Prithiv Sakthi's picture

Prithiv Sakthi

prithivMLmods

AI & ML interests

computer vision, multimodality, adapters @starngerzonehf @strangerguardhf

Recent Activity

liked a model about 10 hours ago
prithivMLmods/Road-Subsigns-Classification
liked a model about 10 hours ago
prithivMLmods/Fashion-Product-Usage
updated a model about 10 hours ago
prithivMLmods/La-Superba-14B-Y.2
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prithivMLmods's activity

reacted to danielhanchen's post with 🔥 8 days ago
reacted to onekq's post with 🚀 9 days ago
posted an update 9 days ago
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3209
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 : prithivMLmods/siglip2-content-filters-models-67f001055ec2bed56ca41f6d
reacted to hesamation's post with ❤️ 10 days ago
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The best researchers from Yale, Stanford, Google DeepMind, and Microsoft laid out all we know about Agents in a 264-page paper [book],

Here are some of their key findings:

They build a mapping of different agent components, such as perception, memory, and world modelling, to different regions of the human brain and compare them:

- brain is much more energy-efficient
- no genuine experience in agents
- brain learns continuously, agent is static

An agent is broken down to:
- Perception: the agent's input mechanism. can be improved with multi-modality, feedback mechanisms (e.g., human corrections), etc.
- Cognition: learning, reasoning, planning, memory. LLMs are key in this part.
- Action: agent's output and tool use.

Agentic memory is represented as:
- Sensory memory or short-term holding of inputs which is not emphasized much in agents.
- Short-term memory which is the LLM context window
- Long-term memory which is the external storage such as RAG or knowledge graphs.

The memory in agents can be improved and researched in terms of:
- increasing the amount of stored information
- how to retrieve the most relevant info
- combining context-window memory with external memory
- deciding what to forget or update in memory

The agent must simulate or predict the future states of the environment for planning and decision-making.

ai world models are much simpler than the humans' with their causal reasoning (cause-and-effect) or physical intuition.

LLM world models are mostly implicit and embedded.

EMOTIONS are a deep aspect of humans, helping them with social interactions, decision-making, or learning.

Agents must understand emotions to better interact with us.

But rather than encoding the feeling of emotions, they have a surface-level modelling of emotions.

Perception is the process by which an agent receives and interprets raw data from its surroundings.

READ PAPER: Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems (2504.01990)
posted an update 10 days ago
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2099
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
replied to their post 13 days ago
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There is nothing intended for commercial use or profit; this is purely for experimental purposes with models based on voice essences. I have adhered strictly to the base model I used, specifically the 0th version. Even the 'Orpheus' models, which are licensed under Apache-2.0, follow their own policies and alignment. I will ensure compliance with the model I have post-trained and its licenses, specifically Llama 3.2. I am not claiming ownership of the model—everything in it is calibrated within the framework of Llama 3.2

So, I will continue following the work I have done. The point is, if someone intends to use the model, they must also adhere to the license of the original material that I have. @JLouisBiz

reacted to hesamation's post with ❤️ 14 days ago
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2681
What, How, Where, and How Well? This paper reviews test-time scaling methods and all you need to know about them:
> parallel, sequential, hybrid, internal scaling
> how to scale (SFT, RL, search, verification)
> metrics and evals of test-time scaling

🔗paper: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models (2503.24235)

If you want to learn what inference-time compute scaling is @rasbt has a great blog post on that:
https://magazine.sebastianraschka.com/p/state-of-llm-reasoning-and-inference-scaling
replied to their post 17 days ago
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@JLouisBiz

But the model is licensed under Llama 3.2, on which the base model is also built. The License Rights and Redistribution section states that the grant of rights allows the use of the content for derivative works and modifications to the Llama materials, provided that 'Built with Llama' is properly mentioned and the Llama is displayed wherever it is used. I believe I have properly mentioned that and have not overruled anything from the license.

Provided a copy of the license. Include 'Llama' at the beginning of the model’s name. In the 'About' section of the model, mention that it is built based on Llama.

" If you use the Llama Materials or any outputs or results of the Llama Materials to 𝗰𝗿𝗲𝗮𝘁𝗲, 𝘁𝗿𝗮𝗶𝗻, 𝗳𝗶𝗻𝗲 𝘁𝘂𝗻𝗲, 𝗼𝗿
𝗼𝘁𝗵𝗲𝗿𝘄𝗶𝘀𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗮𝗻 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹, 𝘄𝗵𝗶𝗰𝗵 𝗶𝘀 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗼𝗿 𝗺𝗮𝗱𝗲 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲, 𝘆𝗼𝘂 𝘀𝗵𝗮𝗹𝗹 𝗮𝗹𝘀𝗼 𝗶𝗻𝗰𝗹𝘂𝗱𝗲 “𝗟𝗹𝗮𝗺𝗮”
at the beginning of any such AI model name. "

