course-demos (HF Course Demos)

lewtun

posted an update 9 days ago

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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥

How? By combining step-wise reward models with tree search algorithms :)

We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"

We're open sourcing the full recipe and sharing a detailed blog post.

In our blog post we cover:

📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.

🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.

🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM

Here's the links:

- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute

- Code: https://github.com/huggingface/search-and-learn

Enjoy!

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abidlabs

posted an update 3 months ago

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👋 Hi Gradio community,

I'm excited to share that Gradio 5 will launch in October with improvements across security, performance, SEO, design (see the screenshot for Gradio 4 vs. Gradio 5), and user experience, making Gradio a mature framework for web-based ML applications.

Gradio 5 is currently in beta, so if you'd like to try it out early, please refer to the instructions below:

---------- Installation -------------

Gradio 5 depends on Python 3.10 or higher, so if you are running Gradio locally, please ensure that you have Python 3.10 or higher, or download it here: https://www.python.org/downloads/

* Locally: If you are running gradio locally, simply install the release candidate with pip install gradio --pre
* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the sdk_version to be 5.0.0b3 in the README.md file on Spaces.

In most cases, that’s all you have to do to run Gradio 5.0. If you start your Gradio application, you should see your Gradio app running, with a fresh new UI.

-----------------------------

Fore more information, please see: https://github.com/gradio-app/gradio/issues/9463

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abidlabs

posted an update 7 months ago

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4080

𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs

We're working on making that a lot easier with 𝗚𝗿𝗮𝗱𝗶𝗼 and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace

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abidlabs

posted an update 8 months ago

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Open Models vs. Closed APIs for Software Engineers
-----------------------------------------------------------------------

If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.

But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:

1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.

2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.

3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)

lewtun

posted an update 9 months ago

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Introducing Zephyr 141B-A35B 🪁:

HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1

Yesterday, Mistral released their latest base model (via magnet link of course 😅) and the community quickly converted it to transformers format and pushed it to the Hub: mistral-community/Mixtral-8x22B-v0.1

Early evals of this model looked extremely strong, so we teamed up with Argilla and KAIST AI to cook up a Zephyr recipe with a few new alignment techniques that came out recently:

🧑‍🍳 Align the base model with Odds Ratio Preference Optimisation (ORPO). This novel algorithm developed by @JW17 and @nlee-208 and @j6mes and does not require an SFT step to achieve high performance and is thus much more computationally efficient than methods like DPO and PPO.

🦫 Use a brand new dataset of 7k high-quality, multi-turn preferences that has been developed by our friends at Argilla. To create this dataset, they took the excellent Capybara SFT dataset from @LDJnr LDJnr/Capybara and converted it into a preference dataset by augmenting the final turn with responses from new LLMs that were then ranked by GPT-4.

What we find especially neat about this approach is that training on 7k samples only takes ~1.3h on 4 H100 nodes, yet produces a model that is very strong on chat benchmarks like IFEval and BBH.

Kudos to @alvarobartt @JW17 and @nlee-208 for this very nice and fast-paced collab!

For more details on the paper and dataset, checkout our collection: HuggingFaceH4/zephyr-orpo-6617eba2c5c0e2cc3c151524

abidlabs

posted an update 9 months ago

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Introducing the Gradio API Recorder 🪄

Every Gradio app now includes an API recorder that lets you reconstruct your interaction in a Gradio app as code using the Python or JS clients! Our goal is to make Gradio the easiest way to build ML APIs, not just UIs 🔥

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lewtun

posted an update 10 months ago

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Can we align code generation models to be good at chat without compromising their base capabilities 🤔?

This was the question the H4 team asked itself when BigCode released StarCoder2 a bit over a week ago. We knew that code models like deepseek-ai/deepseek-coder-6.7b-instruct and m-a-p/OpenCodeInterpreter-DS-33B get impressive scores on code benchmarks like HumanEval, but they tend to score poorly on chat benchmarks like MT Bench and IFEval. We also knew that the Zephyr recipe we applied to Mistral 7B produced a strong chat model, so we wondered -- could be tweaked to produce a strong coding assistant?

It turns out the answer is yes and I'm happy to share StarChat2, a DPO fine-tune of StarCoder2 15B that scores highly on both HumanEval and MT Bench / IFEval 🌟!

