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OzTianlu  authored a paper 10 days ago
A Closed-Form Upper Bound for Admissible Learning-Rate Steps in Belief-Space Dynamics
OzTianlu  submitted a paper 10 days ago
A Closed-Form Upper Bound for Admissible Learning-Rate Steps in Belief-Space Dynamics
parkneurals  authored a paper about 1 month ago
KV Cache Recycling to Expand Usable Context Capacity in Low Parameter LLMs
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prithivMLmods 
posted an update about 18 hours ago
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I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.

Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.

Thanks for all the community support! 🤗❤️

🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
OzTianlu 
authored a paper 10 days ago
OzTianlu 
submitted a paper to Daily Papers 10 days ago
Aurelien-Morgan 
posted an update 17 days ago
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@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
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Ujjwal-Tyagi 
posted an update 19 days ago
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6 Open-Source Libraries to FineTune LLMs
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
→ Fastest way to fine-tune LLMs locally
→ Optimized for low VRAM (even laptops)
→ Plug-and-play with Hugging Face models

2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
→ Flexible LLM fine-tuning configs
→ Supports LoRA, QLoRA, multi-GPU
→ Great for custom training pipelines

3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
→ RLHF, DPO, PPO for LLM alignment
→ Built on Hugging Face ecosystem
→ Essential for post-training optimization

4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
→ Train massive models efficiently
→ Memory + speed optimization
→ Industry standard for scaling

5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
→ All-in-one fine-tuning UI + CLI
→ Supports multiple models (LLaMA, Qwen, etc.)
→ Beginner-friendly + powerful

6. PEFT
GitHub: https://github.com/huggingface/peft
→ Fine-tune with minimal compute
→ LoRA, adapters, prefix tuning
→ Best for cost-efficient training
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Sri-Vigneshwar-DJ 
posted an update 20 days ago
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![Feather DB LongMemEval Results]( Hawky-ai/longmemeval-results)

We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) — 500 questions across real multi-session conversations, up to 115K tokens each.

**Score: 0.693** · GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**

Per-axis breakdown:
→ Info-extraction: **0.942**
→ Knowledge-update: **0.714**
→ Multi-session: **0.606**
→ Temporal: **0.477** ← the hard one, Phase 9 addresses this

Architecture: Hybrid BM25+dense · adaptive temporal decay · embedded (no server) · p50 = 0.19ms · MIT

pip install feather-db

Raw results + audit JSONs: Hawky-ai/longmemeval-results
prithivMLmods 
posted an update 21 days ago
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Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.

🤗 Demo: prithivMLmods/Multimodal-Edge-Node
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
✅ Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps

🤗 > To learn more, visit the app page or the respective model pages.
Ujjwal-Tyagi 
posted an update 29 days ago
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This is the best set of AI and ML books and a full guide to learning machine learning from the ground up. This is my study material that I used, so I thought it would be helpful to share it with others. Like, share, and add it to your collection at Ujjwal-Tyagi/ai-ml-foundations-book-collection.
prithivMLmods 
posted an update 30 days ago
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Now, a collection of various compression schemes for Qwen3.6 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. 👇

🔗 Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
🔗 Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions

🤗 > To learn more, visit the app page or the respective model pages.
Ujjwal-Tyagi 
posted an update about 1 month 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
prithivMLmods 
posted an update about 1 month ago
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HY-World-2.0 — 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

🤗To learn more, visit the app page or the respective model pages.
Parveshiiii 
posted an update about 1 month ago
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🚀 Sonic: A lightweight Python audio processing library with tempo matching, BPM detection, time-stretching, resampling & track blending — now with GPU (CUDA) acceleration for 10x speed!

Perfect for quick remixes, batch edits or syncing tracks.

👉 https://github.com/Parveshiiii/Sonic

#Python #AudioProcessing #OpenSource #PyTorch
Aurelien-Morgan 
posted an update about 1 month ago
prithivMLmods 
posted an update about 1 month ago
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A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4× faster, uses ~1.4× 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.

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

🤗 > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update about 1 month 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. 👇

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

🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update about 1 month 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.

🤗 Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
🥽 SAM3: facebook/sam3
🔗 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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parkneurals 
authored a paper about 1 month ago
Parveshiiii 
posted an update about 2 months 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

prithivMLmods 
posted an update about 2 months 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.

🤗 Demo Space: prithivMLmods/SAM3-Plus-Qwen3.5
🥽 SAM3: facebook/sam3
🔗 Qwen-3.5: Qwen/Qwen3.5-2B

To learn more, visit the app page or the respective model pages.
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MaziyarPanahi 
posted an update about 2 months 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