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SivilTaram  authored a paper 9 days ago
Diffusion Language Models are Super Data Learners
ljvmiranda921  authored a paper 16 days ago
FilBench: Can LLMs Understand and Generate Filipino?
ZennyKenny  authored a paper about 1 month ago
BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
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prithivMLmods 
posted an update about 9 hours ago
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Made a small write up and experimental finetuning guide for MetaCLIP2 for Image Classification on Downstream Tasks. The blog titled Fine Tuning MetaCLIP 2 for Image Classification on Downstream Tasks demonstrates the step by step finetuning using CIFAR10 and is also flexible for adapting to other datasets. For more details, check out the linked blog below. 🤗↗️

⮞ Blog Article: https://huggingface.co/blog/prithivMLmods/metaclip2-downstream-finetune
⮞ Demo Space[Zero-Shot Classification]: prithivMLmods/metaclip-2-demo

Some other models
╰› MetaCLIP-2-Cifar10: prithivMLmods/MetaCLIP-2-Cifar10
╰› MetaCLIP-2-Age-Range-Estimator: prithivMLmods/MetaCLIP-2-Age-Range-Estimator
╰› MetaCLIP-2-Gender-Identifier: prithivMLmods/MetaCLIP-2-Gender-Identifier
╰› MetaCLIP-2-Open-Scene: prithivMLmods/MetaCLIP-2-Open-Scene

⮞ Collection: https://huggingface.co/collections/prithivMLmods/metaclip2-image-classification-experiments

To know more about it, visit the app page or the respective model page!
prithivMLmods 
posted an update 4 days ago
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Try the all-new trending Qwen-Image-Edit specialized adapter demos, including Photo-to-Anime, Light Restoration, Multi-Angle Edits, Relighting, and more — all in a single Hugging Face Space. Below is the demo link. 🤗🌠

⮞ Demo-Space: prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast
⮞ How-to-Use: prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast#2
⮞ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

To know more about it, visit the app page or the respective model page!
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flozi00 
posted an update 4 days ago
Locutusque 
posted an update 6 days ago
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🚀 AutoXLA - Accelerating Large Models on TPU
AutoXLA is an experimental library that automates the distribution, optimization, and quantization of large language models for TPUs using PyTorch/XLA. It extends the Hugging Face Transformers interface with TPU-aware features such as automatic sharding, custom attention kernels, and quantization-aware loading, making large-scale deployment and training both simpler and faster.
With quantization and Splash Attention kernels, AutoXLA achieves up to 4× speedups over standard Flash Attention implementations, significantly improving throughput for both inference and training workloads.
Whether you’re experimenting with distributed setups (FSDP, 2D, or 3D sharding) or optimizing memory via LanguageModelQuantizer, AutoXLA is built to make scaling LLMs on TPU seamless.
⚠️ Note: This is an experimental repository. Expect rough edges! Please report bugs or unexpected behavior through GitHub issues.
🔗 GitHub Repository: https://github.com/Locutusque/AutoXLA
ZennyKenny 
posted an update 7 days ago
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🎉 Novoyaz is live.

A few months ago, I built a quick POC in Hugging Face that used a fine-tuned variant of OpenAI's OSS-20B model that I trained to convert the text from pre-reform Russian-language documents into modern Russian orthography.

⚡️ This morning, I launched novoyaz.io.

This is a production app, the frontend for which I built in like two hours with Lovable, that uses that same fine-tuned model for transliteration, but now has a bunch of extra features that make using it even easier (like taking and uploading pictures with your on-device camera for example 😅).

👉 If you're a researcher, or know a researcher, for whom this app will improve their day-to-day workflows, please get in touch with me.
prithivMLmods 
posted an update 7 days ago
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Introducing Photo-Mate-v2, based on FLUX.1-Kontext-dev, for advanced image manipulation tasks. It supports transforming scenes into top-down/bottom-up perspectives, CAM-right/left-view and its reverse, as well as general kontext-specified object removal. Below is the list of demos and adapters.🔥🤗

➤ Spaces [Demo] : prithivMLmods/Kontext-Photo-Mate-v2

Kontext-Adapters :
✦ Kontext-Bottom-Up-View: prithivMLmods/Kontext-Bottom-Up-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Top-Down-View: prithivMLmods/Kontext-Top-Down-View
✦ Kontext-CAM-Left-View: prithivMLmods/Kontext-CAM-Left-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Unblur-Upscale: prithivMLmods/Kontext-Unblur-Upscale
✦ Kontext-0811-exp: prithivMLmods/Kontext-0811-exp

