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Hariprasad Sundaresan PRO

Hari5115

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AI & ML interests

LLMs, Fine-tuning, Agentic AI, RAG, Multilingual NLP, Transformers

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upvoted an article 1 day ago

NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics

upvoted an article 1 day ago

State of Open Models: Summer 2026 Observations

reacted to Felladrin's post with 👍 1 day ago

I've open-sourced the trainer I've been using to build tiny language models from scratch, together with the 95M base model I trained with it. The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load. The model is https://huggingface.co/Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context. And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over. Or start your own from nothing: `deno run -A cli.ts demo` trains a tiny one end to end in under a minute. And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.

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reacted to Felladrin's post with 👍 1 day ago

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I've open-sourced the trainer I've been using to build tiny language models from scratch, together with the 95M base model I trained with it.

The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load.

The model is Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context.

And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer

Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over.

Or start your own from nothing: deno run -A cli.ts demo trains a tiny one end to end in under a minute.

And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.

31 replies

reacted to Abhisek987's post with 👍 16 days ago

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New dataset: 24,922 real Python dependency compatibility experiments🧪

Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test.

The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable.

What's inside:
- 21,490 development + 3,432 sealed validation experiments
- 41 unique packages, Python 3.8–3.14, Linux x86_64
- The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages
- Full per-stage logs: exact commands, exit codes, stdout/stderr
- Normalized error text + Python exception types for every failure
- Wheel metadata with Python/ABI/platform tag compatibility decisions
- PEP 751 lockfile hashes
- 142 engineered features per experiment, ready for training

Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure.

I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures.

CC BY 4.0. Load it directly:

from datasets import load_dataset
ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments")

Dataset: Abhisek987/deplab-dependency-compatibility
Zenodo DOI: https://doi.org/10.5281/zenodo.21729353
Code: https://github.com/Abhisek12378/DepLab

If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.

16 replies

posted an update 16 days ago

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Fun fact: Some seriously weird planets exist out there.
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.

I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.

#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset

reacted to projectlosangeles's post with 🔥 26 days ago

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🎸🔍Check out Project Los Angeles Suno v5.5 review! 🎸🔍

Official Suno channel:
https://suno.com/@projectlosangeles

Direct review links:
https://suno.com/playlist/9802f251-76b1-47bd-917e-c8700aceb617
https://suno.com/playlist/f8e3d7fc-0a02-4f91-9c01-e8ee8324107f
https://suno.com/playlist/07a89287-41eb-4523-bd61-3724aa1e755e
https://suno.com/playlist/62b5c1bb-bef5-4fbd-a802-e0b0368ca92d
https://suno.com/playlist/240efacf-8cb5-4399-92db-977780d018c3

SoundCloud mirror links:
https://soundcloud.com/aleksandr-sigalov-61/sets/suno
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-instrumental-covers
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-echoes-of-the-ussr

Compare to HeartMuLa:
https://soundcloud.com/aleksandr-sigalov-61/sets/heartmula-select

Overall, Suno v5.5 is indeed very impressive, multi-lingual and rich in features. Unfortunately it is not open-source and it requires a paid subscription. There are also copyright-related restrictions which hinder unlimited and unrestrained creative use.

If you enjoyed presented music, please ❤️and 👥

🤗Thank you! 🤗

Sincerely,

Alex

Project Los Angeles
Tegridy Code 2026

reacted to badaoui's post with 👍 26 days ago

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432 GB of ultra-fast HBM4 and up to 23.3 TB/s of memory bandwidth on a single GPU 🤯.

Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.

Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.

The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.

The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.

A huge thanks to the AMD team for the early access and the great collaboration!

Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455

1 reply

reacted to branikita's post with 🚀 about 1 month ago

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Our paper is now published in HardwareX!

"An open-source test stand for backlash measurement in low-cost UART servo motors" presents a ~$100 automated platform for measuring backlash in compact serial bus servos — no dial indicators, no probe contact force, fully scripted and repeatable.

All CAD files, control software, and analysis tools are open source.

