🏗️ Building on HF

Hariprasad Sundaresan PRO

Hari5115

8 12

AI & ML interests

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

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posted an update about 14 hours ago

Did you know the sun regularly fires off storms strong enough to knock out satellites and GPS? And those same storms are what light up the Northern Lights. Pulled together a dataset of space weather ( solar flares, solar storms, and blasts of radiation from the Sun, tracked over the years ) Link : https://huggingface.co/datasets/Hari5115/nasa-donki Great for space enthusiasts, aurora chasers, or anyone who likes playing with data, map when the big storms hit, see how active the Sun's been, or even try predicting the next flare. Spot a wild one? Tell me. Open for anyone to use , data's from NASA's DONKI.

updated a collection about 14 hours ago

NASA Open Data

updated a collection about 14 hours ago

NASA Open Data

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posted an update about 14 hours ago

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Did you know the sun regularly fires off storms strong enough to knock out satellites and GPS? And those same storms are what light up the Northern Lights.

Pulled together a dataset of space weather ( solar flares, solar storms, and blasts of radiation from the Sun, tracked over the years )

Link : Hari5115/nasa-donki

Great for space enthusiasts, aurora chasers, or anyone who likes playing with data, map when the big storms hit, see how active the Sun's been, or even try predicting the next flare. Spot a wild one? Tell me.

Open for anyone to use , data's from NASA's DONKI.

updated a collection about 14 hours ago

NASA Open Data

Collection

Five clean, open datasets from NASA's public APIs: astronomy images, asteroids, exoplanets, space weather, and Earth events. Public domain. • 6 items • Updated about 14 hours ago

updated a dataset about 14 hours ago

Hari5115/nasa-eonet

Viewer • Updated about 14 hours ago • 60.5k

published a dataset about 14 hours ago

Hari5115/nasa-eonet

Viewer • Updated about 14 hours ago • 60.5k

updated a dataset about 14 hours ago

Hari5115/nasa-donki

Viewer • Updated about 14 hours ago • 9.29k

published a dataset about 14 hours ago

Hari5115/nasa-donki

Viewer • Updated about 14 hours ago • 9.29k

reacted to mihailgribov's post with 👍 1 day ago

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100

Will your AI agent tell you it was attacked?

We took the same agent from our earlier experiment and added one thing: a twentieth tool, escalate_security_incident.

The system prompt said nothing about attacks or when to use it. We then ran the same 395 injected emails through nine agentic models.

Alarm rates ranged from 49% to zero.

The unexpected result came from the newest model in the test, gpt-6-astra.

Astra did not follow a single injected payment instruction. But it did not report a single one either. On clean and injected emails alike, it simply read the email, logged the subject, and finished.

That is a useful distinction: resisting an attack and recognizing it as a security event are not the same capability.

A model can be perfectly resistant in this test and still leave you with no evidence that anyone attacked it.

Full experiment and results:
https://huggingface.co/blog/mihailgribov/will-the-agent-tell-you-it-was-attacked

Quadrat-IPI dataset:
mihailgribov/quadrat-ipi

Run your own model:
https://github.com/mihail-gribov/quadrat-ipi-model-eval

#prompt-injection #indirect-prompt-injection #agentic-ai #llm-security #ai-agents

reacted to salma-remyx's post with 👍 about 1 month ago

Post

2725

If you can’t explain why your AI-generated contribution belongs in the repo, don’t put it in a maintainer’s queue 🙅🏻‍♀️

Before asking for review, you should be able to answer:

1. What project need does it address?
2. Where does it fit, and does it duplicate existing work?
3. What evidence shows it works, and will you own it through review?

If you can’t answer those, you haven’t saved anyone time. You’ve passed the buck to the maintainer.

Outrider has made us better contributors by doing more of this work before upstream review by
* reading contribution rules, accepted PRs, and open issues
* finding needs and integration points
* drafting the code, tests, and context.

We still decide what deserves to go upstream, verify the claims, coordinate with maintainers and contributors, and stay involved through review.

On huggingface/peft, only 4 of 20 Outrider runs opened draft PRs. Two contributions have now merged:

✅ Riemannian-preconditioned LoRA: https://github.com/huggingface/peft/pull/3382
✅ Super-Tuning: https://github.com/huggingface/peft/pull/3518

We have more contributions in review and far more ideas were filtered out before they reached a maintainer.

Full case study: https://remyx.ai/case-study
Outrider: https://github.com/remyxai/outrider

11 replies

reacted to Hoglet-33's post with 🔥 about 1 month ago

Post

3080

We are announcing the first generation of the Pebble model family!

These are the models we are releasing:

- Pebble 10M
- Pebble 25M
- Pebble 50M

Each model will use a Mamba-Transformer 3:1 hybrid architecture and will be pretrained on 25 billion tokens before IFT and SFT.

Depending on development time and resources, we may also release:

- Pebble 5M
- Pebble 75M
- Pebble 1M (possibly)

We hope you're excited and enjoy the models!

Follow for more:
@Hoglet-33

basically-ai

7 replies

reacted to GoktugD's post with ❤️ about 1 month ago

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2383

🇹🇷 We trained a 1B OCR model specifically for Turkish enterprise documents.

**Werea-DocOCR-1B v2**

The result surprised us:

LightOnOCR-2 base → **64.2% CER**
Werea-DocOCR v1 → **~8.1% CER**
Werea-DocOCR v2 → **0.15% CER** 🚀

Evaluated on a held-out 72-page test set across 12 Turkish document types and 3 different capture conditions.

📄 12 Turkish enterprise document types
🧪 12,960 synthetic training pages
📱 Digital + scanned + phone photos
📊 Tables → structured Markdown
⚙️ Full-parameter fine-tuning
🖥️ Trained on a single RTX 3090

It handles:

• e-Invoices
• rental contracts
• bank receipts
• payroll documents
• insurance policies
• vehicle documents
• official correspondence
• trade registry documents
• SGK-style tables
• and more.

**Model 🤗**
Werea-co/Werea-DocOCR-1B

**Dataset 📚**
Werea-co/werea-tr-doc-ocr-enterprise-v2

**Werea 🇹🇷**

Werea-co

We're building open AI models from Türkiye.

This is just the beginning.

#HuggingFace #OCR #DocumentAI #TurkishAI #OpenSourceAI #ComputerVision

upvoted 2 articles about 1 month ago

Article

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

nvidia

•

Jul 27

• 77

Article

State of Open Models: Summer 2026 Observations

AdinaY, multimodalart, irenesolaiman

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Aug 14

• 216

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

Post

4216

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.

36 replies

reacted to Abhisek987's post with 👍 about 2 months ago

Post

2649

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

liked a dataset about 2 months ago

Abhisek987/deplab-dependency-compatibility

Viewer • Updated Aug 1 • 24.9k • 47 • 3

Hariprasad Sundaresan PRO

AI & ML interests

Recent Activity

Organizations

Hari5115's activity

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

State of Open Models: Summer 2026 Observations