Lightricks/LTX-Video
Image-to-Video
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Sizor Cloud
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reacted to
as-cle-bert's
post with 🤯
5 months ago
Post
5059
Hi HF Community!🤗
In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon microsoft/Phi-3-mini-4k-instruct, llama.cpp🦙 and Streamlit.
Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)
If you want to know more, head over to the GitHub repository (https://github.com/AstraBert/SearchPhi) and, to test it out, use this HF space: as-cle-bert/SearchPhi
Have fun!🐱
In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon microsoft/Phi-3-mini-4k-instruct, llama.cpp🦙 and Streamlit.
Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)
If you want to know more, head over to the GitHub repository (https://github.com/AstraBert/SearchPhi) and, to test it out, use this HF space: as-cle-bert/SearchPhi
Have fun!🐱
reacted to
merve's
post with 🔥
5 months ago
Post
2282
We have recently merged Video-LLaVA to transformers! 🤗🎞️
What makes this model different?
Demo: llava-hf/video-llava
Model: LanguageBind/Video-LLaVA-7B-hf
Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.
It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.
I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models
It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not 🤩
What makes this model different?
Demo: llava-hf/video-llava
Model: LanguageBind/Video-LLaVA-7B-hf
Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.
It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.
I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models
It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not 🤩
Amazing!
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#2 opened 5 months ago
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SizorCloud
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Very good model
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#4 opened 5 months ago
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SizorCloud
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5 months ago
Differences between Tiger Gemma, Smegmma and Broken Gemma
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#1 opened 6 months ago
by
isr431
reacted to
mitkox's
post with 🔥
6 months ago
Post
2365
Me: I want on device AI: fast, without latency, with real privacy, convenient for use and development.
Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.
Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.
Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.
Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.
Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
- 4 replies
reacted to
victor's
post with 🔥
6 months ago
Post
4002
Together MoA is a really interesting approach based on open source models!
"We introduce Mixture of Agents (MoA), an approach to harness the collective strengths of multiple LLMs to improve state-of-the-art quality. And we provide a reference implementation, Together MoA, which leverages several open-source LLM agents to achieve a score of 65.1% on AlpacaEval 2.0, surpassing prior leader GPT-4o (57.5%)."
Read more here: https://www.together.ai/blog/together-moa
PS: they provide some demo code: (https://github.com/togethercomputer/MoA/blob/main/bot.py) - if someone release a Space for it it could go 🚀
"We introduce Mixture of Agents (MoA), an approach to harness the collective strengths of multiple LLMs to improve state-of-the-art quality. And we provide a reference implementation, Together MoA, which leverages several open-source LLM agents to achieve a score of 65.1% on AlpacaEval 2.0, surpassing prior leader GPT-4o (57.5%)."
Read more here: https://www.together.ai/blog/together-moa
PS: they provide some demo code: (https://github.com/togethercomputer/MoA/blob/main/bot.py) - if someone release a Space for it it could go 🚀
- 1 reply