The most interesting lesson for me was that you get better models by blending in more code/math data than chat during the SFT step - in terms of tokens, we found a ratio of 3:1 worked best.

Anyway, here's a demo of the model, along with all the code and datasets we used to train it:

* Demo: HuggingFaceH4/starchat2-playground
* Collection: HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
* Recipe: https://github.com/huggingface/alignment-handbook

Hope it's useful to others!

3 replies

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sanchit-gandhi

posted an update 10 months ago

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Why does returning timestamps help Whisper reduce hallucinations? 🧐

Empirically, most practitioners have found that setting return_timestamps=True helps reduce hallucinations, particularly when doing long-form evaluation with Transformers’ “chunked” algorithm.

But why does this work?..

My interpretation is that forcing the model to predict timestamps is contradictory to hallucinations. Suppose you have the transcription:

The cat sat on the on the on the mat.

Where we have a repeated hallucination for “on the”. If we ask the model to predict timestamps, then the “on the” has to contribute to the overall segment-level timing, e.g.:

<|0.00|> The cat sat on the on the on the mat.<|5.02|>

However, it’s impossible to fit 3 copies of “on the” within the time allocation given to the segment, so the probability for this hallucinatory sequence becomes lower, and the model actually predicts the correct transcription with highest probability:

<|0.00|> The cat sat on the mat.<|5.02|>

In this sense, the end timestamp is of the opposite of the initial timestamp constraint they describe in Section 4.5 of the paper Robust Speech Recognition via Large-Scale Weak Supervision (2212.04356) → it helps the model remove extra words at the end of the sequence (rather than the initial timestamp which helps when the model ignores words at the start), but the overall principle is the same (using timestamps to improve the probability of more realistic sequences).

Leaving it open to you: why do you think timestamps reduces Whisper hallucinations?

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abidlabs

posted an update 11 months ago

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Necessity is the mother of invention, and of Gradio components.

Sometimes we realize that we need a Gradio component to build a cool application and demo, so we just build it. For example, we just added a new gr.ParamViewer component because we needed it to display information about Python & JavaScript functions in our documentation.

Of course, our users should be able able to do the same thing for their machine learning applications, so that's why Gradio lets you build custom components, and publish them to the world 🔥

abidlabs

posted an update 11 months ago

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Lots of cool Gradio custom components, but is the most generally useful one I've seen so far: insert a Modal into any Gradio app by using the modal component!

from gradio_modal import Modal

with gr.Blocks() as demo:
    gr.Markdown("### Main Page")
    gr.Textbox("lorem ipsum " * 1000, lines=10)

    with Modal(visible=True) as modal:
        gr.Markdown("# License Agreement")

abidlabs

posted an update 11 months ago

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Just out: new custom Gradio component specifically designed for code completion models 🔥

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abidlabs

posted an update 11 months ago

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The next version of Gradio will be significantly more efficient (as well as a bit faster) for anyone who uses Gradio's streaming features. Looking at you chatbot developers @oobabooga @pseudotensor :)

The major change that we're making is that when you stream data, Gradio used to send the entire payload at each token. This is generally the most robust way to ensure all the data is correctly transmitted. We've now switched to sending "diffs" --> so at each time step, we automatically compute the diff between the most recent updates and then only send the latest token (or whatever the diff may be). Coupled with the fact that we are now using SSE, which is a more robust communication protocol than WS (SSE will resend packets if there's any drops), we should have the best of both worlds: efficient *and* robust streaming.

Very cool stuff @aliabid94 ! PR: https://github.com/gradio-app/gradio/pull/7102

abidlabs

posted an update 11 months ago

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The most interesting LLM benchmark I've seen so far... reminder that there's lots of characterization of LLMs still yet to do.

EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models (2312.06281)

abidlabs

posted an update 11 months ago

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Gradio 4.16 introduces a new flow: you can hide/show Tabs or make them interactive/non-interactive.