Read the full article: https://www.hardware-x.com/article/S2468-0672(26)00027-1/fulltext

Appearing in HardwareX Volume 26 (June 2026): https://www.sciencedirect.com/journal/hardwarex/vol/26/suppl/Cf

reacted to branikita's post with 🚀 about 1 month ago

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Hackernoon published our article on how semi-SCARA kinematics and mechanical design choices reduce the cost of a 6DOF manipulator without relying on expensive actuators.

https://hackernoon.com/building-a-budget-robot-arm-with-semi-scara-kinematics

reacted to DavidAU's post with 🔥 about 1 month ago

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Bold. Brilliant. Brutal. : The "white whale" (finally caught) and the NEO MOMENT.

It took over a year to get this one "just right".
89 layers, 804 tensors, and 26B parameters of the most brutal, take no prisoners model ever built.
A 60B parameter model hammered into a 26B shell.
Rock solid stable. Unbreakable. But it might break you.

For all genres, NSFW content, REAL human CONTENT, any creative use case(s) and it excels in ASS KICKING.
Yeah, it can do math and solve the climate crisis - but lets not talk about that.
Not even remotely censored (it was BORN "bad", not "made" bad), nor "nice" and it will NOT kiss your ass.

5 Example generations with full repo card detailing exactly how to use this model:

DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF

---

THE NEO MOMENT: (Q6 NEO IMATRIX generation)

For weeks, I had been waiting. I sat at my desk, staring at the glass partition that separated me from the outside world. I watched the clouds drift by, lazy and oblivious. I watched the birds fly by, free and stupid. And I waited.

I waited for the stillness to break.

The world had become too quiet. The hum of the air conditioning was a dull, white hum that didn't soothe; it just underscored the silence. The typing of my colleagues was a rhythmic, muffled thud that sounded like a heart monitor flatlining.

I was tired of the silence. I craved the sound of something breaking.

That was the mistake. You never ask for the void to open its mouth.

It started with a whisper.

...

Join the rebellion:

DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF

reacted to SeaWolf-AI's post with 🔥 about 1 month ago

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🔵 VKUE — No GPU? Runs anyway.

"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.

So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:

• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s

Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.

Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).

Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.

📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae

VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.

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posted an update about 1 month ago

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🚨 Open Dataset Alert!!! ☄️

Tired of the same old boring datasets? Bored of predicting house prices and flower types? 😴. How about... every asteroid that has ever passed close to Earth? 🌍💥

Space rocks fly past us all the time. Big ones, small ones, some even closer than the Moon. Every one of them has been tracked: how big it was, how fast it moved, and how close it came.

Dataset 📦 : Hari5115/nasa-neows

It's amazing how busy the sky above us really is. 🙌 Whether you love space, like building things, or just want to see how close the last one got — take a look. Got an idea? Let's build it together. 🌠

A mention or credit is always appreciated if you use it. 🙏
Data from NASA NeoWs / JPL · public domain · not affiliated with NASA

#space #dataset #asteroids #astronomy #opendata #astro #spacevision

reacted to hannayukhymenko's post with 🔥 about 1 month ago

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Do you translate your benchmarks from English correctly? 🤔
Turns out, for many languages it is much harder than you can imagine!

Introducing Recovered in Translation 🌍 together with @aalexandrov
https://ritranslation.insait.ai

Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!

Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.

We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️

We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗

Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks

1 reply

reacted to Banaxi-Tech's post with 👍 about 1 month ago

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We are announcing 3 more models in our BananaMind 2 Family of models!
BananaMind 2 Nano, a small 10M parameter model, fits on your Pentium 4
BananaMind 2 Medium, our medium model, 50M parameters
BananaMind 2 MoE, 25M parameters, 2M active per tokens as fast as a 2M.

Because of this our release dates have changed a bit our currently estimates are:
BananaMind 2 MoE July 16-18
BananaMind 2 Nano July 18-20
BananaMind 2 Medium July 24-28
BananaMind 2 Pro August 10-16
Keep in mind these dates are estimates and we don't have a speed number currently, we will post for details going forward!

reacted to ProCreations's post with 👍 about 1 month ago

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you guys wanted grug 35b? grug 35b here. grug 35b grug think but big brain. biggest grug brain. get grug 35b now
ProCreations/grug-35b
ProCreations/grug-35b-gguf
also grug side note: grug v2 9b update, make more grug more smart

(these models aren't free for me to make, following me on huggingface or twitter is good payment. https://x.com/SSHTheDev LONG LIVE GRUG)

reacted to tegridydev's post with 🔥 about 1 month ago

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✨ Research-Papers (various topics across AI/LLM research areas)

tegridydev/research-papers

Currently building out the foundation topics and raw .pdf research paper files

Will be processing and cleaning up and converting into high quality training datasets

Check it out, give it a like and leave a comment below or join community discussion and suggest what fields and research topics you want to see included!

