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John6666

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reacted to sequelbox's post with 👀 3 days ago

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156

Esper 4 is coming soon! Check out the EARLY SNEAK PREVIEW of our agentic DevOps/architecture dataset, Titanium 4:

- Questions prioritize real-world, challenging agentic coding tasks in DevOps and architecture across a variety of programming languages and topics.
- Areas of focus include IaC, cloud architecture, incident response, configuration and cost optimization, security and administration, containerization and virtualization, logging and monitoring, shell scripting, Linux administration, platform engineering, reliability, APIs and databases, real-world deployments and version control, performance engineering, edge computing, and more!
- Synthetic prompts utilize a variety of personas, experience levels, and styles of communication to maximize real-world flexibility and usability on DevOps and architecture tasks.

Get it now: sequelbox/Titanium4-DeepSeek-V4-Pro-PREVIEW

Titanium 4 is the core of the coming-very-soon Esper 4, and whatever you can build! :) we're also working on a new experimental reasoning release!

build with open source, for an AI future that looks more like what The Internet Was and less like what The Internet Is. we can still turn this around!

follow our central page for updates, upcoming releases, and to offer your support: sequelbox/SupportOpenSource

for love and pride,
allegra

reacted to hypothetical's post with 🚀 3 days ago

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The smallest and the highest quality in the world Gemma4 E2B and E4B models! 7x compression! From 9.3GB -> 1.4GB!

TheStageAI/gemma-4-E2B-it
TheStageAI/gemma-4-E4B-it

2 replies

reacted to evalstate's post with 🤗🚀 3 days ago

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Hugging Face MCP Server v0.3.17
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

SEP-2640 "Skills Over MCP" support added (early access)

2 replies

reacted to RiverRider's post with 🔥 3 days ago

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This is not the end of words. It is the end of pretending their meanings are determined.

Meaning Forks. SRT detects it.

Paste any text to identify contested terms

RiverRider/srt-introspect

Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer

RiverRider/srt-introspect

Repository

https://github.com/space-bacon/SRT

Paper

https://github.com/space-bacon/SRT/blob/main/paper_nla.md

Explainer

https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md

reacted to sergiopaniego's post with 👍 3 days ago

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most multi-turn RL loops have a silent bug: you decode the model's output to detect tool calls, then re-tokenize the conversation for the next turn. BPE isn't invertible, so decode then re-encode can land on different ids. gradient ends up on tokens the model never sampled. no crash, just quietly wrong math and broken training

@qgallouedec wrote a super educational blog on MITO (message-in, token-out) vs TITO (token-in, token-out) and how you might fix the problem above

go read it 🤓

https://qgallouedec-tito.hf.space/

reacted to ovi054's post with 🔥 3 days ago

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Qwen Image Edit 2511 Fast + LoRA ⚡

ovi054/Qwen-Image-Edit-2511-LoRA

QIE-2511 is an image editing model with integrated LoRA capabilities. You can add any custom LoRA to generate and edit images within this Space.

👉 Try it now: ovi054/Qwen-Image-Edit-2511-LoRA

reacted to kanaria007's post with ❤️ 3 days ago

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✅ Article highlight: *Deployment & Rollback Governance for Learning Worlds* (art-60-169, v0.1)

TL;DR:
This article argues that deployment is the highest-risk moment in a learning world.

Training produces a new policy. Deployment turns that policy into an institution inside the world. So rollout cannot be treated like a casual model swap. It needs deploy-gate contracts, canaries, phased rollout, kill-switches, rollback receipts, and explicit non-interference rules that stop “better learning” from silently rewriting world reality.

Read:
kanaria007/agi-structural-intelligence-protocols

Why it matters:
• treats deployment as governed change, not routine ops
• prevents silent reality drift when a newly trained policy changes world outcomes
• binds rollout to safety envelopes, evaluation validity, performance SLOs, and canon boundaries
• makes rollback and emergency stop part of the formal operating contract

What’s inside:
• a *model deploy gate contract* that defines when a learned policy may enter the world
• canary and phased rollout as explicit governed stages
• kill-switch and rollback receipts for emergency containment
• non-interference audits so training and deployment do not rewrite canon or governance outcomes
• appeal and publication boundaries for claims like “we deployed safely” or “we rolled back successfully”

Key idea:
Do not say:

*“we trained a better model, so we deployed it.”*

Say:

*“this policy entered the world under this deploy gate, this rollout stage, these envelope and SLO checks, these rollback guarantees, and these receipts.”*

That is how deployment becomes governance with receipts.

reacted to AbstractPhil's post with 👀 3 days ago

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104

The first large scale distillation is coming using the geolip-aleph-void architecture as the mathematical aleph procrustes geofractal addressed language latent.

