WhisperSpeech/WhisperSpeech
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tuanlda78202
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8 days ago
WhisperSpeech/WhisperSpeech
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paper
16 days ago
BitNet a4.8: 4-bit Activations for 1-bit LLMs
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tuanlda78202's activity
liked
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model
8 days ago
Reacted to
AdinaY's
post with 👀
10 days ago
Post
1439
LLaMA Mesh 🔥 Unifying 3D Mesh Generation with Language Models
Model: Zhengyi/LLaMA-Mesh
Demo: Zhengyi/LLaMA-Mesh
Paper: LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models (2411.09595)
✨ Unified 3D generation & text understanding.
✨ 3D meshes as plain text for seamless LLM integration.
✨ High-quality 3D outputs rivaling specialized models.
Model: Zhengyi/LLaMA-Mesh
Demo: Zhengyi/LLaMA-Mesh
Paper: LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models (2411.09595)
✨ Unified 3D generation & text understanding.
✨ 3D meshes as plain text for seamless LLM integration.
✨ High-quality 3D outputs rivaling specialized models.
upvoted
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paper
16 days ago
Reacted to
clem's
post with 🚀
about 2 months ago
Post
3702
Very few people realize that most of the successful AI startups got successful because they were focused on open science and open-source for at least their first few years. To name but a few, OpenAI (GPT, GPT2 was open-source), Runway & Stability (stable diffusion), Cohere, Mistral and of course Hugging Face!
The reasons are not just altruistic, it's also because sharing your science and your models pushes you to build AI faster (which is key in a fast-moving domain like AI), attracts the best scientists & engineers and generates much more visibility, usage and community contributions than if you were 100% closed-source. The same applies to big tech companies as we're seeing with Meta and Google!
More startups and companies should release research & open-source AI, it's not just good for the world but also increases their probability of success!
The reasons are not just altruistic, it's also because sharing your science and your models pushes you to build AI faster (which is key in a fast-moving domain like AI), attracts the best scientists & engineers and generates much more visibility, usage and community contributions than if you were 100% closed-source. The same applies to big tech companies as we're seeing with Meta and Google!
More startups and companies should release research & open-source AI, it's not just good for the world but also increases their probability of success!
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Reacted to
fdaudens's
post with 👍
about 2 months ago
Post
978
🚀 OpenAI's new Whisper "turbo": 8x faster, 40% VRAM efficient, minimal accuracy loss.
🔒 Run it locally in-browser for private transcriptions! Transcribe interviews, audio & video.
⚡️ 40 tokens/sec on my MacBook
🔗 Try it: webml-community/whisper-large-v3-turbo-webgpu
Model: https://huggingface.co/ylacombe/whisper-large-v3-turbo
🔒 Run it locally in-browser for private transcriptions! Transcribe interviews, audio & video.
⚡️ 40 tokens/sec on my MacBook
🔗 Try it: webml-community/whisper-large-v3-turbo-webgpu
Model: https://huggingface.co/ylacombe/whisper-large-v3-turbo
upvoted
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about 2 months ago
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about 2 months ago
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2 months ago
Article
PaliGemma – Google's Cutting-Edge Open Vision Language Model
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212
Article
Vision Language Models Explained
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216
Article
Illustrated LLM OS: An Implementational Perspective
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Reacted to
elinas's
post with 👀
3 months ago
Post
2022
We conducted an experiment in an effort to revive LLaMA 1 33B as it had unique prose and a lack of "GPT-isms" and "slop" in its pretraining data, as well as being one of the favorites at the time. With multiple finetune runs, we were able to extend the model from it's pretrained base of 2048 to ~12,000 tokens adding approx. 500M tokens in the process. The effective length is 16,384 but it's better to keep it on the lower range. It writes well and in multiple formats. In the future, we have some ideas like implementing GQA. Please take a look and we would love to hear your feedback!
ZeusLabs/Chronos-Divergence-33B
ZeusLabs/Chronos-Divergence-33B
Reacted to
davidberenstein1957's
post with 🔥
3 months ago
Post
1501
Interested in learning about everything Image?
With the rise of recent interest in Vision Language Models (VLMs), we decided to make a push to include an ImageField within Argilla! This means any open source developer can now work on better models for vision ML tasks too and we would like to show you how.
We would love to introduce this new feature to you, so we've prepared a set of notebooks to go over some common image scenarios.
finetune an CLIP retrieval model with sentence transformers
use ColPali+ Qwen VL for RAG and log the results to Argilla
image-generation preference: creating multi-modal preference datasets for free using Hugging Face inference endpoints.
See you on Thursday!
https://lu.ma/x7id1jqu
With the rise of recent interest in Vision Language Models (VLMs), we decided to make a push to include an ImageField within Argilla! This means any open source developer can now work on better models for vision ML tasks too and we would like to show you how.
We would love to introduce this new feature to you, so we've prepared a set of notebooks to go over some common image scenarios.
finetune an CLIP retrieval model with sentence transformers
use ColPali+ Qwen VL for RAG and log the results to Argilla
image-generation preference: creating multi-modal preference datasets for free using Hugging Face inference endpoints.
See you on Thursday!
https://lu.ma/x7id1jqu
upvoted
a
paper
3 months ago
liked
a
model
3 months ago
Reacted to
AlexBodner's
post with 👀
3 months ago
Post
3783
💾🧠How much VRAM will you need for training your AI model? 💾🧠
Check out this app where you convert:
Pytorch/tensorflow summary -> needed VRAM
or
Parameter count -> needed VRAM
Use it in: http://howmuchvram.com
And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!
Check out this app where you convert:
Pytorch/tensorflow summary -> needed VRAM
or
Parameter count -> needed VRAM
Use it in: http://howmuchvram.com
And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!
- 3 replies
liked
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models
3 months ago
upvoted
an
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3 months ago
Article
Welcome Gemma 2 - Google's new open LLM
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124
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models
3 months ago
