unmodeled-tyler (Tyler Williams)

reacted to telcom's post with 🔥 about 3 hours ago

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59

In https://deegitals.com
you can find a workflow that suits your use case.
Please share your feedback, it is free to start testing.

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reacted to Sri-Vigneshwar-DJ's post with 🧠 about 3 hours ago

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The recent update to Meta's ad algorithm is very difficult to crack, and even the latest models struggle to keep up with it. To address this, we've created a small experimental dataset for fine-tuning models to better tackle Meta's Andromeda algorithm: Sri-Vigneshwar-DJ/hawky-ai-andromeda-dataset

reacted to mahimairaja's post with 🚀 1 day ago

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Happy New Years 2026!

For next 365 days I will be commit to work on:

- Document AI and OCR Automations
- Voice Agents
- Long Running Tasks - Durable Agents

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posted an update 1 day ago

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Happy New Year, Hugging Face!

It's been a crazy year for me! This year I launched VANTA Research as a solo operator and managed to push out 14 original open source finetunes and 5 datasets in the span of about 4 months, completely on my own.

The reception has been much higher than I ever anticipated and sincerely appreciate everyone that's checked out my work thus far.

The good news is, I'm just getting started! In 2026 you can expect even more original models from VANTA Research, more open source datasets, and maybe some other cool things as well? 👀

2026 is gonna be big for AI in general, and I can't wait to experience it with all of you!

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reacted to mike-ravkine's post with 🔥 1 day ago

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Happy 2026 everyone!

I've been busy working on some new ranking/position methodologies and excited to start sharing some results.

Plot legends:

- X = truncation rate (low = good)
- ? = confusion rate (low = good)
- blue bars = average completion tokens (low = good)
- black diamonds = CI-banded performance (high = good)
- cluster squares = models inside this group are equivalent

openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon.

Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - ByteDance-Seed/Seed-OSS-36B-Instruct

This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length).

Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.

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reacted to dhruv3006's post with ❤️ 2 days ago

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Git is powerful, but it’s also one of the biggest sources of developer mistakes.

What is Git GUI, and how does it help here ?

Git GUI makes version control visual, predictable, and easier to reason about especially when things go wrong.

That’s exactly why we built Git GUI in Voiden.

Instead of relying on memorized commands, Voiden lets you see what Git is doing before it does it.

What Voiden’s Git GUI helps developers do
• View exact file and line-level changes before committing
• Stage only intended changes (no accidental commits)
• Clearly distinguish staged vs unstaged files
• Inspect visual diffs with full context
• Understand branches, commit history, and repo state instantly

When Git behavior is hidden, errors increase. Voiden’s Git GUI doesn’t abstract Git away, it explains Git.

Whether you’re new to Git or an experienced developer who prefers clarity, this is Git you can reason about.

Version control should feel safe, not stressful.

What Git pain points slow you down today?

Try out Git GUI in beta : https://voiden.md ( Now in Linux and Mac )

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reacted to MohamedRashad's post with ❤️ 2 days ago

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I have update my https://huggingface.co/collections/MohamedRashad/arabic-speech-datasets
with new datasets, making the full audio data more than 3000 hours of good arabic speech.

Feel Free to use it in your new innovations, And happy new year!

reacted to their post with 🚀 2 days ago

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NEW MODEL ALERT: vanta-research/atom-27b

Atom-27B has arrived! This model is the largest open-weight model so far from VANTA Research, and is our 4th model in Project Atom - an effort to scale our collaborative Atom persona from 4B-400B+

Atom-27B is based on Google's Gemma 3 27B architecture, and embodies the familiar friendly, warm, and curious persona that appeared in previous releases.

Atom is designed to think WITH you, not FOR you - marking VANTA Research's commitment to building frontier collaborative models.

Check it out!

posted an update 2 days ago

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NEW MODEL ALERT: vanta-research/atom-27b

Atom-27B has arrived! This model is the largest open-weight model so far from VANTA Research, and is our 4th model in Project Atom - an effort to scale our collaborative Atom persona from 4B-400B+

Atom-27B is based on Google's Gemma 3 27B architecture, and embodies the familiar friendly, warm, and curious persona that appeared in previous releases.