In short, a single geometric patchwork vocabulary chunk. Which ironically needs chunking to properly prepare.

The address structure I have been meticulously refining is about to show it's genuine distillation muscle.

This is heavily due to the discovery and refinement of a specific logit I've named an aleph logit. This logit is baked clean into the architecture with the void-based codebook, and is available for review https://github.com/AbstractEyes/geolip-svae/blob/main/geolip_svae/aleph_model.py

This model provides solid MSE, recon, cosine sim, and many other elements directly aligned to the SVD and H2 procrustes paradigm. Prelims are not smart, but the scaling principal is perfectly attuned to scale.

This invention will allow for direct internalized tokenization and utilization of compressed information, entirely internally within the models latent structure. This allows direct control capabilities baked into the model itself, which requires a few robustness tests to solidify the full structure. The first validation tests run clean, so it will work when correctly aligned.

In short, the first step towards the geometric encoder system that will work with all tested data types.

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reacted to BibbyResearch's post with 😎 3 days ago

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Breaking 🚨 - New Viral Dataset Alert

World's largest Profanity Dataset with categorized offensive, abusive, hateful, sexual, and toxic terms across multiple languages. Built for content moderation, NLP research, AI safety, and text classification. Featuring 82k rows of profanity across 715 languages and 459 countries.

BibbyResearch/ProfanityBench

Repo - https://github.com/NileshArnaiya/profanitybench
Website - https://profanity-bench.vercel.app/

reacted to evijit's post with 😎 3 days ago

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Weekend mini project! Since commentary on AI is inherently interdisciplinary, we connected the observations in the Pope's encyclical with decades of scholarship in Responsible AI and Ethics research and created an interactive space with these annotations!

Work with @IJ-Reynolds , @yjernite , and @meg

Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!

society-ethics/annotated-encyclical

reacted to juiceb0xc0de's post with 😎 3 days ago

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😅 You ever fumble on a project? Please someone tell me I'm not alone. I fumbled at step one and remained oblivious for the remainder of the project. Funny story, I was under the assumption that Qwen/Qwen3-8B was the base model that the paired with the Qwen SAE released by Alibaba. I didn't realize there was a Qwen3-8b-Base model until after the 12 hours of independent mapping techniques I had applied to the model that was missing the -Base suffix. 🤗 My bad, I'm just a bartender. I should not be unsupervised.

Not all is lost however. The outcome was a very in depth neural network atlas complete with its own SQLite queryable database for the Qwen3-8B model I can now share with you all. The data base combines these methods for a full in depth dive:

- Neuron Taxonomy
- Category Separation Scoring
- Co-activation Analysis
- Per-Head Decomposition
- Component Comparison
- Attribution Patching
- Sparse Non-negative Matrix Factorization
- NeuronLens
- DAS SVD rotation
- Cross-layer Coherence
- SQLite database

So if you've ever wondered where a specific behaviour or ability lives in the hidden dimensions of Qwen-8B or perhaps wanted to make informed quantization decisions please enjoy the fruits of my ill-informed labour lol. 😂

juiceb0xc0de/qwen3-8b-atlas
Qwen/Qwen3-8B

reacted to FlameF0X's post with 🔥 3 days ago

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MiniMax-M3 coming soon.
https://github.com/MiniMax-AI/MiniMax-M3

reacted to sergiopaniego's post with ❤️ 3 days ago

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new banger blog alert 🚨

@ariG23498 is starting a blog series about profiling in pytorch and part 1 just dropped

takes you from the simplest scenario to actually knowing what your gpu is doing. if you have never opened a profiler trace this is where you start

covers torch.profiler from scratch. reading tables and traces, overhead bound vs compute bound, the full dispatch chain from python to gpu kernels, and what torch.compile is actually fusing under the hood

find it here: https://huggingface.co/blog/torch-profiler

1 reply

reacted to AINovice2005's post with 🤗 3 days ago

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The hf article detailing the integration between hf datasets and dagster is live 🤗 : https://huggingface.co/blog/AINovice2005/dagster-hf-datasets

reacted to lbourdois's post with 🤗 3 days ago

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New blog post!
An introduction to a little-known but highly effective model reduction method: 𝗧𝗿𝗶𝗺𝗺𝗶𝗻𝗴✂️
We show how to reduce model size (we went up to 87.24% reduction) while preserving its performance.

