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sequelbox 
posted an update 2 days ago
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3364
Multiple NEW RELEASES for Gemma 4 12B!

- Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps!
- Tachibana-Agent, trained only on code for dedicated, predictable deployment!
- Guardpoint, our structured medical reasoning model: medical diagnosis, management, knowledge, and understanding in structured, concise form!

GET OUR NEW MODELS:

ValiantLabs/gemma-4-12B-it-Esper4
sequelbox/gemma-4-12B-it-Tachibana-Agent
ValiantLabs/gemma-4-12B-it-Guardpoint

Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
sequelbox/Superpotion-DeepSeek-V3.2-Speciale

Esper 4 is also available for Qwen 3.6 27B: ValiantLabs/Qwen3.6-27B-Esper4

We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource

More to come soon!

go build stuff :)
allegra
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ProCreations 
posted an update 1 day ago
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2371
sad grug news:
I have burnt a lot of GPU credits already just making grug and it’s variants (I do not own a workstation yet) and grug 35b has a pretty bad issue but if I continue burning GPU credits to fix it then it will take even longer to get the workstation I am working for… so the fix may take a few days or never happen.

The error:
I tested it in opencode after benchmarks were good and after release (my mistake) and it had pretty bad repetition failure and simply didn’t work. so.. yea.
  • 3 replies
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danielhanchen 
posted an update 3 days ago
DavidAU 
posted an update about 11 hours ago
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1169
Qwen 3.6 27B Fine tune exceed 700 ARC-C for both 8 and 4 bit.

A new level of uncensored performance the puts this model squarely at "closed source" level of intelligence.

Model exceeds all critical benchmarks for both Qwen 3.6 27B AND Qwen 3.6 35B-A3B... and not by a little either.

Neo Imatrix MAX ggufs in both regular and MTP quants.

DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
salma-remyx 
posted an update 1 day ago
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2311
It's conference season, so you'll find an uptick in chatter around the research reproducibility crisis. Consider this a PSA on where the real challenges in working with research actually live.

After all, AI has made it way easier to release code and model artifacts alongside the preprints. And how many times do you really need to replicate the authors' exact configuration?

Downstream of that, as engineers evaluate candidate methods for improving THEIR systems, they rarely find a drop-in solution. More often, they're making tough tradeoffs in fidelity to the documented technique and the constraints of their deployment scenario.

They're swapping models or data indexing strategies. They have their own benchmarks to measure changes against. They're making principled reductions of a technique to respect some resource limit not considered in the source paper.

AI coding has made replication cheap when a paper provides starting point for your own experiments. But the work of adoption requires validation grounded in real-world outcomes.

So put these techniques to the test in your own system, and you'll understand a method's impact well before the survey paper drops in six months.

At Remyx AI, we're helping teams discover, implement, and validate what's next for their systems.

Get Outrider: https://github.com/remyxai/outrider
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Banaxi-Tech 
posted an update 3 days ago
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3745
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!
scthornton 
posted an update 4 days ago
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2613
SecureCode update: we went back and fact-checked our own security dataset and corrected what didn't hold up.

The original claim was "complete incident grounding, every example ties to a documented CVE." An adversarial re-audit found that it was overstated: many CVEs were misattributed, and many "incidents" were representative scenarios carrying invented statistics. So we fixed it.

- Grounding: re-verified every reference. Removed 802 misattributed CVEs on the web side, corrected or honestly relabeled the incident narratives, and confirmed the AI/ML conversation CVEs are real (EchoLeak CVE-2025-32711, EmailGPT CVE-2024-5184, and others).
- Fix-correctness: reviewed whether each "secure" example actually eliminates the vulnerability. Removed 28 that did not (a "secure" secret scanner whose entropy check always returned zero, an Angular example still using bypassSecurityTrustHtml, and more).
- Leakage: re-split so near-duplicates stay on one side. Test contamination went from 11.6% to zero.
- Viewer, schema, and metadata: rebuilt as parquet under a shared schema. All three viewers are live.
- Models: retrained the whole family on the corrected data so the fix reaches the weights, not just the cards. Now ten open models (3B to 26B), including two new Gemma 4 variants, refreshed locally on a DGX Spark GB10. The paper (arXiv:2512.18542) was revised to match.

Counts moved from 2,185 to 2,372 unified (web 1,625 + AI/ML 747). A slightly smaller, fully-checked dataset beats a larger one you have to take on faith. Full writeup and links in the article.

Datasets: scthornton/securecode, scthornton/securecode-web, scthornton/securecode-aiml

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wop 
posted an update about 2 hours ago
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# PixelModel v1

Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt.

Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG.

That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel.

## links
bench-labs
Blog post [read it here ⇗]( bench-labs/blog)
See us on the [Leaderboard ⇗]( FlameF0X/Tiny-T2I-Leaderboard)
Model card [here]( bench-labs/pixelmodel-v1)

## The catch
A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it.

# cherry on top 🍒
The model generates 600 images (cpu) in 5 (five) seconds.
Thats 5000 images in 24 seconds on cpu.
The model trained on cpu for just 30 minutes.
ajibawa-2023 
posted an update about 5 hours ago
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Technical-Architectures-Large
Dataset: ajibawa-2023/Technical-Architectures-Large

This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.

These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
DavidAU 
posted an update 23 days 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
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