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Building interactive demos to scikit-learn examples 🧡

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1024m  authored a paper 9 days ago
Robust and Fine-Grained Detection of AI Generated Texts
1024m  authored a paper 11 days ago
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
1024m  authored a paper 16 days ago
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
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sklearn-docs's activity

ZennyKenny 
posted an update about 17 hours ago
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Phew, maybe a little dark, but I've submitted my second dataset to the Reasoning Datasets Competition: ZennyKenny/tactical-military-reasoning-v.1.0

I'd be interested to hear the community's thoughts on the applications of AI in the military. Especially in the wargaming space.

This is something that feels inevitable (and realistically, probably already in progress). Doesn't it make sense for us to have an understanding of the mechanics of such processes? Surely they will never be open source.
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merve 
posted an update about 18 hours ago
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782
Don't sleep on new AI at Meta Vision-Language release! 🔥

facebook/perception-encoder-67f977c9a65ca5895a7f6ba1
facebook/perception-lm-67f9783f171948c383ee7498

Meta dropped swiss army knives for vision with A2.0 license 👏
> image/video encoders for vision language modelling and spatial understanding (object detection etc) 👏
> The vision LM outperforms InternVL3 and Qwen2.5VL 👏
> They also release gigantic video and image datasets

The authors attempt to come up with single versatile vision encoder to align on diverse set of tasks.

They trained Perception Encoder (PE) Core: a new state-of-the-art family of vision encoders that can be aligned for both vision-language and spatial tasks. For zero-shot image tasks, it outperforms latest sota SigLIP2 👏



> Among fine-tuned ones, first one is PE-Spatial. It's a model to detect bounding boxes, segmentation, depth estimation and it outperforms all other models 😮



> Second one is PLM, Perception Language Model, where they combine PE-Core with Qwen2.5 LM 7B. it outperforms all other models (including InternVL3 which was trained with Qwen2.5LM too!)

The authors release the following checkpoints in sizes base, large and giant:

> 3 PE-Core checkpoints (224, 336, 448)
> 2 PE-Lang checkpoints (L, G)
> One PE-Spatial (G, 448)
> 3 PLM (1B, 3B, 8B)
> Datasets



Authors release following datasets 📑
> PE Video: Gigantic video datasete of 1M videos with 120k expert annotations ⏯️
> PLM-Video and PLM-Image: Human and auto-annotated image and video datasets on region-based tasks
> PLM-VideoBench: New video benchmark on MCQA
  • 1 reply
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merve 
posted an update 3 days ago
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2665
New foundation model on image and video captioning just dropped by NVIDIA AI 🔥

Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮

The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c

Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)

DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀

They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.

Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏
ZennyKenny 
posted an update 9 days ago
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Submitted my first dataset for the Reasoning Datasets Competition! ZennyKenny/TRON-dataset-v.1.0

This dataset is designed to post-train Metareasoning agents, or those agents whose job it is to quickly (and importantly, cheaply) reason through whether it makes sense to launch a full reasoning job or simply use a simple completions job.

There's still plenty of time to join the competition! https://www.bespokelabs.ai/blog/reasoning-datasets-competition

Generation notebook (linked in dataset) is open source and pretty well generalized if I don't say so myself, so you can use it to make your own Metareasoning datasets.

Shoutout to @onekq for his inspiring comment on this topic.
1024m 
authored a paper 9 days ago
1024m 
authored a paper 11 days ago
merve 
posted an update 12 days ago
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sooo many open AI releases past week, let's summarize! 🤗
merve/april-11-releases-67fcd78be33d241c0977b9d2

multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)

LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork

Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)

*OS ones have Apache 2.0 or MIT licenses
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1024m 
authored a paper 16 days ago
ZennyKenny 
posted an update 16 days ago
AtAndDev 
posted an update 19 days ago
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Llama 4 is out...
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ZennyKenny 
posted an update 25 days ago
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A few new Russian-language synthetic datasets. The labelling is good, but some of the syntax and grammar is not great.

Great for Russian-language classification models, probably not great for fine-tuning Russian-langauge text generation.

- Virtual Assistant Query / Responses: ZennyKenny/ru_virtual_assistant_chatgpt_distill
- LLM Query / Responses: ZennyKenny/russian_llm_response_chatgpt_distill

Crazy how much language drift is still an issue, especially given that Russian constitutes nearly 5% of the content on the internet.
ZennyKenny 
posted an update about 1 month ago
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Besides being the coolest named benchmark in the game, HellaSwag is an important measurement of здравый смысль (or common sense) in LLMs.