Atom is designed to think WITH you, not FOR you - marking VANTA Research's commitment to building frontier collaborative models.

Check it out!

reacted to eaddario's post with 🤗 4 days ago

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Experimental global target bits‑per‑weight quantization of allenai/Olmo-3-7B-Instruct and allenai/Olmo-3-7B-Think

Unlike standard llama.cpp quantizations that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global file size target.

Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.

Full benchmarks (PPL, KLD, ARC, MMLU, etc.) and methodology in the models' cards

eaddario/Olmo-3-7B-Instruct-GGUF
eaddario/Olmo-3-7B-Think-GGUF

reacted to dejanseo's post with 🚀 4 days ago

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Testing a search query volume prediction model.
dejanseo/query-volume-prediction

reacted to Sri-Vigneshwar-DJ's post with 🔥 4 days ago

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2021

I recently encountered a context graph issue in specific verticals and wrote about the importance of the context layer in performance marketing.

https://huggingface.co/blog/Sri-Vigneshwar-DJ/the-context-layer-performance-marketing-needs

replied to their post 7 days ago

Hey there! Feel free to send me an email (in my bio) and I'd be happy to provide some guidance.

reacted to their post with 🚀 13 days ago

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NEW MODEL: vanta-research/scout-8b

VANTA Research is excited to share our new model, Scout-8B! This iteration of Scout is based on the RNJ-1 Instruct architecture from Essential AI, and not only improves but expands on the capabilities from vanta-research/scout-4b

Scout is specifically designed for:

Tactical Intelligence Analysis
- Systematic problem decomposition
- Structured reconnaissance approach
- Data-driven assessment methodology

Operational Planning
- Multi-phase operation planning
- Risk assessment and mitigation
- Resource allocation guidance

Technical Assessment
- Architecture evaluation and analysis
- Performance optimization recommendations
- Security perimeter assessment

This model is great for anyone that works in security, IT, DevOps, or anyone looking for a unique, but functional AI collaborator. Check it out!

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posted an update 14 days ago

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NEW MODEL: vanta-research/scout-8b

VANTA Research is excited to share our new model, Scout-8B! This iteration of Scout is based on the RNJ-1 Instruct architecture from Essential AI, and not only improves but expands on the capabilities from vanta-research/scout-4b

Scout is specifically designed for:

Tactical Intelligence Analysis
- Systematic problem decomposition
- Structured reconnaissance approach
- Data-driven assessment methodology

Operational Planning
- Multi-phase operation planning
- Risk assessment and mitigation
- Resource allocation guidance

Technical Assessment
- Architecture evaluation and analysis
- Performance optimization recommendations
- Security perimeter assessment

This model is great for anyone that works in security, IT, DevOps, or anyone looking for a unique, but functional AI collaborator. Check it out!

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reacted to daavoo's post with 🔥 14 days ago

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2025: The Year of Agents.
2026: The Year of Local Agents?

Relying on cloud-hosted LLMs is often overkill. While frontier models still lead in complex coding, local models are now more than capable of handling many agentic workflows—with zero latency and total privacy.

To help bridge the gap between local inference and usable agents, I’m releasing agent.cpp: https://github.com/mozilla-ai/agent.cpp

It provides minimal, high-performance building blocks for agents in C++, built directly around the awesome llama.cpp ecosystem.
Stop sending your data to a remote API. Start building and running agents on your own hardware.

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reacted to branikita's post with 🔥 14 days ago

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Next week, we will release full documentation for the SO ARM 101 with a parallel gripper, featuring leader and follower arms and support for widely used stereo cameras.

reacted to etemiz's post with 🧠 14 days ago

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looks like the best way to incorporate truth in AI is to use some kind of RAG.
what are the state of the art ways to consume knowledge graphs?
and what is the best way to build a knowledge graph using AI?

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reacted to branikita's post with 🚀 15 days ago

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Update: Our engineer Alan has received a batch of components for the manipulator assemblies — including clamps and metal bracket parts. Prototype assembly is planned for the beginning of next year.