Please refer to the Llama 3.2 License [ https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt ], specifically the License Rights and Redistribution section, clauses (a) and (b).

posted an update 17 days ago
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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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reacted to AdinaY's post with 🔥 20 days ago
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1657
A new OPEN Omni model just dropped by @Alibaba_Qwen on the hub🔥🤯

Qwen2.5-Omni: a 7B end-to-end multimodal model
Qwen/Qwen2.5-Omni-7B

✨ Thinker-Talker architecture
✨ Real-time voice & video chat
✨ Natural speech generation
✨ Handles text, image, audio & video
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reacted to tomaarsen's post with 🔥 20 days ago
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2413
‼️Sentence Transformers v4.0 is out! You can now train and finetune reranker models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also prove that finetuning on your domain helps much more than you might think.

1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!

Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.

2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE

3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)

4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1
replied to clem's post 20 days ago
posted an update 20 days ago
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1696
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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posted an update 23 days ago
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2605
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

posted an update 26 days 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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reacted to jsulz's post with 🤗 27 days ago
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If you've been following along with the Xet Team's ( xet-team ) work, you know we've been working to migrate the Hugging Face Hub from Git LFS and to Xet.

Recently, we launched a waitlist to join the movement to Xet (join here! https://huggingface.co/join/xet ) but getting to this point was a journey.

From the initial proof of concept in August, to launching on the Hub internally, to migrating a set of repositories and routing a small chunk of download traffic on the Hub through our infrastructure. Every step of the way has been full of challenges, big and small, and well worth the effort.

Over the past few weeks, with real traffic flowing through our services we’ve tackled some truly gnarly issues (unusual upload/download patterns, memory leaks, load imbalances, and more) and resolved each without major disruptions.

If you're curious about how this sliver of Hub infrastructure looks as we routed traffic through it for the first time (and want a deep dive full of Grafana and Kibana charts 🤓) I have a post for you.

Here's an inside look into the day of our first migrations and the weeks following, where we pieced together solutions in real time.

https://huggingface.co/blog/xet-on-the-hub
reacted to onekq's post with 🚀 29 days ago
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Introducing 🎉 OneSQL-v0.1🥳, our first text-to-SQL model based on Qwen2.5-Coder. This model has achieved an EX score of 63.33 on the BIRD leaderboard (https://bird-bench.github.io/).

The model family includes 7B and 32B,
onekq-ai/onesql-v01-qwen-67d8e3eb1611c5532bb90c5f
and can be also found on Ollama (https://ollama.com/onekq/OneSQL-v0.1-Qwen)

My goal is to make OneSQL the most usable open-weights model for text-to-SQL. I'm currently working on best practices to help users use this model the right away and avoid pitfalls. After that, I plan to train the next version to push for a higher EX score.

Enjoy this model and feel free to share comments/questions 🤗
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reacted to mlabonne's post with 🚀 30 days ago
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6109
✂️ Gemma 3 Abliterated

I noticed that Gemma 3 was much more resilient to refusal removal than other models like Qwen 2.5.

I experimented with different recipes and improved the abliteration technique I wrote about last year.

It's still experimental but the refusal rate is super low in my tests. Enjoy!

mlabonne/gemma-3-4b-it-abliterated
mlabonne/gemma-3-12b-it-abliterated
mlabonne/gemma-3-27b-it-abliterated

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reacted to Kseniase's post with 🔥 about 1 month ago
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7796
15 types of attention mechanisms

Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.

Here is a list of 15 types of attention mechanisms used in AI models:

1. Soft attention (Deterministic attention) -> Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.

2. Hard attention (Stochastic attention) -> Effective Approaches to Attention-based Neural Machine Translation (1508.04025)
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.

3. Self-attention -> Attention Is All You Need (1706.03762)
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.

4. Cross-Attention (Encoder-Decoder attention) -> Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2104.08771)
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.

5. Multi-Head Attention (MHA) -> Attention Is All You Need (1706.03762)
Multiple attention “heads” are run in parallel.​ The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.

6. Multi-Head Latent Attention (MLA) -> DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2405.04434)
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.

7. Memory-Based attention -> End-To-End Memory Networks (1503.08895)
Involves an external memory and uses attention to read from and write to this memory.

See other types in the comments 👇
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posted an update about 1 month ago
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Hey Guys! One Small Announcement 🤗
Stranger Zone now accepts LoRA requests!

✍️Request : https://huggingface.co/spaces/strangerzonehf/Request-LoRA [ or ] https://huggingface.co/spaces/strangerzonehf/Request-LoRA/discussions/1

Page : strangerzonehf

Describe the artistic properties by posting sample images or links to similar images in the request discussion. If the adapters you're asking for are truly creative and safe for work, I'll train and upload the LoRA to the Stranger Zone repo!

Thank you!