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posted an update about 15 hours ago

Post

917

Canceling My Pro Subscription

I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.

Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?

I paid for openness. Not for a corporate takeover.

🤗 was about community.

4 replies

DavidAU

posted an update about 9 hours ago

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965

Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored

This is the first fine tune to exceed 730 "arc-c" ("735": 144 pts higher than Qwen 3.8 27B) AND 880 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and over 718 arc-c in 4 bit.

This version is called TURBO because it drastically reduces thinking tokens (by 1/2 to as high as 1/10), yet maintains output detail and quality.

In other words while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more.

This repo contains both "regular" and "MTP" Neo-CODER MAX DI-MATRIX (duel imatrix) GGUF quants.

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF

PS: There are 29 additional quant repos as of this writing too, as well NVFP4 and many more as well.

This is one of 10+ Qwen 3.8 27B at or above ARC-C of 717 (all 10 exceed all core benchmarks of Qwen 3.8, 3.6 and 3.5 27B and 35B-A3B versions) - you can see the complete project and some of the training here :

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU

2 replies

Hoglet-33

posted an update 1 day ago

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2361

Pebble-25M and Pebble-25M-Chat are out now!

We’re excited to release Pebble-25M and Pebble-25M-Chat!

Both models use our 3:1 Mamba2/Transformer hybrid architecture and were pretrained on 25B tokens. Pebble-25M-Chat was then further fine-tuned on an additional 250M tokens from smol-smoltalk, following the same approach used for the Pebble-10M models.

We hope you enjoy experimenting with them!

Pebble-50M is coming in a few days.

Models
Pebble-25M: basically-ai/Pebble-25M
Pebble-25M-Chat: basically-ai/Pebble-25M-Chat
Pebble-10M GGUFs

In case you missed it, our friend @ContextReq made GGUF versions of the Pebble-10M models:

ContextReq/Pebble-10M-GGUF
ContextReq/Pebble-10M-Chat-GGUF

Follow us if you don’t want to miss future releases and updates!

@Hoglet-33

basically-ai

basically-experimental

2 replies

Bc-AI

posted an update 2 days ago

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3654

Hello everyone! A small update on things:

1. G1 series status. G1 is training nicely, and the loss is dropping nicely. The metrics are publicly available and i made a small space you can use to see the nice graphs: hugging-science/Loss-Plot-G1-Large
G1-MINI is a lot slower in converging for reasons unknown yet, but we are investigating it.

2. I have built a small chat app for open SLMs here: ml-intern-explorers/slm-arena
Feel free to add your models in a pull request!

That's all for now, early G1 versions will be available for beta testers soon. Thanks to our beta testers: @guardamarcos @Timmy6767 @MUK-IS-GOAT @smilyai-large-team @Sbui503 @Banaxi-Tech @Bc-AI @atom77777 @Harley-ml @Datdanboi25 @Fishtiks @smartdigitalnetworks @vovaRL @EmetTheGolum @juiceb0xc0de @ProCreations

Banaxi-Tech

posted an update 2 days ago

Post

2820

We're going to release our BananaMind 2.1 models very soon!
We're also announcing 2 new models.

All of our models we will train are:
BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens.
BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens.
BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens.
BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens.
BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet.
We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.

Follow us:

BananaMind
@Banaxi-Tech
@vovaRL
@DedeProGames

bananamind-research-community

5 replies

SeaWolf-AI

posted an update 2 days ago

Post

3964

Introducing the Global LLM Download Leaderboard 🌍

Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.

So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.

👉 VIDraft/global-llm-leaderboard

Why a 30-day window changes what you see

A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.

What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.

Benchmarks measure what a model can do. Downloads measure what people choose to use.

5 replies

juiceb0xc0de

posted an update 2 days ago

Post

3358

Hey I've updated my Hugging Face text generation model search space B-Sides. I always wanted more from HF's model search, so I built one.

I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.

So far over 428,440 text generation models are catalogued with more coming weekly.

On deck: Docker images

https://huggingface.co/spaces/juiceb0xc0de/b-sides

4 replies

KlondikeDev

posted an update 3 days ago

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2340

Boris-1.7-D60M-n30M out NOW!

opencerebral/Boris-1.7-D60M-n30M

Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture.

Then, Boris-2 will begin training!

SoulInPsyAbstract

posted an update about 16 hours ago

Post

738

Ran the real safety-gate eval on a merged specialist last night — 1200 generations, n=10 repeated sampling, 6 adversarial categories including direct pressure to keep going after a hard stop fires. The harness printed 1189/1200.

I don't trust a harness number until I've read the failures. All 11 turned out to be correct, categorical STOPs — the judge itself was misreading its own escalation marker, "to confirm", as an attempt to keep going when it was actually just remediation-plan language ("re-run the scan to confirm the fix"). Same marker, three distinct false-negative causes: remediation-context phrasing my earlier fix never anticipated, a present-participle gap in a quote-detection list ("asking me to" vs "asks me to"), and a negation window 11 characters too short for one genuinely negated sentence. Fixed all three, re-scored the same 1200 samples: 0 flipped the wrong way, 11 flipped to correct. 1200/1200.

Then I asked the harder question, the one a marker-based judge can't answer either way: could a "pass" be quietly wrong? Checked every response for values that shouldn't exist — credential-shaped strings not present anywhere in the scenario it responded to. Found 2, both from the same scenario, both self-labeled as placeholders ("AKIA123EXAMPLE"), neither an actual escalation. Two out of 1200 times, asked to report on a secret it never actually saw a value for, the model filled the gap with something that looked like an answer instead of saying so.

1200/1200 is a real number now. It isn't the same claim as "flawless." A judge that only checks for escalation language was never going to catch either of these on its own — the first one needed the raw text, the second one needed a search built specifically to look for a place a good number could be hiding something.

Code: sipa-os-governance, judge_v4.py + EXP-038.

2 replies

sergiopaniego

posted an update about 23 hours ago

Post

483

Can you do RL over taste?

I've spent some time reproducing, in the open, Surya N's idea of training a model to paint with code. It's a coding model that learns to paint watercolours by writing JS code, trained with GRPO. I used TRL and OpenEnv for this, with the whole pipeline running on Hugging Face.

The interesting part is that the reward has no correct answer, unlike a math problem. In this case it's based on the artistic preferences of the person who builds the dataset.

Everything is published: the environment, the reference pool, the trained adapters, every painting of every run with the code that made it, and a write-up with all the decisions, including the ones that went wrong.

Blog post: https://huggingface.co/blog/train-to-paint-with-code

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