Photo-Mate Collection:
✦ Kontext CAM Angles: https://huggingface.co/collections/prithivMLmods/kontext-cam-angles
✦ i2i - Kontext (exp): https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp
✦ LZO-1 (Lossless Zoom Operator): https://huggingface.co/collections/prithivMLmods/lzo-1-lossless-zoom-operator

Related-Apps:
✦ Photo-Mate [Version 1.0]: prithivMLmods/Photo-Mate-i2i
✦ Image Generation Apps [Collection]: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

To know more about it, visit the app page or the respective model page!
@prithivMLmods
SivilTaram 
authored a paper 9 days ago
flozi00 
posted an update 10 days ago
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I just got asked about the differences between Blackwell systems and Grace Blackwell systems. What's the difference and how much of a performance gap is there between them?

https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison

Here's a summary of the key points from the article:

GB200 (Grace Blackwell) is a Superchip: It integrates a Grace CPU and two Blackwell GPUs into a single package.
B200 is a GPU-only module: It's designed to be paired with x86 or ARM CPUs in more traditional server setups.


Performance and Efficiency:

Based on MLPerf Training v5.0 benchmarks, the article concludes:

GB200 systems are approximately 42% more efficient than B200 systems on average. This is especially true in large-scale deployments (100+ GPUs), where the GB200's integrated design and high-speed NVLink interconnect provide a significant advantage.

In smaller, single-node systems (e.g., 8 GPUs), the performance difference is much smaller, around 10-15%.


Use Cases:

Choose GB200 for large-scale AI clusters, training massive models, and when maximum efficiency is the top priority.

Choose B200 for smaller deployments, when you need the flexibility to choose your own CPU, or for mixed AI and HPC workloads.
prithivMLmods 
posted an update 12 days ago
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A week ago, I shared a post about the latest transformers test implementation of DeepSeek-OCR Compatibility (https://tinyurl.com/ykc4mm66). Now, I’m dropping the most compatible version of it to support the model with the latest transformers. 🤗🔥

➠ DeepSeek-OCR-Latest-BF16.I64: prithivMLmods/DeepSeek-OCR-Latest-BF16.I64
➠ DeepSeek OCR [exp] : prithivMLmods/DeepSeek-OCR-experimental

✅Supports the latest transformers v4.57.1
✅torch: 2.6.0+cu124 (or) the latest version (i.e., torch 2.9.0)
✅cuda version: 12.4
✅users can also opt out of specific attention implementations if desired.

✨Previous version: strangervisionhf/deepseek-ocr-latest-transformers
↗️Related Blog: https://huggingface.co/blog/prithivMLmods/multimodal-ocr-vlms
✨Community Page: strangervisionhf
✨Original Model Page: deepseek-ai/DeepSeek-OCR

To know more about it, visit the app page or the respective model page!
flozi00 
posted an update 13 days ago
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Some weeks ago, i've just decide its time to leave LinkedIn for me.
It got silent around my open source activities the last year, so i thought something has to change.

That's why my focus will move to share experiences and insights about hardware, drivers, kernels and linux. I won't post about how to use models, built agents or do prompting. I want to share about some deeper layers the actual hypes are built on.

I will start posting summarizations of my articles here on the hub.

English version:
https://flozi.net/en

German translated version:
https://flozi.net/de

Feel free to reach me if you want to read something specific.
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ZennyKenny 
posted an update 15 days ago
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Anyone got the scoop on a good OCR model that's available on inference?

Keen to make use of an endpoint (gated or not -- happy to pay for usage) for a personal project, but not so keen to pay for the GPU hosting myself.

🙈🙈🙈
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ljvmiranda921 
authored a paper 16 days ago
prithivMLmods 
posted an update 16 days ago
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A small blog post titled - Hall of Multimodal OCR VLMs and Demonstrations has been published on ↗️ https://huggingface.co/blog/prithivMLmods/multimodal-ocr-vlms on behalf of strangervisionhf

It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining transformers compatibility.
prithivMLmods 
posted an update 18 days ago
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Implemented DeepSeek-OCR to support the latest transformers on the strangervisionhf page. The page includes the model weights and corrected configuration, which fix the issues and allow transformers inference to run smoothly.🤗🔥

> Model: strangervisionhf/deepseek-ocr-latest-transformers
> Demo Space: prithivMLmods/DeepSeek-OCR-experimental

✅Supports the latest transformers
✅You can also opt out of the attention implementation if needed.
✅Supports torch version 2.6.0 or higher
✅torch version cuda: 12.4

If you are interested in experimenting with new things and streamlining compatibility, the strangervisionhf organization is open for you, and you can join the community.

