stepfun-ai/GOT-OCR2_0
Image-Text-to-Text
•
Updated
•
539k
•
1.36k
John Locke
johnlockejrr
AI & ML interests
NLP, OCR, AI
Recent Activity
liked
a model
about 17 hours ago
stepfun-ai/GOT-OCR2_0
liked
a Space
7 days ago
Riksarkivet/htr_demo
Organizations
None yet
johnlockejrr's activity
liked
a
model
about 17 hours ago
reacted to
victor's
post with 🚀
7 days ago
Post
2903
Finally, an open-source AI that turns your lyrics into full songs is here—meet YuE! Unlike other tools that only create short clips, YuE can make entire songs (up to 5 minutes) with vocals, melody, and instruments all working together. Letsss go!
m-a-p/YuE-s1-7B-anneal-en-cot
m-a-p/YuE-s1-7B-anneal-en-cot
liked
a
Space
7 days ago
PyLaia enhancement
2
#7 opened 17 days ago
by
johnlockejrr
published
a
Space
17 days ago
New activity in
cantillation/Teamim-small_Random_WeightDecay-0.05_Augmented_New-Data_date-02-08-2024
about 1 month ago
A little info
#1 opened about 1 month ago
by
johnlockejrr
reacted to
singhsidhukuldeep's
post with 🚀
about 1 month ago
Post
3672
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:
• Lightweight Local Encoder that converts bytes to patch representations
• Powerful Global Latent Transformer that processes patches
• Local Decoder that converts patches back to bytes
>> Technical Advantages
• Matches performance of Llama 3 at 8B parameters while being more efficient
• Superior handling of non-English languages and rare character sequences
• Remarkable 99.9% accuracy on spelling tasks
• 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:
• Lightweight Local Encoder that converts bytes to patch representations
• Powerful Global Latent Transformer that processes patches
• Local Decoder that converts patches back to bytes
>> Technical Advantages
• Matches performance of Llama 3 at 8B parameters while being more efficient
• Superior handling of non-English languages and rare character sequences
• Remarkable 99.9% accuracy on spelling tasks
• 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
liked
a
Space
about 2 months ago
Model inference
1
#1 opened about 2 months ago
by
johnlockejrr
reacted to
MohamedRashad's
post with ❤️❤️
2 months ago
Post
1663
A while back i shared this model
MohamedRashad/arabic-small-nougat that was a finetune from
facebook/nougat-small for the Arabic Language.
Today this humble project has been scaled with new models, new datasets, new space, and a new paper
Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e
Today this humble project has been scaled with new models, new datasets, new space, and a new paper
Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e
- 1 reply
upvoted
a
paper
2 months ago
Arabic Small Nougat
10
#1 opened 10 months ago
by
johnlockejrr
reacted to
MohamedRashad's
post with 🤗🚀
2 months ago
Post
1663
A while back i shared this model
MohamedRashad/arabic-small-nougat that was a finetune from
facebook/nougat-small for the Arabic Language.
Today this humble project has been scaled with new models, new datasets, new space, and a new paper
Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e
Today this humble project has been scaled with new models, new datasets, new space, and a new paper
Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e
-
- 1 reply
upvoted
an
article
2 months ago
Article
HTRflow - A tool for HTR and OCR
By
•
•
15
reacted to
jsulz's
post with 🔥
3 months ago
Post
2929
When the XetHub crew joined Hugging Face this fall,
@erinys
and I started brainstorming how to share our work to replace Git LFS on the Hub. Uploading and downloading large models and datasets takes precious time. That’s where our chunk-based approach comes in.
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
⏩ Only upload the chunks that changed.
🚀 Download just the updates, not the whole file.
🧠 We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isn’t just a performance boost. It’s a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
⏩ Only upload the chunks that changed.
🚀 Download just the updates, not the whole file.
🧠 We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isn’t just a performance boost. It’s a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks
Finetune the model on other writing systems like Arabic or Hebrew
1
#1 opened 3 months ago
by
johnlockejrr
liked
a
Space
3 months ago
updated
a
Space
3 months ago