yahma (Gene Ruebsamen)

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GPU Poor LLM Arena

Compact LLM Battle Arena: Frugal AI Face-Off!

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FLUX Prompt Generator

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[NEW] Assistants

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#357 opened 11 months ago by

victor

reacted to KingNish's post with 👍 7 months ago

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New Updates OpenGPT 4o
1. Live Chat (also known as video chat) (very powerful and fast, it can even identify famous places and persons)
2. Powerful Image Generation

Test and give feedback of New features:
KingNish/OpenGPT-4o

Future Updates
1. PDF Chat
2. Human like speech (Using Parler tts expresso)
3. Multilingual support for voice chat

Suggest more features that should be added. 🤗

Edit: Live Chat is now very powerful (than prev)

26 replies

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upvoted an article 9 months ago

Article

Introducing Idefics2: A Powerful 8B Vision-Language Model for the community

Apr 15

• 170

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Artificialguybr Demo Lora

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HuggingFaceH4/zephyr-7b-gemma-v0.1

Text Generation • Updated Mar 3 • 615 • 121

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MeloTTS

Fast, efficient, & multilingual text-to-speech

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Vision Arena (Testing VLMs side-by-side)

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Whisper Speech X DreamTalk

Combine voice cloning and portrait lipsync animation

reacted to xianbao's post with ❤️ 11 months ago

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There appears to be a huge misunderstanding regarding the licensing requirements for open sourced Chinese speaking speaking LLMs on
@huggingface

I initially shared this misconception too, but after conducting some research, I came up with the list below.

Veryimpressive!

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chat-ui

reacted to dhuynh95's post with ❤️ 11 months ago

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✅New paper to ensure valid LLM output with SOTA LLMs like GPT4 by mixing it with OSS LLMs

Paper: arxiv.org/abs/2401.09967

Great paper showing how strong proprietary AI like #GPT4 can be paired with #OSS LLM to ensure LLM output validity, e.g. valid JSON.

Many devs complain that #LLMs cannot be reliably used in production if the output is not valid, for instance, if one wants to use LLMs to generate SQL queries or JSON, it is crucial that the output is valid.

Frameworks have arisen to constrain the outputs of the LLM to follow some constraints, like outlines (https://github.com/outlines-dev/outlines), but they assume access to logits.

This makes them incompatible with proprietary LLMs like GPT4 that don’t share logits, so one can only use open-source LLMs that are much less performant.

This paper shows how can use powerful proprietary LLMs like GPT4 to create a first unconstrained sketch and refine it using an OSS model like Llama 2 where logits are accessible, to rewrite the sketch following some specific constraints.

They show that GPT4 Precision can be increased by 14% (43% before, 57% after), by boosting it with constrained output on information extraction on Wiki-NRE!