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IAMJB  authored a paper about 1 month ago
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
thomwolf  authored a paper about 2 months ago
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
txya900619  authored a paper about 2 months ago
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech
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prithivMLmods 
posted an update 3 days ago
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Try Liquid AI's all-new multimodal models: LFM2-VL-1.6B & LFM2-VL-450M! Demo with the Gradio UI and ReportLab support and both models are runnable on T4 GPU!

↗ LFM2-VL-1.6B-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-1.6B-LiquidAI/LFM2-VL-1.6B_ReportLab.ipynb

↗ LFM2-VL-450M-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-450M-LiquidAI/LFM2-VL-450M_ReportLab.ipynb

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To know more about it, visit the multimodal outpost notebooks !!
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sergiopaniego 
posted an update 3 days ago
sergiopaniego 
posted an update 4 days ago
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New Zero-Shot Object Detectors in transformers! 🥽

We’ve added LLMDet and MM GroundingDINO, plus a demo Space to compare them with others 🖼️

Play with it: ariG23498/zero-shot-od
sergiopaniego 
posted an update 5 days ago
prithivMLmods 
posted an update 6 days ago
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On the verge of releasing Poseidon-Reasoning-5M, a dataset built to excel in general thought processes, mathematics, and science across a diverse mixture of domains, I’m also dropping the Gargantua-R1-Compact dataset, a collection of over six million high-quality reasoning QA pair traces. 🤗🚀

✦ Gargantua-R1-Compact : prithivMLmods/Gargantua-R1-Compact

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train")

Additionally, I’m adding the mini version of Gargantua — the Gargantua-R1-Wee : prithivMLmods/Gargantua-R1-Wee

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Wee", split="train")

The composition spans 73.93% core mathematical reasoning involving problems, proofs, and computational challenges, 12.11% across diverse scientific domains such as physics, chemistry, biology, and interdisciplinary topics, 11.35% in competitive coding covering algorithms and data structures, 1.37% in academic science focusing on research-level methodology, 0.95% in creative and analytical reasoning through logic puzzles and problem-solving tasks, 0.25% in specialized technical areas like MLOps, LLMs, diffusion models, and CUDA, and 0.06% involving data from graphs and charts converted into structured JSON formats. Designed with both rich contextual depth and formal structural clarity, Gargantua-R1-Compact is an optimal resource for advancing research in symbolic reasoning, interpretability, and high-precision question answering in mathematical domains.

✦ Collection : prithivMLmods/gargantua-r1-mod-6896bfd7834e82b89ad2b38b


To know more about it, visit the dataset card of the respective dataset. !!
prithivMLmods 
posted an update 8 days ago
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I've added the demo of the openbmb/MiniCPM-V-4 model to the Hugging Face Space:
prithivMLmods/Multimodal-VLM-Thinking

✨ MiniCPM-V 4.0 is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B, with a total of 4.1B parameters. It inherits the strong single-image, multi-image, and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency.

✨ With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. This performance surpasses GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B parameters, OpenCompass 65.2), and Qwen2.5-VL-3B-Instruct (3.8B parameters, OpenCompass 64.5). It also shows good performance in multi-image and video understanding.

The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀

To know more about it, visit the model card of the respective model. !!
sergiopaniego 
posted an update 9 days ago
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Latest TRL release brings major upgrades for multimodal alignment!

We dive into 3 new techniques to improve VLM post-training in our new blog:

🌋 GRPO
🎞️ GSPO
🐙 MPO
➕ vLLM integration for online training w/ transformers backend\

🐡 Blog: https://huggingface.co/blog/trl-vlm-alignment
merve 
posted an update 9 days ago
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GPT-4.1-mini level model right in your iPhone 🤯

openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥

allows commercial use as well!
MaziyarPanahi 
authored 2 papers 9 days ago
takarajordan 
posted an update 10 days ago
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What do you all actually think about the open source OpenAI models? Are they legitimately any good or are they hype?
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sergiopaniego 
posted an update 10 days ago
merve 
posted an update 11 days ago
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we're all sleeping on this OCR model rednote-hilab/dots.ocr 🔥

dots.ocr is a new 3B model with sota performance, support for 100 languages & allowing commercial use! 🤯

single e2e model to extract image, convert tables, formula, and more into markdown 📝
try it MohamedRashad/Dots-OCR
prithivMLmods 
posted an update 11 days ago
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Qwen Image – The Latest Image Generation Model🔥

Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source. [ 𝚀𝚠𝚎𝚗-𝙸𝚖𝚊𝚐𝚎 : Qwen/Qwen-Image ]

⤷ Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion

⤷ Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
⤷ Qwen Image [GitHub] : https://github.com/QwenLM/Qwen-Image

Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery.

Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.

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To know more about it, visit the model card of the respective model. !!
sergiopaniego 
posted an update 11 days ago
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Want to learn how to align a Vision Language Model (VLM) for reasoning using GRPO and TRL? 🌋

🧑‍🍳 We've got you covered!!

NEW multimodal post training recipe to align a VLM using TRL in @HuggingFace 's Cookbook.

Go to the recipe 👉https://huggingface.co/learn/cookbook/fine_tuning_vlm_grpo_trl

Powered by the latest TRL v0.20 release, this recipe shows how to teach Qwen2.5-VL-3B-Instruct to reason over images 🌋
merve 
posted an update 11 days ago
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massive releases and tons of Flux 1. Krea LoRas past week!
here's some of the picks, find more models in collection 🫡 merve/releases-august-2-6890c14248203522b7d0267f

LLMs 💬
> Tencent dropped tencent/Hunyuan-7B-Instruct
> Qwen released Qwen/Qwen3-Coder-30B-A3B-Instruct, 30B MoE with 3B params for coding (OS)

vision/multimodal
> RedNote released rednote-hilab/dots.ocr - 3B OCR model (OS)
> Cohere released CohereLabs/command-a-vision-07-2025 - 112B (dense!) VLM for 6 languages
> StepFun-AI shipped stepfun-ai/step3 - 321B MoE VLM (OS)
> Skywork shipped Skywork/Skywork-UniPic-1.5B - new any-to-any model (image+text → image+text) (OS)
a-r-r-o-w 
posted an update 12 days ago
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You would've implemented the 3-loop matrix multiplication many times as a ML practitioner, but the naive implementation is terrible for GPU performance. Modern GPUs achieve peak performance through careful memory access patterns and minimizing scheduling overhead.

In naive matmul (MxK . KxN), the computation happens in tiles - both for the output matrix and for how you read chunks from the input matrices. Each thread-block processes one output tile by loading corresponding tiles from input (for sum-reduction across K dimension), performing the computation, then terminating. The GPU launches many thread-blocks and schedules them across available streaming multiprocessors (SMs). When an SM finishes one tile, it gets assigned a new thread-block for the next uncomputed tile. This way, multiple output tiles are computed in parallel across the SMs, but we pay the cost for launching thread-blocks each time a new tile is computed.

Persistent matmul changes this approach. Instead of launching thread-blocks to compute some output tiles, computing the results on SMs in parallel, and repeating until all output tiles are computed, you launch only as many thread-blocks as you have SMs available (typically 80-132 on modern GPUs). These thread-blocks stay alive until all output tiles are computed, looping through multiple tiles sequentially. Each persistent thread-block may handle multiple output tiles.

The key benefit is the reduced thread-block launch latency. This persistence strategy, combined with other optimizations like coalesced memory loads/stores, block-tiling, warp-tiling, warp-specialization, double-buffering, ping-pong scheduling and other tricks, helps achieve peak performance. More on this in the future!

Code snippet for testing: https://gist.github.com/a-r-r-o-w/28339b442d164084506c0967029968a8

(Bonus: Since I've wanted to learn Manim for a while, this was a great opportunity to make a visualization for Naive VS Persistent matmul. Enjoy ✨)
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sergiopaniego 
posted an update 12 days ago
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Just included example scripts for aligning models using GSPO (including VLM example) 🙆‍♂️🙆‍♂️

GSPO is the latest RL alignment algo by @Alibaba_Qwen and it's already supported in the latest TRL v0.20 release.

Super-easy-to-get-started example scripts below, GO run them!👩‍💻👩‍💻

🧑‍🎨 Script: https://github.com/huggingface/trl/blob/main/examples/scripts/gspo.py
🦄 VLM script: https://github.com/huggingface/trl/blob/main/examples/scripts/gspo_vlm.py
🧩 More TRL examples: https://huggingface.co/docs/trl/main/en/example_overview
🧙‍♂️ GSPO paper: Group Sequence Policy Optimization (2507.18071)
Tonic 
posted an update 13 days ago
prithivMLmods 
posted an update 14 days ago
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Introducing Camel-Doc-OCR-080125(v2), a document content-structure retrieval VLM designed for content extraction and summarization. This is the second model in the Camel Doc OCR VLM series, following Camel-Doc-OCR-062825(v1). The new version fixes formal table reconstruction issues in both en and zh language, achieving optimal performance for long-context inferences.🤗🐪

⤷ Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
⤷ Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
⤷ Demo : prithivMLmods/core-OCR

Multimodal Model Collections and Spaces:

➝ Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
➝ Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
➝ Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
➝ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027

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To know more about it, visit the model card of the respective model. !!
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