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Team Tonic

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https://www.tonic-ai.com
josephpollack
Tonic-AI
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AI & ML interests

vision , multimedia , gradio, accessibility & cool demos

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Tonic  updated a Space 14 days ago
TeamTonic/README
peaceAsh  authored a paper 5 months ago
Maya: An Instruction Finetuned Multilingual Multimodal Model
Tonic  updated a Space 12 months ago
TeamTonic/README
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ZennyKenny 
posted an update about 15 hours ago
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Community! 💡💡💡

It's the last day to submit your datasets for the Reasoning Datasets Competition: https://www.bespokelabs.ai/blog/reasoning-datasets-competition

Here are my submissions:
- ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset
- ZennyKenny/cosa-benchmark-dataset
- ZennyKenny/tactical-military-reasoning-v.1.0
- ZennyKenny/tron-dataset-v.1.0

Have a look and drop a ❤️ or comment! Check out the entire collection of submissions here: https://huggingface.co/datasets?other=reasoning-datasets-competition
prithivMLmods 
posted an update 1 day ago
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Well, here’s the updated version with the 20,000+ entry sampled dataset for Watermark Filter Content Moderation models incl. [Food25, Weather, Watermark, Marathi/Hindi Sign Language Detection], post-trained from the base models: sigLip2 patch16 224 — now with mixed aspect ratios for better performance and reduced misclassification. 🔥

Models :
➮ Watermark-Detection : prithivMLmods/Watermark-Detection-SigLIP2
⌨︎ Watermark Detection & Batch Image Processing Experimentals, Colab Notebook : https://colab.research.google.com/drive/1mlQrSsSjkGimUt0VyRi3SoWMv8OMyvw3?usp=drive_link
➮ Weather-Image-Classification : prithivMLmods/Weather-Image-Classification
➮ TurkishFoods-25 : prithivMLmods/TurkishFoods-25
➮ Marathi-Sign-Language-Detection : prithivMLmods/Marathi-Sign-Language-Detection
➮ Hindi-Sign-Language-Detection : prithivMLmods/Hindi-Sign-Language-Detection

Datasets :
Watermark : qwertyforce/scenery_watermarks
Weather : prithivMLmods/WeatherNet-05-18039
Turkish Foods 25 : yunusserhat/TurkishFoods-25
Marathi Sign Language : VinayHajare/Marathi-Sign-Language
Hindi Sign Language : Vedant3907/Hindi-Sign-Language-Dataset

Collection : prithivMLmods/content-filters-siglip2-vit-68197e3357d4de18fb3b4d2b
ZennyKenny 
posted an update 3 days ago
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After hearing the news that Marc Andreessen thinks that the only job that is safe from AI replacement is venture capital: https://gizmodo.com/marc-andreessen-says-one-job-is-mostly-safe-from-ai-venture-capitalist-2000596506 🧠🧠🧠

The Reasoned Capital synthetic dataset suddenly feels much more topical: ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset 🔥🔥🔥

Really looking forward to potentially expanding this architecture and seeing how algorithmic clever investment truly is! 💰💰💰
ZennyKenny 
posted an update 4 days ago
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When I heard the Reasoning Dataset Competition deadline was extended to 9 May, I knew I had time to get in one more entry. 🔥🔥🔥

With the rise of Vibe Coding, and the potential risks that are introduced by humans letting LLMs build their apps for them, lots of people are (rightfully) concerned about the safety of the code that is hitting prod.

In response to that, I'm happy to present my final submission to the Reasoning Dataset Competition and attempt to start benchmarking the ability of LLMs to identify unsafe and / or exploitable code by way of the CoSa (Code Safety) benchmark: ZennyKenny/cosa-benchmark-dataset

Currently a curated set of 200 examples, calibrated on OpenAI's standard issue models (GPT-4.1, o4 mini, and GPT-3.5 Turbo) as "baseline performance" (70% decile). Check it out and drop a ❤️ if you think it could be useful or hit the Community section with suggestions / critiques.
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prithivMLmods 
posted an update 5 days ago
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The new versions of Midjourney Mix adapters have been dropped in stranger zone hf. These adapters excel in studio lighting portraits and painterly styles, trained using the style of strangerzonehf/Flux-Midjourney-Mix2-LoRA. They leverage 24-bit colored synthetic images generated form midjourney v6 to achieve high-quality image reproducibility and support adaptable aspect ratios, using Flux.1 as the base model. 🥳

Models [ ⌗ ]

> Flux-Midjourney-Painterly-LoRA : strangerzonehf/Flux-Midjourney-Painterly-LoRA
> Flux-Midjourney-Studio-LoRA : strangerzonehf/Flux-Midjourney-Studio-LoRA

> Collection : strangerzonehf/midjourney-mix-3-ft-flux1-dev-68165d58a2a08025852d63f3

> Space : prithivMLmods/FLUX-LoRA-DLC2

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.
Nymbo 
posted an update 7 days ago
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PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.
prithivMLmods 
posted an update 9 days ago
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Dropping downstream tasks using newly initialized parameters and weights supports domain-specific image classification post-training, based on the SigLIP-2 models: Patch-16/224, Patch-16/256, and Patch-32/256. For more details, please refer to the respective model cards : 🤗

+ watermark detection : prithivMLmods/Watermark-Detection-SigLIP2
+ resisc45 : prithivMLmods/RESISC45-SigLIP2
+ pacs dg : prithivMLmods/PACS-DG-SigLIP2
+ 3d printed or not : prithivMLmods/3D-Printed-Or-Not-SigLIP2
+ formula or text : prithivMLmods/Formula-Text-Detection

Categorizing Un-Safe Content :
- explicit content patch16 256 : prithivMLmods/siglip2-x256-explicit-content
- explicit content patch32 256 : prithivMLmods/siglip2-x256p32-explicit-content

Collection :
> SigLIP2 Content Filters 042025 Final : https://huggingface.co/collections/prithivMLmods/siglip2-content-filters-04202-final-680fe4aa1a9d589bf2c915ff
> SigLIP2 : google/siglip2-67b5dcef38c175486e240107
> SigLIP2 Multilingual Vision-Language Encoders : https://arxiv.org/pdf/2502.14786
ZennyKenny 
posted an update 9 days ago
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The same way the advent of Adobe Illustrator has led to innovation in the way that creative professionals work, I earnestly believe that AI will do the same (contrary to the popular opinion that it represents some regression in the world of creatives).

