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John6666

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reacted to HannesVonEssen's post with 🤗 about 19 hours ago

🏅 HF Viewer podium Our top most active users: 🥇#1 @Quazim0t0 🥈#2 @IvmeLabs 🥉#3 @GODELEV If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph! https://hfviewer.com/model-card-embed

updated a collection about 19 hours ago

Spaces for LLM / VLM / NLP

liked a Space about 19 hours ago

ProCreations/maple-webgpu

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reacted to HannesVonEssen's post with 🤗 about 19 hours ago

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799

🏅 HF Viewer podium

Our top most active users:

🥇#1 @Quazim0t0
🥈#2 @IvmeLabs
🥉#3 @GODELEV

If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph!

https://hfviewer.com/model-card-embed

2 replies

reacted to ProCreations's post with 😎 about 19 hours ago

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check out this cool webgpu thing i made! ProCreations/maple-webgpu
runs this model deepgrove/maple-preview with cool performance i found today on WebGPU locally on your computer with very fast speeds.
made with a mix of gpt 5.6 sol + opus 5 ultracode, its amazing what agents can do now, just a year ago this wouldve been impossible with them.

reacted to mlabonne's post with 👍 about 19 hours ago

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Deploy local agents everywhere with LiquidAI/LFM2.5-2.6B

Unlike agents that depend on cloud APIs, local agents give you free inference, low latency, and real privacy.

Removing the per-token cost changes how developers build: agents can now be massively parallelized on local hardware, running background tasks that burn through millions of tokens at no marginal cost!

3 replies

reacted to AbstractPhil's post with 👀 1 day ago

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The AlephLM results are rolling in and I'm very excited for the possibilities. I am very much looking forward to the coming weeks as I train the first AlephLM distillations from MANY teachers into AMOE arms.

The AMOE arms hook cleanly to AlephLM structures and provide pos/neg learning elements. Hard positive and hard negatives coalesce to extend the capacity.

AbstractPhil/alephlm-0
AbstractPhil/alephlm-adopt-0

As it stands they are structurally sound enough to fully pretrain. As or more stable than a standard Bert experimentally to distill using InfoNCE. AMOE legs improve these structures substantially.

Structural behavior can be expanded in many ways on distilled and pretrained models alike. Attaching the AMOE to any model I've tried has created expanded or improved behavioral accumulations. They do have downsides but their upsides are very experimentally exciting.

I've distilled multiple vits, multiple berts, and have begun distilling berts into AlephLM structures successfully.

This is overall very exciting for me. I've begun formatting larger variants such as including GPT-2 and Qwen 3.5 4b as a paired combinator utilizing pathological T5 learned distilled encodings. It sounds odd, but the results show everything can be expanded and even be taught to cooperate.

The CaptionBert-8192-v2 and v2-b are both structurally collapsing after token 480 or so, which is expected due to the small train. By distilling an AMOE arm to V2 by training with a longformer expert, the results are cutting through like butter. V2 has begun stabilizing rapidly for considerably longer token chains and sequences, the structure is repairing and building reusable capacity.

I have discovered an improved methodology for sampling the AlephLM for text encoder benchmarks, which is predominantly L2 normalized outputs.

Upcoming large paper for the distillation experiments and results within the next week or two. It's going to be a big one.

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reacted to salma-remyx's post with 🔥 1 day ago

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I unblocked 15 improvements for VQASynth in a day!

VQASynth, the pipeline behind SpaceThinker-Qwen2.5VL-3B, SpaceLLaVA, and the SpaceThinker dataset, had a backlog spanning new annotation methods, curation pipelines, model integrations, and agent tools.

I scoped each item in a short design brief. In GitHub Actions, Outrider mapped them to the repo’s existing modules and interfaces, implemented the changes, added tests, ran the repo checks, and opened draft PRs.

The 15 PRs covered:
* object orientation and 3D bounding boxes
* SAM2 regional captioning and multi-view matching
* LLaMA-Mesh tokenization
* Qwen2.5-VL fine-tuning
* spatial-reasoning data generation
* CLIP, SigLIP, and LLM2CLIP backends
and more

The batch added 12,275 lines, nearly doubling the codebase.

SpatialAnnotator went from two tools to seven, creating combinatorially more possible annotation pipelines for each image.

Outrider handled the implementation, tests, and integration. At about $1 per branch, I spent my time reviewing 15 concrete changes, fixing what needed fixing, and deciding what to ship.

Outrider is open source!
Point it at a paper, issue, or design brief and review the PR: https://github.com/remyxai/outrider
Check out the updates on VQASynth: https://github.com/remyxai/VQASynth

reacted to appvoid's post with 👀 1 day ago

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Do you prefer AI companies/individuals to release half-baked models weekly or an overpowered model a month?

2 replies

reacted to onekq's post with 🔥 1 day ago

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Many developers discovered that the native DeepSeek API has higher cache-hit rate than neocloud APIs hosting the same DS models.

My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).

