ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events
Paper
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2501.03040
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Published
Spaces-explorers
AI & ML interests
Contributors who are invited to beta-test our next big feature! Contact us if you want to join this team :-)
Recent Activity
spaces-explorers's activity
duyguislakoglu
authored
a
paper
about 16 hours ago
Post
4918
👋 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
* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the
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
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
- 2 replies
Post
4395
Hello everybody,
We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.
Major improvements:
1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use
4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!
Feel free to answer in the post if you have any questions
🤗 Best regards,
Charles
We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.
Major improvements:
1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use
progress=gr.Progress(track_tqdm=True))4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!
Feel free to answer in the post if you have any questions
🤗 Best regards,
Charles
kingabzpro
posted
an
update
4 months ago
Post
1090
I believe Hugging Face should have something similar to Hacktoberfest. I miss the days when there were events like this every 3 months for audio, deep reinforcement learning, gradio themes, but it turns out everything slowed down. There are no more Hugging Face events.
@victor
@victor
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- 3 replies
kingabzpro
posted
an
update
4 months ago
Post
1286
I never imagined that Jenkins could be as powerful and easy to implement as GitHub Actions. Loving it. 🥰
kingabzpro
posted
an
update
4 months ago
Post
1838
How can I make my RAG application generate real-time responses? Up until now, I have been using Groq for fast LLM generation and the Gradio Live function. I am looking for a better solution that can help me build a real-time application without any delay.
@abidlabs
kingabzpro/Real-Time-RAG
kingabzpro/Real-Time-RAG
-
- 2 replies
bhoov
authored
3
papers
5 months ago
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
Paper
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2210.14896
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Published
•
1
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Paper
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2305.03509
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Published
•
1
Transformer Explainer: Interactive Learning of Text-Generative Models
Paper
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2408.04619
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Published
•
156
Post
4369
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
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
- 2 replies
Post
3625
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)
-----------------------------------------------------------------------
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)
Post
3328
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 🔥
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 🔥
- 5 replies
nav13n
authored
a
paper
11 months ago
Post
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 🔥
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 🔥
Post
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")Post
Just out: new custom Gradio component specifically designed for code completion models 🔥
- 1 reply
Post
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
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
Post
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)
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models (2312.06281)
monsoon-nlp
authored
a
paper
12 months ago
Post
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 ⚡️
Really nice for multi-step machine learning ademos ⚡️
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- 6 replies