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Undertone0809 / promptulate

🚀Lightweight Large language model automation and Autonomous Language Agents development framework. Build your LLM Agent Application in a pythonic way!
https://promptulate.cn/
Apache License 2.0
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:arrow_up: Bump huggingface-hub from 0.24.6 to 0.26.2 #1008

Open dependabot[bot] opened 3 weeks ago

dependabot[bot] commented 3 weeks ago

Bumps huggingface-hub from 0.24.6 to 0.26.2.

Release notes

Sourced from huggingface-hub's releases.

[v0.26.2] Fix: Reflect API response changes in file and repo security status fields

This patch release includes updates to align with recent API response changes:

  • Update how file's security metadata is retrieved following changes in the API response (#2621).
  • Expose repo security status field in ModelInfo (#2639).

Full Changelog: https://github.com/huggingface/huggingface_hub/compare/v0.26.1...v0.26.2

v0.26.0: Multi-tokens support, conversational VLMs and quality of life improvements

🔐 Multiple access tokens support

Managing fine-grained access tokens locally just became much easier and more efficient! Fine-grained tokens let you create tokens with specific permissions, making them especially useful in production environments or when working with external organizations, where strict access control is essential.

To make managing these tokens easier, we've added a ✨ new set of CLI commands ✨ that allow you to handle them programmatically:

  • Store multiple tokens on your machine by simply logging in with the login() command with each token:
huggingface-cli login
  • Switch between tokens and choose the one that will be used for all interactions with the Hub:
huggingface-cli auth switch
  • List available access tokens on your machine:
huggingface-cli auth list
  • Delete a specific token from your machine with:
huggingface-cli logout [--token-name TOKEN_NAME]

✅ Nothing changes if you are using the HF_TOKEN environment variable as it takes precedence over the token set via the CLI. More details in the documentation. 🤗

⚡️ InferenceClient improvements

🖼️ Conversational VLMs support

Conversational vision-language models inference is now supported with InferenceClient's chat completion!

from huggingface_hub import InferenceClient

works with remote url or base64 encoded url


image_url ="https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"


client = InferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct")
output = client.chat.completions.create(
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
</tr></table>

... (truncated)

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