Releases · huggingface/huggingface_hub

{
  "model": "Qwen/Qwen2.5-72B-Instruct",
  "provider": "nebius",
  "inputs": [
    {
      "type": "promptString",
      "id": "hf-token",
      "description": "Token for Hugging Face API access",
      "password": true
    }
  ],
  "servers": [
    {
      "type": "http",
      "url": "https://huggingface.co/mcp",
      "headers": {
        "Authorization": "Bearer ${input:hf-token}"
      }
    }
  ]
}

Find more examples in https://huggingface.co/datasets/tiny-agents/tiny-agents

Contributors

Wauplin

Assets 2

25 Jun 12:18

hanouticelina

v0.33.1

c8ffea9

[v0.33.1]: Inference Providers Bug Fixes, Tiny-Agents Message handling Improvement, and Inference Endpoints Health Check Update

Full Changelog: v0.33.0...v0.33.1

This release introduces bug fixes for chat completion type compatibility and feature extraction parameters, enhanced message handling in tiny-agents, and updated inference endpoint health check:

[Tiny agents] Add tool call to messages #3159 by @NielsRogge
fix: update payload preparation to merge parameters into the output dictionary #3160 by @mishig25
fix(inference_endpoints): use GET healthRoute instead of GET / to check status #3165 by @mfuntowicz
Recursive filter_none in Inference Providers #3178 by @Wauplin

Contributors

mfuntowicz, Wauplin, and 2 other contributors

Assets 2

11 Jun 14:14

Wauplin

v0.33.0

d5dff4e

[v0.33.0]: Welcoming Featherless.AI and Groq as Inference Providers!

⚡ New provider: Featherless.AI

Featherless AI is a serverless AI inference provider with unique model loading and GPU orchestration abilities that makes an exceptionally large catalog of models available for users. Providers often offer either a low cost of access to a limited set of models, or an unlimited range of models with users managing servers and the associated costs of operation. Featherless provides the best of both worlds offering unmatched model range and variety but with serverless pricing. Find the full list of supported models on the models page.

from huggingface_hub import InferenceClient

client = InferenceClient(provider="featherless-ai")

completion = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-0528", 
    messages=[
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ], 
)

print(completion.choices[0].message)

✨ Support for Featherless.ai as inference provider by @pohnean in #3081

⚡ New provider: Groq

At the heart of Groq's technology is the Language Processing Unit (LPU™), a new type of end-to-end processing unit system that provides the fastest inference for computationally intensive applications with a sequential component, such as Large Language Models (LLMs). LPUs are designed to overcome the limitations of GPUs for inference, offering significantly lower latency and higher throughput. This makes them ideal for real-time AI applications.

Groq offers fast AI inference for openly-available models. They provide an API that allows developers to easily integrate these models into their applications. It offers an on-demand, pay-as-you-go model for accessing a wide range of openly-available LLMs.

from huggingface_hub import InferenceClient

client = InferenceClient(provider="groq")

completion = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image in one sentence."},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://vagabundler.com/wp-content/uploads/2019/06/P3160166-Copy.jpg"},
                },
            ],
        }
    ],
)

print(completion.choices[0].message)

ADd Groq provider by @Wauplin in #3157

🤖 MCP and Tiny-agents

It is now possible to run tiny-agents using a local server e.g. llama.cpp. 100% local agents are right behind the corner!

[MCP] Add local/remote endpoint inference support by @hanouticelina in #3121

Fixing some DX issues in the tiny-agents CLI.

Fix tiny-agents cli exit issues by @Wauplin in #3125
[MCP] reinject JSON parse & runtime tool errors back into the chat history by @hanouticelina in #3137

📚 Documentation

New translation from the Hindi-speaking community, for the community!

Added Hindi translation for git_vs_http.md in concepts section by @february-king in #3156

🛠️ Small fixes and maintenance

😌 QoL improvements

Make hf-xet more silent by @Wauplin in #3124
[HfApi] Collections in collections by @hanouticelina in #3120
Fix inference search by @Wauplin in #3022
[Inference Providers] Raise warning if provider's status is in error mode by @hanouticelina in #3141

🐛 Bug and typo fixes

Fix snapshot_download on very large repo (>50k files) by @Wauplin in #3122
fix tqdm_class argument of subclass of tqdm by @andyxning in #3111
fix quality by @hanouticelina in #3128
second example in oauth documentation by @thanosKivertzikidis in #3136
fix table question answering by @hanouticelina in #3154

🏗️ internal

Create claude.yml by @julien-c in #3118
[Internal] prepare for 0.33.0 release by @hanouticelina in #3138

Significant community contributions

The following contributors have made significant changes to the library over the last release:

@pohnean
- ✨ Support for Featherless.ai as inference provider (#3081)
@february-king
- Added Hindi translation for git_vs_http.md in concepts section (#3156)