- More on HellaSwag: https://github.com/rowanz/hellaswag

I spent the afternoon benchmarking YandexGPT Pro 4th Gen, one of the Russian tech giant's premier models.

- Yandex HF Org: yandex
- More on Yandex models: https://yandex.cloud/ru/docs/foundation-models/concepts/yandexgpt/models

The eval notebook is available on GitHub and the resulting dataset is already on the HF Hub!

- Eval Notebook: https://github.com/kghamilton89/ai-explorer/blob/main/yandex-hellaswag/hellaswag-assess.ipynb
- Eval Dataset: ZennyKenny/yandexgptpro_4th_gen-hellaswag

And of course, everyone wants to see the results so have a look at the results in the context of other zero-shot experiments that I was able to find!
  • 2 replies
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louisbrulenaudet 
posted an update about 1 month ago
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I’ve just released logfire-callback on PyPI, designed to facilitate monitoring of Hugging Face Transformer training loops using Pydantic Logfire 🤗

The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️

Install the package using pip:
pip install logfire-callback

First, ensure you have a Logfire API token and set it as an environment variable:
export LOGFIRE_TOKEN=your_logfire_token

Then use the callback in your training code:
from transformers import Trainer, TrainingArguments
from logfire_callback import LogfireCallback

# Initialize your model, dataset, etc.

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    # ... other training arguments
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    callbacks=[LogfireCallback()]  # Add the Logfire callback here
)

trainer.train()

If you have any feedback, please reach out at @louisbrulenaudet
merve 
posted an update about 1 month ago
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4083
So many open releases at Hugging Face past week 🤯 recapping all here ⤵️ merve/march-21-releases-67dbe10e185f199e656140ae

👀 Multimodal
> Mistral AI released a 24B vision LM, both base and instruction FT versions, sota 🔥 (OS)
> with IBM we released SmolDocling, a sota 256M document parser with Apache 2.0 license (OS)
> SpatialLM is a new vision LM that outputs 3D bounding boxes, comes with 0.5B (QwenVL based) and 1B (Llama based) variants
> SkyWork released SkyWork-R1V-38B, new vision reasoning model (OS)

💬 LLMs
> NVIDIA released new Nemotron models in 49B and 8B with their post-training dataset
> LG released EXAONE, new reasoning models in 2.4B, 7.8B and 32B
> Dataset: Glaive AI released a new reasoning dataset of 22M+ examples
> Dataset: NVIDIA released new helpfulness dataset HelpSteer3
> Dataset: OpenManusRL is a new agent dataset based on ReAct framework (OS)
> Open-R1 team released OlympicCoder, new competitive coder model in 7B and 32B
> Dataset: GeneralThought-430K is a new reasoning dataset (OS)

🖼️ Image Generation/Computer Vision
> Roboflow released RF-DETR, new real-time sota object detector (OS) 🔥
> YOLOE is a new real-time zero-shot object detector with text and visual prompts 🥹
> Stability AI released Stable Virtual Camera, a new novel view synthesis model
> Tencent released Hunyuan3D-2mini, new small and fast 3D asset generation model
> ByteDance released InfiniteYou, new realistic photo generation model
> StarVector is a new 8B model that generates svg from images
> FlexWorld is a new model that expands 3D views (OS)

🎤 Audio
> Sesame released CSM-1B new speech generation model (OS)

🤖 Robotics
> NVIDIA released GR00T, new robotics model for generalized reasoning and skills, along with the dataset

*OS ones have Apache 2.0 or MIT license
AtAndDev 
posted an update about 1 month ago
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There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...
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AtAndDev 
posted an update about 1 month ago
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Gemma 3 seems to be really good at human preference. Just waiting for ppl to see it.
not-lain 
posted an update about 1 month ago
Tonic 
posted an update about 2 months ago
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🙋🏻‍♂️Hey there folks,

Did you know that you can use ModernBERT to detect model hallucinations ?

Check out the Demo : Tonic/hallucination-test

See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard

check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1

and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect

👆🏻if you like this topic please contribute code upstream 🚀

  • 2 replies
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ZennyKenny 
posted an update about 2 months ago
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It took me a while, but I've finally got it working: ZennyKenny/note-to-text

Using a Meta LLaMa checkpoint from Unsloth and some help from the HF community, you can capture handwritten notes and convert them into digital format in just a few second.

Really exciting times for AI builders on Hugging Face.
  • 2 replies
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Tonic 
posted an update about 2 months ago
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Powered by KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.

Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!

### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg]( KRLabsOrg )
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.

LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.