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Building interactive demos to scikit-learn examples 🧡

Recent Activity

1024m  authored a paper 12 days ago
Robust and Fine-Grained Detection of AI Generated Texts
1024m  authored a paper 14 days ago
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
1024m  authored a paper 19 days ago
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
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prithivMLmods 
posted an update 4 days ago
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2020
Bringing out style-intermixing adapters for Flux.Dev, including Aura Glow, Fallen Ink Art, Cardboard Paper Arts, Black & White Expressions, and Glitter Gem Touch. For more details, visit the model card of the LoRA. 🥳

╰┈➤Demo : prithivMLmods/FLUX-LoRA-DLC2 & prithivMLmods/FLUX-LoRA-DLC

╰┈➤ Adapters :
+ Aura Glow : strangerzonehf/2DAura-Flux
+ Fallen Ink Art : strangerzonehf/FallenArt-Flux
+ Black & White Expressions : strangerzonehf/BnW-Expressions-Flux
+ Glitter Gem Touch : strangerzonehf/Gem-Touch-LoRA-Flux
+ Cardboard Paper Arts v1 : strangerzonehf/Flux-Cardboard-Art-LoRA
+ Cardboard Paper Arts v2 : strangerzonehf/Cardboard-v2-Flux

╰┈➤ Pages :
- Repository Page : strangerzonehf
- Collection : strangerzonehf/mixer-adp-042025-68095c365d9d1072c8d860be
- Flux Ultimate LoRA Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection
- By prithivMLmods : @prithivMLmods

The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.
ZennyKenny 
posted an update 4 days ago
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518
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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prithivMLmods 
posted an update 5 days ago
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1139
Dropping the domain-specific downstream image classification content moderation models, including the anime image type classification, GeoSceneNet, indoor-outdoor scene classification, and black-and-white vs. colored image classification models, along with the datasets. 🔥

╰┈➤Models :
+ GeoSceneNet : prithivMLmods/Multilabel-GeoSceneNet
+ IndoorOutdoorNet : prithivMLmods/IndoorOutdoorNet
+ B&W vs Colored : prithivMLmods/BnW-vs-Colored-Detection
+ Anime Image Type : prithivMLmods/Anime-Classification-v1.0
+ Multilabel Portrait : prithivMLmods/Multilabel-Portrait-SigLIP2

╰┈➤Datasets :
- GeoSceneNet : prithivMLmods/Multilabel-GeoSceneNet-16K
- IndoorOutdoorNet : prithivMLmods/IndoorOutdoorNet-20K
- BnW vs Colored : prithivMLmods/BnW-vs-Colored-10K
- Multilabel Portrait : prithivMLmods/Multilabel-Portrait-18K

╰┈➤Collections :
> Multilabel Image Classification Datasets : prithivMLmods/multilabel-image-classification-datasets-6809aa64637f45d4c47fa6ca
> Model Collection : prithivMLmods/siglip2-content-filters-models-v2-68053a958c42ef17a3a3f4d1

Note: The anime scene type dataset is not mentioned in the list because it is private and only accessible to members of the DeepGHS organization.

For raw ZIP files or more information about the datasets, visit: https://www.kaggle.com/prithivsakthiur/datasets
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prithivMLmods 
posted an update 11 days ago
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2806
Dropping an entire collection of Style Intermixing Adapters on StrangerZone HF — including Realism, Anime, Sketch, Texture-Rich 3D Experimentals, Automotive Concept Images, and LoRA models based on Flux.1, SD 3.5 Turbo/Large, Stable Diffusion XL 🎨

╰┈➤Collection :
➜ sketch : strangerzonehf/sketch-fav-675ba869c7ceaec7e652ee1c
➜ sketch2 : strangerzonehf/q-series-sketch-678e3503bf3a661758429717
➜ automotive : strangerzonehf/automotive-3d-675bb31a491d8c264d45d843
➜ texture 3d : strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
➜ super 3d : strangerzonehf/super-3d-engine-6743231d69f496df97addd2b
➜ style mix : strangerzonehf/mixer-engine-673582c9c5939d8aa5bf9533
➜ realism : strangerzonehf/realism-engine-67343495b6daf0fbdb904cc1

╰┈➤The Entire Collection :
➜ flux.1 : prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
➜ flux-ultimate-lora-collection : strangerzonehf/Flux-Ultimate-LoRA-Collection
➜ sd 3.5 large / turbo : prithivMLmods/sd-35-large-lora-671b39d7bc2e7f71a446b163
➜ sdxl : prithivMLmods/sdxl-dev-models-667803a6d5ac75b59110e527

╰┈➤Pages :
➜ page 1: strangerzonehf
➜ page 2: @prithivMLmods
➜ demo : prithivMLmods/FLUX-LoRA-DLC

.🤗
ZennyKenny 
posted an update 12 days ago
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Submitted my first dataset for the Reasoning Datasets Competition! https://huggingface.co./datasets/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 12 days ago
prithivMLmods 
posted an update 13 days ago
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Try out the demo for Multimodal OCR featuring the implementation of models including RolmOCR and Qwen2VL OCR. The use case showcases image-text-to-text conversion and video understanding support for the RolmOCR model ! 🚀

🤗Multimodal OCR Space : prithivMLmods/Multimodal-OCR

📦The models implemented in this Space are:
+ Qwen2VL OCR : prithivMLmods/Qwen2-VL-OCR-2B-Instruct [ or ]
+ Qwen2VL OCR2 : prithivMLmods/Qwen2-VL-OCR2-2B-Instruct
+ RolmOCR : reducto/RolmOCR