> Multimodal Collection: prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0, https://huggingface.co/collections/strangervisionhf/october-2025-models

> Thank you, @merve , for assigning the blazing-fast Zero GPU support!

> Notebook : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/DeepSeek-OCR-Demo/deepseek_ocr_demo.ipynb

To know more about it, visit the app page or the respective model page!
prithivMLmods 
posted an update 19 days ago
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Introducing Gliese-OCR-7B-Post2.0-final, a document content-structure retrieval VLM designed for content extraction (OCR), summarization, and document visual question answering. This is the fourth and final model in the Camel Doc OCR VLM series, following Gliese-OCR-7B-Post1.0. The model delivers superior accuracy across a wide range of document types, including scanned PDFs, handwritten pages, structured forms, and analytical reports.🚀🤗

> Gliese-OCR-7B-Post2.0-final : prithivMLmods/Gliese-OCR-7B-Post2.0-final
> Gliese-OCR-7B-Post1.0 (previous) : prithivMLmods/Gliese-OCR-7B-Post1.0
> Gliese OCR Post-x.0 (collection) : https://huggingface.co/collections/prithivMLmods/gliese-ocr-post-x0
> Multimodal Implementations (collection) : https://huggingface.co/collections/prithivMLmods/multimodal-implementations
> Qwen VL Captions (other-collection) : https://huggingface.co/collections/prithivMLmods/qwen-vl-captions
> Run Demo Here : prithivMLmods/Gliese-OCR-7B-Post2.0-final
> GitHub (4bit) : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Gliese-OCR-7B-Post2.0-final(4bit)/Gliese_OCR_7B_Post2_0_final.ipynb

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> To know more about it, visit the app page or the respective model page!!
prithivMLmods 
posted an update 20 days ago
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Here is the official Florence-2 Transformers-converted demo for the following vision models: florence-community/Florence-2-large, florence-community/Florence-2-large-ft, florence-community/Florence-2-base, and florence-community/Florence-2-base-ft. These models support tasks such as object detection, captioning, detailed captioning, more detailed captioning, dense region captioning, region proposal, OCR, and OCR with region. Try the official demo at the link below:

> Space: prithivMLmods/florence2-vision-models
> Collection: prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

> To know more about it, visit the app page or the respective model page!!
ZennyKenny 
posted an update 24 days ago
prithivMLmods 
posted an update 26 days ago
prithivMLmods 
posted an update about 1 month ago
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Now you can try all the latest state-of-the-art multimodal vision-language models from the Qwen3-VL series demo on Hugging Face Spaces — including 4B, 8B, and 30B (Instruct, 4B-Thinking) variants. I’ve also uploaded the weights for the Abliterated variants of these models, up to 30B parameters. Check out the Spaces and model links below! 🤗🔥

✨ Qwen3-VL[4B,8B]: prithivMLmods/Qwen3-VL-Outpost
✨ Qwen3-VL-30B-A3B-Demo: prithivMLmods/Qwen3-VL-HF-Demo
✨ Collection: prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

Qwen3-VL Abliterated Model Collection [ Version 1.0 ]

✨ Qwen3-VL-8B-Instruct-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-abliterated
✨ Qwen3-VL-4B-Instruct-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-4B-Instruct-abliterated
✨ Qwen3-VL-8B-Thinking-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Thinking-abliterated
✨ Qwen3-VL-4B-Thinking-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-4B-Thinking-abliterated
✨ Qwen3-VL-30B-A3B-Instruct-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-30B-A3B-Instruct-abliterated
✨ Qwen3-VL-30B-A3B-Thinking-abliterated: https://huggingface.co/prithivMLmods/Qwen3-VL-30B-A3B-Thinking-abliterated

⚡Collection: https://huggingface.co/collections/prithivMLmods/qwen3-vl-abliteration-oct-1625-68f0e3e567ef076594605fac

Note: This is version 1.0 of the Abliteration of the Qwen3-VL series of models. It may perform sub-optimally in some cases. If you encounter any issues, please open a discussion.
ZennyKenny 
posted an update about 1 month ago
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Did Hugging Face just ban hammer a bunch of bot accounts or am I just so uninteresting that 30% of my subs dropped me overnight?

😬 Wait, don't answer that.
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