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Grantley Cullar
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OpenGPT 4o NEW UPDATES:
1. Dedicated Image and Video Engine
2. Model Choices for Voice Chat
3. Better and Faster Voice Chat
4. Various Bug fixes
Test and give feedback of New features:
KingNish/OpenGPT-4o
Future Updates:
1. Web Search (Suggested by @GPT007 and @Saionton )
2. Live Chat with Voice Chat
3. Model Choices (Suggested by @NotAiLOL )
4. Multilingual Chats.
Suggest more features that should be added. 🤗
Thanks!
1. Dedicated Image and Video Engine
2. Model Choices for Voice Chat
3. Better and Faster Voice Chat
4. Various Bug fixes
Test and give feedback of New features:
KingNish/OpenGPT-4o
Future Updates:
1. Web Search (Suggested by @GPT007 and @Saionton )
2. Live Chat with Voice Chat
3. Model Choices (Suggested by @NotAiLOL )
4. Multilingual Chats.
Suggest more features that should be added. 🤗
Thanks!
- 6 replies
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Train custom AI models with the trainer API and adapt them to 🤗
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33試してみたら、すごいことが分かりました！ Yi-1.5は日本語も中国語も理解できます。 Yi-1.5 がより多くの言語を理解し、より多くの人がそれらを利用できるようになったのは素晴らしいことです。
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@Niansuh
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narra-ai/ChatGPT
narra-ai/ChatGPT
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Okay, first pass over KAN: Kolmogorov–Arnold Networks, it looks very interesting!
Interpretability of KAN model:
May be considered mostly as a safety issue these days, but it can also be used as a form of interaction between the user and a model, as this paper argues and I think they make a valid point here. With MLP, we only interact with the outputs, but KAN is an entirely different paradigm and I find it compelling.
Scalability:
KAN shows better parameter efficiency than MLP. This likely translates also to needing less data. We're already at the point with the frontier LLMs where all the data available from the internet is used + more is made synthetically...so we kind of need something better.
Continual learning:
KAN can handle new input information w/o catastrophic forgetting, which helps to keep a model up to date without relying on some database or retraining.
Sequential data:
This is probably what most people are curious about right now, and KANs are not shown to work with sequential data yet and it's unclear what the best approach might be to make it work well both in training and regarding the interpretability aspect. That said, there's a rich long history of achieving sequential data in variety of ways, so I don't think getting the ball rolling here would be too challenging.
Mostly, I just love a new paradigm and I want to see more!
KAN: Kolmogorov-Arnold Networks (2404.19756)
Interpretability of KAN model:
May be considered mostly as a safety issue these days, but it can also be used as a form of interaction between the user and a model, as this paper argues and I think they make a valid point here. With MLP, we only interact with the outputs, but KAN is an entirely different paradigm and I find it compelling.
Scalability:
KAN shows better parameter efficiency than MLP. This likely translates also to needing less data. We're already at the point with the frontier LLMs where all the data available from the internet is used + more is made synthetically...so we kind of need something better.
Continual learning:
KAN can handle new input information w/o catastrophic forgetting, which helps to keep a model up to date without relying on some database or retraining.
Sequential data:
This is probably what most people are curious about right now, and KANs are not shown to work with sequential data yet and it's unclear what the best approach might be to make it work well both in training and regarding the interpretability aspect. That said, there's a rich long history of achieving sequential data in variety of ways, so I don't think getting the ball rolling here would be too challenging.
Mostly, I just love a new paradigm and I want to see more!
KAN: Kolmogorov-Arnold Networks (2404.19756)
- 5 replies