We applied this technique to 16 different model families across several modalities to illustrate that it works on any architecture (as long as the embedding layer is the last one of the model) and on any modality involving text.
From these 16 families, we generated over 𝟱,𝟱𝟬𝟬 𝗺𝗼𝗻𝗼𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝟭𝟮𝟰 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 🌍

Key takeaways from our experiments:
1️⃣ Trimming does not require a GPU. Our models were obtained on a CPU.
2️⃣ This method scales up to at least 4B parameters (we did not test beyond that).
3️⃣ Trimmed model is smaller than the original while preserving its performance. If you observe a slight performance drop, just fine-tuned to recover or even surpass the original performance.
4️⃣ For an equivalent compute budget, it is better to trim then fine-tune rather than fine-tuning the original model. Since the model is smaller, you can run more epochs/show more data and get in fine a better model than the original.
5️⃣ Trimming is a competitive alternative to distillation and quantization. E.g. we obtained our alternative to DistilBERT in 9 minutes on CPU vs. 90 hours of GPU for the latter.
6️⃣ Trimming could generate reasoning traces in the language of the trimmed model. This could be an alternative to generating traces in English and then translating them into the desired language.

And many other things (such as how much data are needed, the impact of the database used, the order in which it should be done, etc.) are available in the blogpost!

Blogpost: https://huggingface.co/blog/lbourdois/introduction-to-trimming
Models: alphaedge-ai/Trimming_models_search

4 replies

reacted to RakshitAralimatti's post with 🚀 3 days ago

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Reading engineering and research blogs from OpenAI, Anthropic, DeepMind, Meta and others has genuinely leveled up my understanding of AI systems and helped me in my day-to-day work. But keeping track of 20+ sites manually is a pain.

So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.

One click. ~30 seconds. Everything in one place.

🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent

1 reply

reacted to ovi054's post with 🔥 3 days ago

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Color Grading Transfer⚡

ovi054/Color-Grade-Transfer-Qwen-Image

What if you could steal color grade from your favorite films or any still image and apply it to your own content. And no, you don't need to be a professional colorist.

Input 1: Source Image - Content to be preserve
Input 2: Reference Image - Any still from films
Output: Color graded output image

👉 Try it now: ovi054/Color-Grade-Transfer-Qwen-Image

reacted to witcheer's post with 🚀 3 days ago

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new dataset: which local LLM best *drives an agent*? benchmarked 4 models for pairing with Hermes Agent (@NousResearch ) - a CodeAct agent that writes python to call its tools. RTX 5090, llama.cpp. two phases, hybrid:

>>> phase A (synthetic): scored 4 axes — code-as-action, long-context, instruction-following under Hermes' real ~3.5K-token prompt, multi-step loops. top was a near-tie (within noise): an 18B frankenmerge (Qwopus) edged Qwen3.6-27B, and Hermes' own 36B came LAST.

>>> phase B (real harness): installed Hermes, ran the top 3 through 14 multi-step tasks x3 repeats. the tie broke — and an efficiency gap appeared:

Qwen3.6-27B    100%   | 3.0 turns | 364 tok
Qwopus-18B     85.7%  | 3.6 turns | 870 tok
Nemotron-30B   85.7%  | 4.4 turns | 1334 tok

Qwen is perfect AND 2.4-3.7x more token-efficient — something a synthetic test can't see (only the real agent loop can). verdict: Qwen3.6-27B for local Hermes.

dataset: witcheer/hermes-pairing-bench
collection: witcheer/rtx-5090-benchmark-rig-6a17e365b534abb474250e11

reacted to mahwizzzz's post with 🔥 3 days ago

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Released lafzyn , built over Qwen, an Urdu language model that converts Urdu text into IPA phonetic transcription, with GGUF builds for local inference.

Release contents:
- mahwizzzz/lafzyn: full weights
- mahwizzzz/lafzyn-gguf: quantized builds

Try it out 🤗
Demo: https://huggingface.co/spaces/mahwizzzz/Lafzyn

John Smith PRO

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