Really nice for multi-step machine learning ademos ⚡️

6 replies

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abidlabs

posted an update 11 months ago

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✨ Excited to release gradio 4.16. New features include:

🐻‍❄️ Native support for Polars Dataframe
🖼️ Gallery component can be used as an input
⚡ Much faster streaming for low-latency chatbots
📄 Auto generated docs for custom components

... and much more! This is HUGE release, so check out everything else in our changelog: https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md

3 replies

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abidlabs

posted an update 11 months ago

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𝗛𝗼𝘄 𝘄𝗲 𝗺𝗮𝗱𝗲 𝗚𝗿𝗮𝗱𝗶𝗼 𝗳𝗮𝘀𝘁𝗲𝗿 𝗯𝘆... 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝘁 𝗱𝗼𝘄𝗻!

About a month ago, @oobabooga (who built the popular text generation webui) reported an interesting issue to the Gradio team. After upgrading to Gradio 4, @oobabooga noticed that chatbots that streamed very quickly had a lag before their text would show up in the Gradio app.

After some investigation, we determined that the Gradio frontend would receive the updates from the backend immediately, but the browser would lag before rendering the changes on the screen. The main difference between Gradio 3 and Gradio 4 was that we migrated the communication protocol between the backend and frontend from Websockets (WS) to Server-Side Events (SSE), but we couldn't figure out why this would affect the browser's ability to render the streaming updates it was receiving.

After diving deep into browsers events, @aliabid94 and @pngwn made a realization: most browsers treat WS events (specifically the WebSocket.onmessage function) with a lower priority than SSE events (EventSource.onmessage function), which allowed the browser to repaint the window between WS messages. With SSE, the streaming updates would stack up in the browser's event stack and be prioritized over any browser repaint. The browser would eventually clear the stack but it would take some time to go through each update, which produced a lag.

We debated different options, but the solution that we implemented was to introduce throttling: we slowed down how frequently we would push updates to the browser event stack to a maximum rate of 20/sec. Although this seemingly “slowed down” Gradio streaming, it actually would allow browsers to process updates in real-time and provide a much better experience to end users of Gradio apps.

See the PR here: https://github.com/gradio-app/gradio/pull/7084

Kudos to @aliabid94 and @pngwn for the fix, and to @oobabooga and @pseudotensor for helping us test it out!

3 replies

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abidlabs

posted an update 11 months ago

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There's a lot of interest in machine learning models that generate 3D objects, so Gradio now supports previewing STL files natively in the Model3D component. Huge thanks to Monius for the contribution 🔥🔥

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sanchit-gandhi

updated a Space 11 months ago

Runtime error

2

👁

Gtzan Samples

abidlabs

posted an update 11 months ago

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𝐄𝐦𝐛𝐫𝐚𝐜𝐞𝐝 𝐛𝐲 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞: 𝐭𝐡𝐞 𝐈𝐧𝐬𝐢𝐝𝐞 𝐒𝐭𝐨𝐫𝐲 𝐨𝐟 𝐎𝐮𝐫 𝐒𝐭𝐚𝐫𝐭𝐮𝐩’𝐬 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

In late 2021, our team of five engineers, scattered around the globe, signed the papers to shut down our startup, Gradio. For many founders, this would have been a moment of sadness or even bitter reflection.

But we were celebrating. We were getting acquired by Hugging Face!

We had been working very hard towards this acquisition, but for weeks, the acquisition had been blocked by a single investor. The more we pressed him, the more he buckled down, refusing to sign off on the acquisition. Until, unexpectedly, the investor conceded, allowing us to join Hugging Face.

For the first time since our acquisition, I’m writing down the story in detail, hoping that it may shed some light into the obscure world of startup acquisitions and what decisions founders can make to improve their odds for a successful acquisition.

To understand how we got acquired by Hugging Face, you need to know why we started Gradio.

𝐀𝐧 𝐈𝐝𝐞𝐚 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐇𝐞𝐚𝐫𝐭

Two years before the acquisition, in early 2019, I was working on a research project at Stanford. It was the third year of my PhD, and my labmates and I had trained a machine learning model that could predict patient biomarkers (such as whether patients had certain diseases or an implanted pacemaker) from an ultrasound image of their heart — as well as a cardiologist.

Naturally, cardiologists were skeptical... read the rest of the story here: https://twitter.com/abidlabs/status/1745533306492588303

1 reply

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lewtun

authored a paper about 1 year ago

RAFT: A Real-World Few-Shot Text Classification Benchmark

Paper • 2109.14076 • Published Sep 28, 2021 • 2

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RAFT: A Real-World Few-Shot Text Classification Benchmark

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