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guifre103Β  updated a model about 1 month ago
LocalLLaMA/GuifreAI
smcleodΒ  new activity about 2 months ago
LocalLLaMA/GuifreAI:What's the organisation & project relating to (I'm a r/localllama mod)
guifre103Β  updated a model about 2 months ago
LocalLLaMA/GuifreAI
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Ujjwal-TyagiΒ 
posted an update about 12 hours ago
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We are hiring at Shirova AI. We need AI researchers and engineers to work in our research lab. Shirova AI is a research lab in India, so we can help our researchers move to nearby workspaces or let them work from home without ever coming to the lab. We're building our founding team, so the pay will be good. You can learn, so don't hesitate to mail us at: careers@shirova.com
prithivMLmodsΒ 
posted an update 4 days ago
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3763
HY-World-2.0 β€” A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.

> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo

πŸ€—To learn more, visit the app page or the respective model pages.
ParveshiiiiΒ 
posted an update 7 days ago
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πŸš€ Sonic: A lightweight Python audio processing library with tempo matching, BPM detection, time-stretching, resampling & track blending β€” now with GPU (CUDA) acceleration for 10x speed!

Perfect for quick remixes, batch edits or syncing tracks.

πŸ‘‰ https://github.com/Parveshiiii/Sonic

#Python #AudioProcessing #OpenSource #PyTorch
Aurelien-MorganΒ 
posted an update 8 days ago
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Launching a workweek of @retrain-pipelines wheels.

Day #1 : Compose
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prithivMLmodsΒ 
posted an update 9 days ago
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A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4Γ— faster, uses ~1.4Γ— less VRAM, and maintains near-identical image quality. It has ~28M parameters with narrower channels [96, 192, 384, 384] vs. [128, 256, 512, 512], and the demo supports sequence generation by running both decoders simultaneously and comparing the results side by side.

πŸ€— Comparator: prithivMLmods/Flux.2-4B-Decoder-Comparator
πŸ”— FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
πŸ”— GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
🚁 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

πŸ€— > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ 
posted an update 10 days ago
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Now, a collection of various compression schemes for Gemma 4 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. πŸ‘‡

πŸ”—Gemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
πŸ”—Gemma 4 Uncensored [MAX] + Compression(s) - [`Ξ² ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
πŸ”—Gemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
πŸ”—Gemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf

πŸ€— > To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ 
posted an update 13 days ago
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Now the demo for image detection based on SAM3 and Gemma-4 (*Filter) is available on Spaces, using full-fledged Transformers inference with multimodal reasoning for processed images. It also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.

πŸ€— Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
πŸ₯½ SAM3: facebook/sam3
πŸ”— gemma-4-E2B-it: google/gemma-4-E2B-it

To learn more, visit the app page or the respective model pages.
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ParveshiiiiΒ 
posted an update 14 days ago
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Excited to announce my latest open-source release on Hugging Face: Parveshiiii/breast-cancer-detector.

This model has been trained and validated on external datasets to support medical research workflows. It is designed to provide reproducible benchmarks and serve as a foundation for further exploration in healthcare AI.

Key highlights:
- Built for medical research and diagnostic study contexts
- Validated against external datasets for reliability
- Openly available to empower the community in building stronger, more effective solutions

This release is part of my ongoing effort to make impactful AI research accessible through **Modotte**. A detailed blog post explaining the methodology, dataset handling, and validation process will be published soon.

You can explore the model here: Parveshiiii/breast-cancer-detector

#AI #MedicalResearch #DeepLearning #Healthcare #OpenSource #HuggingFace

prithivMLmodsΒ 
posted an update 16 days ago
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The demo for Image Detection (*Filter) based on SAM3 and Qwen-3.5 is now available on Hugging Face Spaces using Transformers inference, with multimodal reasoning for processed images, and it also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.

πŸ€— Demo Space: prithivMLmods/SAM3-Plus-Qwen3.5
πŸ₯½ SAM3: facebook/sam3
πŸ”— Qwen-3.5: Qwen/Qwen3.5-2B

To learn more, visit the app page or the respective model pages.
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MaziyarPanahiΒ 
posted an update 21 days ago
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Training mRNA Language Models Across 25 Species for $165

We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.

https://huggingface.co/blog/OpenMed/training-mrna-models-25-species
OzTianluΒ 
posted an update 22 days ago
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https://github.com/lizixi-0x2F/March
I just released March, an open-source high-performance KV cache sharing library for LLM inference that uses Trie-based prefix deduplication.
When you run LLM services, you often see thousands of requests sharing the same system prompt and conversation history. But traditional KV cache systems store each sequence separately β€” duplicating the exact same data over and over again. Pure waste.
March uses a Trie structure to automatically detect and reuse identical token prefixes. Instead of storing [system_prompt + history] 1000 times, it's stored once. Everyone shares it.
- 80-97% memory reduction in prefix-heavy workloads (tested on SmolLM2-135M with 500 multi-turn conversations)
- Zero-copy queries β€” returns direct pointers into the memory pool, no expensive memcpy on the hot path
- Predictable memory usage β€” fixed-size page pool with O(L) complexity
- Trade-off: slightly slower than dict O(1) lookup, but the memory savings are worth it in production
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Ujjwal-TyagiΒ 
posted an update 22 days ago
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I am sharing my study material for AI & ML, these books are really a "bible" and gives very strong foundation, I also have given guidance, introduction and my master notes in the dataset repo card! I hope you will find them helpful, if you have any queries, just start a discussion and I am always there to help you out!
Ujjwal-Tyagi/ai-ml-foundations-book-collection
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prithivMLmodsΒ 
posted an update 25 days ago
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Flux-Klein-KV-Edit-Consistency demo is now available on Spaces. It preserves character identity and delivers high-quality, realistic results after edits. No need for any special prompts, just upload the image, type your prompt, and get the resulting image blazing fast.