@natalika and I were speaking about this topic and like most illustrators she has some understandable concerns about the spread of AI in her field. She also told me how much time she spends generating concept art that will never see the light of day in >98% of cases. 💡

To me, that sounded like a perfect opportunity to leverage image diffusion in a way that helps artists spend more time creating cool stuff rather than just malevolently mining their work and using it without credit. Using the Black Forest Labs base model FLUX, Replicate, and about $5 of H100 compute, I post-trained a LoRA adapter on a set of her images associated with one project she's working on and spun up an app with Hugging Face Spaces (and Zero GPU for the win).

I give you, Natalie Diffusion: ZennyKenny/natalie-diffusion

Now, generating concept art in her particular style takes seconds instead of hours and when it's time to put the work into production, a human designer is still invaluable. And building it in the open hopefully inspires other use cases amongst other designers. 🖖
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ZennyKenny 
posted an update 10 days ago
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I've created a new dataset using the Algorithm of Thoughts architecture proposed by Sel et al. (2023) in a reasoning context. (paper: https://arxiv.org/pdf/2308.10379)

The dataset simulates the discovery phase of a fictitious VC firm called Reasoned Capital and, once expanded, can be used to create models which are able to make complex, subjective financial decisions based on different criteria.

The generation process encourages recursive problem-solving in increasingly complex prompts to encourage models to assess and reevaluate the conclusions and generated opinions of upstream models. Pretty neat stuff, and I'm not aware of this architecture being used in a reasoning context anywhere else.

Check it out: ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset
prithivMLmods 
posted an update 13 days ago
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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 13 days 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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Tonic 
updated a Space 14 days ago
prithivMLmods 
posted an update 15 days ago
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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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hesamation 
posted an update 18 days ago
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The best researchers from DeepSeek, OpenAI, Microsoft, and ByteDance explored RL and Reasoning in LLMs,

Here's some of their key findings:

1/ RL can further improve distilled models. These models are essentially SFT fine-tuned with the data generated by larger models, and the SFT+RL combo does not disappoint.

This is verified in the DeepSeek-R1 paper.

2/ both GRPO and PPO algorithms suffer from length bias; they encourage longer responses. This can be tackled by introducing explicit rewards based on the length of the answer.

3/Most reasoning research is focused on code and math. But training models on logic puzzles improves them for mathematical tasks too.

This shows the RL reasoning is generalized beyond the specific domain knowledge.

Previous research also shows RL can be a great generalizer.

4/The reasoning might not be only induced by RL; it might already be hidden in the base models due to the pre-training and CoT data they were trained on.

So while RL does wake up the reasoning beast, maybe it's not the only solution (e.g. other methods such as distillation)

5/ back to the length bias; reasoning models tend to generate longer responses for wrong answers. RL might be the culprit.

RL favours longer answers when the reward is negative, to dilute the penalty per individual token and lower the loss.

This might explain the "aha" moments!

6/ OpenAI's competitive programming paper showed an interesting finding:

o3 can learn its own test-time strategies (like writing an inefficient but correct solution to verify the answer of an optimized solution)

RL helps LLMs develop their own reasoning & verification methods.
The recent article by @rasbt helped me a lot in getting a broad view of the recent research on reasoning models.

He also lists more influential papers on this topic, It's a must-read if you're interested.

check it out 👇
https://magazine.sebastianraschka.com/p/the-state-of-llm-reasoning-model-training
prithivMLmods 
posted an update 21 days ago
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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

.🤗
hesamation 
posted an update 21 days ago
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OpenAI just released a 34-page practical guide to building agents,

Here's 10 things it teaches us:

1➜ agents are different from workflows: they are complete autonomous systems that perform tasks on your behalf. many applications use LLMs for workflows, but this is not an agent.

2➜ use them for tricky stuff: complex decision making, dynamic rules, unstructured data

3➜ core recipe: each agent has three main components: Model (the brain), Tools, Instructions on how to behave

4➜ choose the right brain: set up evals to get a baseline performance, use a smart model to see what's possible, gradually downgrade the model for cost and speed

5➜ tools are key: choose well-defined and tested tools. an agent needs tools to retrieve data and context, and take actions.

6➜ instruction matters A LOT: be super clear telling the agent its goals, steps, and rules. Vague instructions = unpredictable agent. Be explicit.

7➜ start simple, then scale: often a single agent with several tools is ok. don't jump to complex multi-agent systems immediately.

8➜ if you use multi-agents: you can have a "manager" agent directing traffic to specialist agents, or have agents hand off tasks to each other.

9➜ gaurdrails are a MUST: check user input for weird stuff, make sure the agent isn't about to do something risky, filter out private info, block harmful content. Don't let it run wild.

10➜ build and plan for humans: start small, test, improve. always have a plan for when the agent gets stuck or is about to do something high-risk.

Download: https://t.co/fJaCkgf7ph
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ZennyKenny 
posted an update 22 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.
prithivMLmods 
posted an update 23 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.
hesamation 
posted an update 23 days ago
ZennyKenny 
posted an update 29 days ago