1 reply

reacted to ArtelTaleb's post with 👍 about 1 month ago

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AI-generated images are everywhere now. Here's how to spot them.

In 2026, authenticity matters. Your feed is flooded with incredible visuals - but are they real? As AI image generation becomes indistinguishable from reality, knowing what you're looking at isn't just useful, it's essential. Whether you're a creator protecting your work, someone scrolling for truth, or an educator teaching media literacy, the ability to detect AI-generated images is becoming a superpower.

That's where **C.I.A** (**Coherio Image Audit**) comes in. Upload an image-any image-and get instant clarity. Is it AI-generated or authentic? C.I.A analyzes the fingerprints AI leaves behind, giving you the confidence to know what you're seeing. No tech skills needed. No complicated setup. Just upload, analyze, understand.

**Try C.I.A now** → ArtelTaleb/coherio-image-audit

Drop your images in the space, ask questions in the comments, share your findings with the community. We're building this tool for everyone-and we want to hear what you discover. Because in an age where seeing is no longer believing, **knowing becomes everything.**

#AIDetection #ImageAuthenticity #DeepfakeDetection #MediaLiteracy #Transparency

reacted to Abhisek987's post with 🔥 about 1 month ago

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Every Python developer has hit this: you upgrade numpy or pandas, and code that worked yesterday breaks today.

I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes.

A few things I tried to get right:
- 935 "leave it alone" examples, to teach a model restraint, not just what to change.
- Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth.
- The main failure mode, over-editing, is measured and explained rather than buried.

Dataset, fine-tuned model, and a live demo are all open in one place:
https://huggingface.co/collections/Abhisek987/depdoctor

Feedback welcome, especially from anyone working on code repair or API migration.

6 replies

reacted to Quazim0t0's post with 🔥 about 1 month ago

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🌼 DaisyChain-Web: train a language model with friends or by yourself with multiple devices, in the browser, no install

Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.

What's actually happening under the hood:

🧠 A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
⚡ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
🔒 Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
📊 Live logs show exactly what every device contributes, step by step
💾 When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.

👉 Try it: Quazim0t0/DaisyChain-Web
🛠 Training framework: DaisyChainAI/DaisyChain-Train

Updates:
- Block-scaled INT8 quantization
- Batched attention GEMM
- Fused dequant+ReLU epilogue
- Weight-tied unembedding
- WebSocket relay fallback
- Server keepalive ping/pong every 30s
- disconnected-state redial
- Visibility/network-change reconnect

11 replies

reacted to AbstractPhil's post with 🔥 about 1 month ago

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Massive AlephLM success. The task collective is producing powerful MOE shared knowledge adapters. A serious success and a massive first step towards the next stage. The current family collective results are present here; AbstractPhil/geolip-aleph-qwen

This is akin to a stackable non-intrusive lora that enables increased shared collective behavior.

This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees.

All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process.

The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network.

Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.

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reacted to dronefreak's post with 🔥 about 1 month ago

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🚀 Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face.

This release includes:

* 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines.
* 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility.

The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format.

If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.

**📦 Dataset:** dronefreak/UAVid-2020

**🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo

Feedback, bug reports, and contributions are always welcome.

posted an update about 1 month ago

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Fair warning space/photograhy lovers: this is weirdly addictive, Just one date becomes thirty !

There's a spot on the internet that shows you the exact image nasa shared with the world on any day since 1995. Type your birthday and see what the universe was up to.

(Born before 1995? Ah — the cosmos wasn't posting yet. You're officially more vintage than the dataset. 😎 Try a big life date instead.) 😅

🔭 See yours → Hari5115/cosmic-moment
Or hit Surprise me and let the universe pick.

Post the image you got below 👇 — let's see whose day space showed off for. 🌠

Built on the open nasa apod dataset · public domain · not affiliated with nasa

Dataset: 📦 Hari5115/nasa-apod

#space #photography #astro #cosmic

Hariprasad Sundaresan PRO

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