Qwen2VL OCR supports only image-text-to-text in the space.
1024m 
authored a paper 14 days ago
1024m 
authored a paper 19 days ago
ZennyKenny 
posted an update 19 days ago
prithivMLmods 
posted an update 22 days ago
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3314
Loaded some domain-specific downstream image classification content moderation models, which is essentially the practice of monitoring and filtering user-generated content on platforms, based on SigLIP-2 Base Patch16 with newly initialized trainable parameters. 🥠

+ Age-Classification-SigLIP2 : prithivMLmods/Age-Classification-SigLIP2
[ Age range classification from 0 to 65+ years ]
+ Facial-Emotion-Detection-SigLIP2 : prithivMLmods/Facial-Emotion-Detection-SigLIP2
[ Designed to classify different facial emotions ]
+ Hand-Gesture-2-Robot : prithivMLmods/Hand-Gesture-2-Robot
[ Human Hand Gesture Classification for Robot Control ]
+ Mature-Content-Detection : prithivMLmods/Mature-Content-Detection
[ Mature [adult] or neutral content categories ]
+ Vit-Mature-Content-Detection : prithivMLmods/Vit-Mature-Content-Detection
[ Mature [adult] or neutral content categories ft. ViT]
+ Human-Action-Recognition : prithivMLmods/Human-Action-Recognition
[ Human actions including clapping, sitting, running, and more ]
+ Mirage-Photo-Classifier : prithivMLmods/Mirage-Photo-Classifier
[ Whether an image is real or AI-generated (fake) ]
+ Food-101-93M : prithivMLmods/Food-101-93M
[ Classify food images into one of 101 popular dishes ]
+ Hand-Gesture-19 : prithivMLmods/Hand-Gesture-19
[ Classify hand gesture images into different categories ]
+ Trash-Net : prithivMLmods/Trash-Net
[ Classification of trash into six distinct categories ]
+ Gender-Classifier-Mini : prithivMLmods/Gender-Classifier-Mini
[ Classify images based on gender [Male / Female] ]

🎡Collections :

+ SigLIP2 Content Filters : https://huggingface.co./collections/prithivMLmods/siglip2-content-filters-models-67f001055ec2bed56ca41f6d
AtAndDev 
posted an update 23 days ago
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Llama 4 is out...
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prithivMLmods 
posted an update 23 days ago
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ChatGPT-4o’s image generation goes wild for a week—featuring everything from Studio Ghibli-style art and image colorization to style intermixing. Here are some examples showcasing the generation of highly detailed images from freestyle design templates. Want to know more? Check out the blog 🚀

🔗Blog : https://huggingface.co./blog/prithivMLmods/chatgpt-4o-image-gen
ZennyKenny 
posted an update 28 days ago
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2129
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.
prithivMLmods 
posted an update 30 days ago
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1893
Luna, the single-speaker text-to-speech model, features a Radio & Atcosim-style sound with a female voice. It offers authentic radio podcast noise and empathetic speech generation, fine-tuned based on Orpheus's Llama-based speech generation state-of-the-art model. 🎙️

+ Model : prithivMLmods/Llama-3B-Mono-Luna
+ Collection : prithivMLmods/clean-radio-mono-voice-67e76fe1b3a87cc3bccef803
+ Reference ft : https://github.com/canopyai/Orpheus-TTS
+ Base Model : canopylabs/orpheus-3b-0.1-ft

I also tried some other clean-voice single-speaker models based on Orpheus. If you're interested, check out the collection.

🔉Try the Mono Luna demo here: http://colab.research.google.com/drive/1K0AAIOKDE5XE0znxXaiiUJvPSpFveteK
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ZennyKenny 
posted an update about 1 month ago
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1937
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!
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prithivMLmods 
posted an update about 1 month ago
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Dropping some new Journey Art and Realism adapters for Flux.1-Dev, including Thematic Arts, 2021 Memory Adapters, Thread of Art, Black of Art, and more. For more details, visit the model card on Stranger Zone HF 🤗

+ Black-of-Art-Flux : strangerzonehf/Black-of-Art-Flux
+ Thread-of-Art-Flux : strangerzonehf/Thread-of-Art-Flux
+ 2021-Art-Flux : strangerzonehf/2021-Art-Flux
+ 3d-Station-Toon : strangerzonehf/3d-Station-Toon
+ New-Journey-Art-Flux : strangerzonehf/New-Journey-Art-Flux
+ Casual-Pencil-Pro : strangerzonehf/Casual-Pencil-Pro
+ Realism-H6-Flux : strangerzonehf/Realism-H6-Flux

- Repository Page : strangerzonehf

The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.
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prithivMLmods 
posted an update about 1 month ago
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2629
Dropping Downstream tasks using newly initialized parameters and weights ([classifier.bias & weights]) support domain-specific 𝗶𝗺𝗮𝗴𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻. Based on siglip2-base-patch16-224 and DomainNet (single-domain, multi-source adaptation), with Fashion-MNIST & More for experimental testing. 🧤☄️

Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet

Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers 🤗.

Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782

Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754
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
prithivMLmods 
posted an update about 1 month ago
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Play with Orpheus TTS, a Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been fine-tuned to deliver human-level speech synthesis 🔥🗣️

👉GitHub [ Demo ] : https://github.com/PRITHIVSAKTHIUR/Orpheus-TTS-Edge

Demo supporting both text-to-speech and text-to-llm responses in speech.

> voice: tara, dan, emma, josh
> emotion: <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>.

🥠Orpheus-3b-0.1-ft
Model Page: canopylabs/orpheus-3b-0.1-ft

🥠Orpheus-3b-0.1-ft
Colab Inference Notebook: https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing

🥠Finetune [ orpheus-3b-0.1-pretrained ]
Resource: https://github.com/canopyai/Orpheus-TTS/tree/main/finetune

🥠Model-releases:
https://canopylabs.ai/model-releases
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