new-model",
  "type": "model",
  "private": true
}

Then run:

curl -s -X POST "https://huggingface.co/api/repos/create" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" --header "Content-Type: application/json" -d @/tmp/hugging_face_request.json | jq .

Repository types: model, dataset, space

15. Delete a Repository

Delete a repository (requires write token):

Write to /tmp/hugging_face_request.json:

{
  "name": "my-new-model",
  "type": "model"
}

Then run:

curl -s -X DELETE "https://huggingface.co/api/repos/delete" --header "Authorization: Bearer $HUGGING_FACE_TOKEN" --header "Content-Type: application/json" -d @/tmp/hugging_face_request.json | jq .

Guidelines

1. Use Bearer authentication: Pass the token via Authorization: Bearer $HUGGING_FACE_TOKEN header
2. Prefer serverless inference for quick tasks: Use the Inference API for prototyping; deploy Inference Endpoints for production
3. Check model availability: Not all models support serverless inference; use the inference_provider filter to find available models
4. Use the OpenAI-compatible chat endpoint for text generation: https://router.huggingface.co/hf-inference/v1/chat/completions
5. Complex JSON payloads: Write JSON to a temp file and use -d @/tmp/hugging_face_request.json to avoid shell quoting issues
6. Respect rate limits: Authenticated requests have higher rate limits; consider a Pro account for heavy usage
7. Model IDs use org/name format: Always specify the full model ID (e.g. meta-llama/Llama-3.1-8B-Instruct)