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SeaWolf-AI 
posted an update 2 days ago
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5517
Instead of making the fly brain play games, we measured what it is for

Since the Drosophila connectome was released, people have had the fly brain doomscroll a feed, play Beat Saber, drive in GTA. Those demos show that the brain runs. We wanted to show what it is for.

So we gave it a looming object — one of the few things a fly brain is unambiguously built to detect — then deleted a single cell type and repeated the identical stimulus. Remove LC4, 126 cells out of 173,023, and the escape signal falls from 0.840 to 0.091. Eighty-nine percent of the danger signal is gone while the other 172,897 neurons run exactly as before.

Deleting neurons does not do this on its own, which is the whole point of the controls. LC11 is the same class and larger than LC4 — 143 cells and 9,940 outgoing connections against 126 and 7,846 — and removing every one of them changes the signal by 0.000000, to six decimal places. It has to be those 126.

No server and no GPU: a looming stimulus drives fewer than one percent of neurons above threshold, so the whole thing is 40 KB gzipped and runs in your browser.

Try deleting one yourself →
VIDraft/fruitfly-brain

The wiring is the measured connectome, but synaptic strength is a uniform count-based value and the dynamics are a firing-rate model of our choosing — a total-effect measurement of a model, not a recording from a fly. Male CNS connectome, FlyEM / HHMI Janelia with Google Research, Columbia and Harvard (2026), CC BY.
Datdanboi25 
posted an update 1 day ago
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2779
100 likes on the Open SLM Leaderboard 🎉

176 models, 54 orgs, 5 benchmarks, and a whole community of support!

Thanks to everyone who’s contributed models, reported issues, suggested benchmark improvements, or used the leaderboard to compare and evaluate small language models.

It’s been awesome watching the leaderboard grow into a broader community resource for transparent and reproducible SLM evaluation.

Thank you all, and more to come 🚀
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onekq 
posted an update 1 day ago
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4401
I turned off subagents in Claude Code. Am I a minority?
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RiverRider 
posted an update about 19 hours ago
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Black Window — a chat model in your browser tab, on your hardware. A memory that stays on the device that opened the page.

https://blackwindow.xyz

Open the site, pick a model (about 0.6B to 8B), hit Load. The weights run in that tab, on that computer. After they load, the network can drop. The context window is a working set, auto-sized to that device, up to ~32K tokens.

Behind the window is the Weave. Every file, picture, recording, link, lookup, and reply is embedded as it arrives. Drop in audio and it is transcribed. Drop in an image and it is described. A question pulls the nearest passages back as notes. A long document is walked once so later questions can use the whole file, not the first pages.

Nothing leaves that tab unless you turn on live lookup or connect a rented GPU box, and the chat says so each time. Prompts can go to the box. Files and the Weave stay in the tab.

Console on that page: bw.ask, bw.search, bw.digest, bw.notes. A local relay exposes /v1/chat/completions on localhost so other tools on the same computer can talk to the tab. The tab polls the relay. That is the boundary.

Not a server with a policy. Your hardware, a window, a Load button.

If on mobile add to home-screen for best performance. If you break it lmk. It can serve a few hundred of you at a time before I have to buy a real server.
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DedeProGames 
posted an update about 19 hours ago
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24
Please give a follow to
OrionLLM


We are conducting extensive research to build the best local models for agentic coding.
eaddario 
posted an update about 19 hours ago
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Experimental global target bits‑per‑weight quantization of **XHToken/Spark-X2.5-1.7B** and **XHToken/Spark-X2.5-4B**.

Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global 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, GPQA, MMLU, etc.) and methodology in the model's card.

eaddario/Spark-X2.5-1.7B-GGUF
eaddario/Spark-X2.5-4B-GGUF
tegridydev 
posted an update 4 days ago
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3670
What can you actually build with a cybersecurity dataset?

I've been updating a few of mine on Hugging Face, and they now cover some pretty different parts of the security workflow.

- open malsec has 1,104 defensive security scenarios across 20 subsets covering phishing, malware, scams, cloud security, API security, AI security and more

- opensec triage has 50,000 contextual alert examples, plus compact model and edge training sets for testing whether models classify from the evidence around an event

- infosec tool output has 1,004 examples across 19 tools for turning raw security output into evidence backed explanations, limitations and defensive next steps

You could use them for:

* phishing and scam explainers
* alert triage tools
* SOC assistants
* scanner output explainers
* analyst training
* model comparisons
* grounding and hallucination tests
* small specialised security models
* edge and local model experiments

Or combine them into something like:

scenario → evidence → triage → explanation → next action

You also don't need to train anything straight away.