πŸ”₯ Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
πŸ€— Model: black-forest-labs/FLUX.2-klein-9b-kv
πŸ€— Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
πŸ”— Gradio Server Mode: https://www.gradio.app/main/guides/server-mode

βž” Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.

βž” Gradio Server Mode is now available from gradio@v6.10.0.

To learn more, visit the app page or the respective model pages.
ParveshiiiiΒ 
posted an update 27 days ago
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Just did something I’ve been meaning to try for ages.

In only 3 hours, on 10 billion+ tokens, I trained a custom BPE + tiktoken-style tokenizer using my new library microtok β€” and it hits the same token efficiency as Qwen3.

Tokenizers have always felt like black magic to me. We drop them into every LLM project, but actually training one from scratch? That always seemed way too complicated.

Turns out it doesn’t have to be.

microtok makes the whole process stupidly simple β€” literally just 3 lines of code. No heavy setup, no GPU required. I built it on top of the Hugging Face tokenizers library so it stays clean, fast, and actually understandable.

If you’ve ever wanted to look under the hood and build your own optimized vocabulary instead of just copying someone else’s, this is the entry point you’ve been waiting for.

I wrote up the full story, threw in a ready-to-run Colab template, and dropped the trained tokenizer on Hugging Face.

Blog β†’ https://parveshiiii.github.io/blogs/microtok/
Trained tokenizer β†’ https://huggingface.co/Parveshiiii/microtok
GitHub repo β†’ https://github.com/Parveshiiii/microtok
SeverianΒ 
posted an update 27 days ago
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I’ve been working on a new mathematical approach to real-time video compositing and background removal, and I wanted to share a live demo.

Traditionally, real-time keyers either use 3D color-space bounding boxes (which struggle with semi-transparent hair and motion blur) or heavy Machine Learning models (which require massive GPU compute and often suffer from temporal "jitter" on the edges).

I wanted to see if I could solve this using purely deterministic math so it could run client-side in a standard browser.

The engine uses a custom mathematical framework I call CMT SRL SEFA. Instead of looking at raw color values or guessing semantics like an AI, it treats the video feed as complex-encoded sequences. It uses harmonic frequencies to map phase geometry and applies a "Stability Cost Function" to find the global minimum stability. In short: it isolates the foreground from the background by measuring signal complexity and structural contradictions.

Give it a try using your own messy plates and such. As I am not a VFX artist, I am curious to hear thoughts and what should be improved upon and made better

https://huggingface.co/proxy/severian-cmt-sefa-realtime-vfx-keyer.hf.space/
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MaziyarPanahiΒ 
posted an update 28 days ago
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We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.

Everything is open-sourced: datasets, adapters, and code.

https://huggingface.co/blog/OpenMed/synthvision
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prithivMLmodsΒ 
posted an update about 1 month ago
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Map-Anything v1 (Universal Feed-Forward Metric 3D Reconstruction) demo is now available on Hugging Face Spaces. Built with Gradio and integrated with Rerun, it performs multi-image and video-based 3D reconstruction, depth, normal map, and interactive measurements.

πŸ€— Demo: prithivMLmods/Map-Anything-v1
πŸ€— Model: facebook/map-anything-v1
πŸ€— Hf-Papers: MapAnything: Universal Feed-Forward Metric 3D Reconstruction (2509.13414)
prithivMLmodsΒ 
posted an update about 1 month ago
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Introducing QIE-Bbox-Studio! πŸ”₯πŸ€—

The QIE-Bbox-Studio demo is now live β€” more precise and packed with more options. Users can manipulate images with object removal, design addition, and even move objects from one place to another, all in just 4-step fast inference.

πŸ€— Demo: prithivMLmods/QIE-Bbox-Studio
πŸ”— GitHub: https://github.com/PRITHIVSAKTHIUR/QIE-Bbox-Studio

πŸš€ Models [LoRA] :

● QIE-2511-Object-Mover-Bbox: prithivMLmods/QIE-2511-Object-Mover-Bbox
● QIE-2511-Object-Remover-Bbox-v3: prithivMLmods/QIE-2511-Object-Remover-Bbox-v3
● QIE-2511-Outfit-Design-Layout: prithivMLmods/QIE-2511-Outfit-Design-Layout
● QIE-2509-Object-Remover-Bbox-v3: prithivMLmods/QIE-2509-Object-Remover-Bbox-v3
● QIE-2509-Object-Mover-Bbox: prithivMLmods/QIE-2509-Object-Mover-Bbox

πŸš€ Collection:

● Qwen Image Edit [Layout Bbox]: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox

To learn more, visit the app page or the respective model pages.
NymboΒ 
posted an update about 1 month ago
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We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
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guifre103Β 
updated a model about 1 month ago