DAMO-NLP-SG/multimodal_textbook
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Mex Ivanov
MexIvanov
AI & ML interests
NLP, Coding, Quantum Computing and more.
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DAMO-NLP-SG/multimodal_textbook
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hexgrad/Kokoro-82M
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reacted to
m-ric's
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23 days ago
Post
5075
Since I published it on GitHub a few days ago,
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
- 3 replies
reacted to
singhsidhukuldeep's
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about 1 month ago
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2191
Exciting News in AI: JinaAI Releases JINA-CLIP-v2!
The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:
๐ Technical Highlights:
- Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder
- Supports 89 languages with 8,192 token context length
- Processes images up to 512ร512 pixels with 14ร14 patch size
- Implements FlashAttention2 for text and xFormers for vision processing
- Uses Matryoshka Representation Learning for efficient vector storage
โก๏ธ Under The Hood:
- Multi-stage training process with progressive resolution scaling (224โ384โ512)
- Contrastive learning using InfoNCE loss in both directions
- Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs
- Incorporates specialized datasets for document understanding, scientific graphs, and infographics
- Uses hard negative mining with 7 negatives per positive sample
๐ Performance:
- Outperforms previous models on visual document retrieval (52.65% nDCG@5)
- Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark
- Strong multilingual performance across 30 languages
- Maintains performance even with 75% dimension reduction (256D vs 1024D)
๐ฏ Key Innovation:
The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!
Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:
๐ Technical Highlights:
- Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder
- Supports 89 languages with 8,192 token context length
- Processes images up to 512ร512 pixels with 14ร14 patch size
- Implements FlashAttention2 for text and xFormers for vision processing
- Uses Matryoshka Representation Learning for efficient vector storage
โก๏ธ Under The Hood:
- Multi-stage training process with progressive resolution scaling (224โ384โ512)
- Contrastive learning using InfoNCE loss in both directions
- Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs
- Incorporates specialized datasets for document understanding, scientific graphs, and infographics
- Uses hard negative mining with 7 negatives per positive sample
๐ Performance:
- Outperforms previous models on visual document retrieval (52.65% nDCG@5)
- Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark
- Strong multilingual performance across 30 languages
- Maintains performance even with 75% dimension reduction (256D vs 1024D)
๐ฏ Key Innovation:
The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!
Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
reacted to
singhsidhukuldeep's
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about 1 month ago
Post
3666
Exciting breakthrough in AI:
@Meta
's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization!
The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:
>> Key Innovations
Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.
Three-Component Architecture:
โข Lightweight Local Encoder that converts bytes to patch representations
โข Powerful Global Latent Transformer that processes patches
โข Local Decoder that converts patches back to bytes
>> Technical Advantages
โข Matches performance of Llama 3 at 8B parameters while being more efficient
โข Superior handling of non-English languages and rare character sequences
โข Remarkable 99.9% accuracy on spelling tasks
โข Better scaling properties than token-based models
>> Under the Hood
The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.
This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:
>> Key Innovations
Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.
Three-Component Architecture:
โข Lightweight Local Encoder that converts bytes to patch representations
โข Powerful Global Latent Transformer that processes patches
โข Local Decoder that converts patches back to bytes
>> Technical Advantages
โข Matches performance of Llama 3 at 8B parameters while being more efficient
โข Superior handling of non-English languages and rare character sequences
โข Remarkable 99.9% accuracy on spelling tasks
โข Better scaling properties than token-based models
>> Under the Hood
The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.
This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
- 3 replies
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reach-vb's
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4624
VLMs are going through quite an open revolution AND on-device friendly sizes:
1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d
4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co/jiuhai
5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/
What a time to be alive! ๐ฅ
1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d
4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co/jiuhai
5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/
What a time to be alive! ๐ฅ
reacted to
burtenshaw's
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about 2 months ago
Post
2708
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. Itโs not big like Li Yin and
@mlabonne
, itโs just smol.
๐ท It focuses on practical use cases, so if youโre working on something, bring it along.
๐ฏโโ๏ธ Itโs peer reviewed and open so you can discuss and get feedback.
๐ค If youโre already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and itโs on instruction tuning!
https://github.com/huggingface/smol-course
๐ท It focuses on practical use cases, so if youโre working on something, bring it along.
๐ฏโโ๏ธ Itโs peer reviewed and open so you can discuss and get feedback.
๐ค If youโre already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and itโs on instruction tuning!
https://github.com/huggingface/smol-course
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julien-c's
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2 months ago
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wow ๐ฎ
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
PrimeIntellect/INTELLECT-1-Instruct
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
PrimeIntellect/INTELLECT-1-Instruct
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2 months ago
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davidberenstein1957's
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2 months ago
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1710
Letโs make a generation of amazing image-generation models
The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Letโs change that!
The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.
Blog: https://huggingface.co/blog/burtenshaw/image-preferences
The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Letโs change that!
The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.
Blog: https://huggingface.co/blog/burtenshaw/image-preferences
updated
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2 months ago
liked
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maxiw's
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3 months ago
Post
4646
I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
โค๏ธ: 7048 times
๐ฅ: 5921 times
๐: 4856 times
๐: 2549 times
๐ค: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
โค๏ธ: 7048 times
๐ฅ: 5921 times
๐: 4856 times
๐: 2549 times
๐ค: 2065 times
- 10 replies
updated
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dataset
3 months ago
updated
a
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