Grab a few examples, run them through whatever model you already use and see where it gets confused :)

Datasets:

tegridydev/open-malsec

tegridydev/opensec-triage

tegridydev/infosec-tool-output
FredyRivera-dev 
posted an update about 14 hours ago
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I ported Evo 2 inference to plain PyTorch and Transformers so the official checkpoints load through AutoModelForCausalLM with no Vortex, Transformer Engine, or custom kernels required.

What is included:

- GitHub repo with the port, a Vortex to HF converter, and a gene completion repro script: https://github.com/Aquiles-ai/Evo2-transformers

- Aquiles-ai/Evo2-1B-Base: the 1B base checkpoint (8k context) in Transformers format: Aquiles-ai/Evo2-1B-Base

- Aquiles-ai/Evo2-7B: the 7B checkpoint (1M context) in Transformers format: Aquiles-ai/Evo2-7B

Both repos vendor the modeling files, so loading needs trust_remote_code=True. The tokenizer matches the original byte level behavior, including the vortex_tokenize and vortex_detokenize helpers.

One honest note: this port is less efficient than the original Vortex stack. It has no FlashAttention, no FP8 path, and no recurrent state decoding, so generation recomputes the prefix and long contexts are slow. For large scale or 1M context work, use the original implementation or NVIDIA NIM.

All credit for the model, data, and research goes to the Evo 2 team at Arc Institute and collaborators. Original project: https://github.com/ArcInstitute/evo2. Paper: https://www.nature.com/articles/s41586-026-10176-5

Feedback and corrections are welcome as GitHub issues on the port repo.
ManniX-ITA 
posted an update 4 days ago
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527
🚀 JackOD-9B-Coder — a 9B merge built to FINISH agentic coding tasks. Four-way omnimerge_v2 over Qwen3.5-9B: Jack = Qwopus3.5-9B-Coder (0.30), O = Ornith-1.5-9B (0.15), D = DeltaCoder (0.55). MTP head kept.

📊 Q6_K + imatrix, llama.cpp, greedy, lcb_v6_55 — merge / base / DeltaCoder / Qwopus / Ornith:
⚡ LiveCodeBench v6 (55 hard) — 0.7818 / 0.7273 / 0.6364 / 0.6000 / 0.5818
✅ HumanEval — 0.8841 / 0.8902 / 0.9146 / 0.8537 / 0.7805
✅ HumanEval+ — 0.8232 / 0.8049 / 0.8232 / 0.7988 / 0.7073
🤝 MultiPL-E — 0.8033 / 0.8200 / 0.8000 / 0.8200 / 0.7267
📋 IFEval — 0.9100 / 0.9300 / 0.9200 / 0.8800 / 0.8200

🎯 LCB beats every source AND the base: +5.45pp over the base, +14.54pp over DeltaCoder, its heaviest.

🛑 And why. Same 55 problems, same cap, generations that NEVER terminated: DeltaCoder 25/55 · base 18/55 · Qwopus 8/55 · Ornith 2/55 · JackOD 1/55. That split is the thesis: DeltaCoder is the cohort's best coder and worst at stopping, Ornith the weakest and best at stopping. The merge takes BOTH.

🤝 tool-eval-bench hardmode, 5 seeds: JackOD 144.4 ±4.7, second behind Ornith 145.6 ±4.2, above base 142.0 — all CIs overlap. But Autonomous Planning: JackOD 5.2/6, best of five, Ornith WORST at 2.8/6. Ornith stops reliably but plans worst — it stops too early. The merge does both.

🔧 Serving: temp 0.6 / top_p 0.95 / top_k 20 + presence_penalty 1.5 — the penalty stops it re-treading a tool call. Tool calling on llama.cpp needs --jinja.

🧪 Initial impression, limited testing: fixed a cline-harness task in 535s; A3B models want 1.5-4h at <50% success.

📦 25 GGUF tiers, every K/IQ imatrix-built incl Q6_K, plus a ContribDynamic ladder (per-tensor maps from our imatrix, Unsloth-UD style).

🙏 danielcherubini/Qwen3.5-DeltaCoder-9B · ornith-ai/Ornith-1.5-9B

🔗 ManniX-ITA/JackOD-9B-Coder
🔗 ManniX-ITA/JackOD-9B-Coder-MTP-GGUF
🔗 https://ollama.com/mannix/JackOD-9B-Coder

Banaxi-Tech 
posted an update 4 days ago
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3011
We're releasing the BananaMind SLM Leaderboard!
It offers a easier look at which models are actually good for your specific needs.
Its primary metric, Intelligence index is a composite of BananaMind Base Bench, PIQA, Hellaswag, ARC Easy and Arithmark 3.
It also allows you to see specific categories like Commonsense on a model.


Check it out at BananaMind/BananaMind-SLM-Leaderboard

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