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ZennyKenny 
posted an update about 3 hours ago
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65
I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.
mmhamdy 
posted an update about 19 hours ago
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1503
⛓ Evaluating Long Context #2: SCROLLS and ZeroSCROLLS

In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.

📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?

1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.

Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:

1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.

💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.

- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
Reality123b 
posted an update 1 day ago
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807
I got an issue with inference API

whatever model I choose, whatever input I give, it outputs "failed to fetch" in my laptop, pc, phone and every device, I tried different Accounts, etc. but still this error.

help me as my hf space (almost all of them) uses the API.
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Reality123b 
posted an update 3 days ago
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it would be nice if hf gave an option like models, datasets for users and orgs
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Xenova 
posted an update 4 days ago
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We did it. Kokoro TTS (v1.0) can now run 100% locally in your browser w/ WebGPU acceleration. Real-time text-to-speech without a server. ⚡️

Generate 10 seconds of speech in ~1 second for $0.

What will you build? 🔥
webml-community/kokoro-webgpu

The most difficult part was getting the model running in the first place, but the next steps are simple:
✂️ Implement sentence splitting, allowing for streamed responses
🌍 Multilingual support (only phonemization left)

Who wants to help?
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Reality123b 
posted an update 5 days ago
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250
Thank you HuggingFace to give me this feature!
I'm Really glad about it.
ucalyptus 
posted an update 5 days ago
Reality123b 
posted an update 6 days ago
jasoncorkill 
posted an update 12 days ago
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2654
We benchmarked @xai-org 's Aurora model, as far as we know the first public evaluation of the model at scale.

We collected 401k human annotations in over the past ~2 days for this, we have uploaded all of the annotation data here on huggingface with a fully permissive license
Rapidata/xAI_Aurora_t2i_human_preferences
ZennyKenny 
posted an update 12 days ago
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421
GradientBoostingClassifier is an algorithm supported by the Python SciKit library, and now you can quickly train an ML model using this powerful technique on any (viable) dataset in the Hugging Face Hub without a line of code.

Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier

Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.
ZennyKenny 
posted an update 24 days ago
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Really pleased with the Bring Your Own Model (BYOM) feature in Brave Browser: https://brave.com/blog/byom-nightly/

Takes about 5 minutes to configure your own locally running LLM as an in-browser assistant. Totally local, totally private, totally yours.
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ZennyKenny 
posted an update 26 days ago
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417
On-demand audio transcription is an often-requested service without many good options on the market.

Using Hugging Face Spaces with Gradio SDK and the OpenAI Whisper model, I've put together a simple interface that supports the transcription and summarisation of audio files up to five minutes in length, completely open source and running on CPU upgrade. The cool thing is that it's built without a dedicated inference endpoint, completely on public infrastructure.

Check it out: ZennyKenny/AudioTranscribe

I wrote a short article about the backend mechanics for those who are interested: https://huggingface.co/blog/ZennyKenny/on-demand-public-transcription
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Xenova 
posted an update 26 days ago
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Introducing Kokoro.js, a new JavaScript library for running Kokoro TTS, an 82 million parameter text-to-speech model, 100% locally in the browser w/ WASM. Powered by 🤗 Transformers.js. WebGPU support coming soon!
👉 npm i kokoro-js 👈

Try it out yourself: webml-community/kokoro-web
Link to models/samples: onnx-community/Kokoro-82M-ONNX

You can get started in just a few lines of code!
import { KokoroTTS } from "kokoro-js";

const tts = await KokoroTTS.from_pretrained(
  "onnx-community/Kokoro-82M-ONNX",
  { dtype: "q8" }, // fp32, fp16, q8, q4, q4f16
);

const text = "Life is like a box of chocolates. You never know what you're gonna get.";
const audio = await tts.generate(text,
  { voice: "af_sky" }, // See `tts.list_voices()`
);
audio.save("audio.wav");

Huge kudos to the Kokoro TTS community, especially taylorchu for the ONNX exports and Hexgrad for the amazing project! None of this would be possible without you all! 🤗

The model is also extremely resilient to quantization. The smallest variant is only 86 MB in size (down from the original 326 MB), with no noticeable difference in audio quality! 🤯
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