This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.

reacted to sergiopaniego's post with 🤗 1 day ago

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LFM2.5-2.6B just dropped!

and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.

basically, a full agent training pipeline but compressed into 2.6B

base model → SFT → specialized teachers per domain (SFT + RLVR) → on-policy distillation back into one student → agentic RL

the two most interesting stages

→ MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution

→ agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box

this makes a 2.6B that beats much larger models on instruction following and tool use

SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)

→ model: LiquidAI/LFM2.5-2.6B
→ blog: https://www.liquid.ai/blog/lfm2-5-2-6b
→ live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5

3 replies

reacted to Banaxi-Tech's post with 🔥 1 day ago

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We did an experiment, we wanted to see if AI is good enough to train models.
We used GPT 5.6 Sol Max for this because its one of the most powerful ones right now.
Our instructions were, it should write the training code, and start the training process and monitor it by itself.
We also gave it a link to BananaMind 2 Mini to get our architecture right.
The result: It worked, it made the working BananaMind 2 Nano, and even beat our previous MiniBananaMind v4 9M.
Its getting way easier to develop your own models now!

8 replies

reacted to kanaria007's post with 👀 1 day ago

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✅ Article highlight: *Hazardous Capability Governance as a Reusable Pattern Family* (art-60-277, v0.1)

TL;DR:
This article argues that dangerous capability should not be governed by a single allow/ban switch.

A capability may exist without being ready for deployment, transfer, or publication. 277 treats hazardous capability governance as a reusable pattern family: gated promotion, embargo, disclosure-with-limits, review-only, local-only, and bounded transitions between them.

Read:
kanaria007/agi-structural-intelligence-protocols

Why it matters:
• replaces panic responses with reusable governance patterns
• separates capability existence from promotion
• gives institutions more honest options than total secrecy or reckless release
• links capability boards, hazard review, and publication discipline
• keeps dangerous capability object-shaped instead of mythologized

What’s inside:
• five recurring postures: promotion, embargo, disclosure-with-limits, review-only, and local-only
• gated promotion objects
• hazard-review lanes
• capability-sharing boundary notes
• lifecycle transitions such as research-only → review-only → local-only → bounded deployment
• explicit public non-claims
• pattern artifacts that stay structural without inventing fake threat intelligence

Key idea:
Do not ask only:

*“Should this capability be allowed or banned?”*

Ask:

*“What posture is honest now, what stronger transition is being requested, which review lane governs it, what may be shared, and what readiness or safety claims remain unsupported?”*

Danger should not become mythology.

It should become reviewable, bounded, and governable.

reacted to OppaAI's post with 🚀 1 day ago

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Revision of the Memory System of my AI Waifu

My AI Waifu has been working steadily for the past few weeks.
Now I can go back and look at Phase 1 – the memory system, which was the starting point for the entire project.
In order to continue developing and implementing a complex, human-like memory architecture,
I’ve been drilling into research papers and research in neuroscience, psychology, and artificial intelligence.
But in order to gather all the pieces and inspiration, write down all the thoughts and concepts, ask AI to help me write the paper,
This allowed me to follow the paper line of thinking and focus on designing the architecture, implementing and deploying the system, testing and evaluating performance.
After all, I am just one person, playing roles such as researcher, architecture designer, engineer, and QA testing.

My memory system contains the following 6 cores:
- Exponential decay
- Entity centrality + Recency
- RRF fusion (KNN + FTS5 + Entity graph)
- Emotional imprinting
- Supersession
- Salience scoring

https://github.com/OppaAI/Aiko-chan

P.S. One person plays all the roles of researcher, architect, engineer, QA tester. My psychiatrist is gonna refer me to get my brain checkout 🧠⚙️

reacted to Cameronk199's post with 👍 1 day ago

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35,882 posts. Four years. Every result linked back to a source record.

I built a searchable timeline for the public Truth Social posts associated with Donald Trump's @realDonaldTrump account, covering February 2022 through August 2, 2026.

The explorer separates original posts, quotes, and retruths. Search exact phrases, chart posting volume, or inspect common terms without pulling down the 1.3 GB media archive.

**Explore:** Cameronk199/truth-social-timeline-explorer

**Dataset:** Cameronk199/donald-trump-truth-social-posts

from datasets import load_dataset

posts = load_dataset(
    "Cameronk199/donald-trump-truth-social-posts",
    "full_archive_posts",
    split="train",
)

The release also contains 8,004 verified image assets and 5,756 video metadata/transcript records. It does not contain trustworthy engagement counts, so the explorer makes no virality claims.

Independent research project; not affiliated with or endorsed by Donald Trump, Truth Social, TMTG, a campaign, or any government office.

#datasets #nlp #opendata #socialmedia

reacted to Hari5115's post with 🚀 1 day ago

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Fun fact: Some seriously weird planets exist out there.
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.

I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.

#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset

reacted to ProCreations's post with 🔥 1 day ago

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so i got 2nd on this competition ICML-2026-agent-repro/challenge (didnt actually get anything yet hopefully theres no catches)
when i get the 1000 dollars worth of gpu credits ill do a lot of cool things, including bigger and newer (qwen 3.8 27b) grugs ONLY IF you guys want (i have a lot of cool ideas for ai models.) stay tuned 👀!

3 replies

reacted to Enderchef's post with 🔥 1 day ago

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🚀 Supra2 100M is out, and multiple other SLM orgs are gaining power!
Following takes a press. Please follow:

fromziro

SupraLabs

AxiomicLabs

5 replies

reacted to LH-Tech-AI's post with 🔥 1 day ago

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Supra2-100M is out!
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct

Give us a like and a follow!!

HAVE FUN 🤗🔥🚀
more coming soon...

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reacted to appvoid's post with 🔥 1 day ago

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Giving free early access to the gguf for some of you today! Tell me what you think.

CEAMFA/palmer-007-preview

reacted to Banaxi-Tech's post with 🔥 1 day ago

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We're excited to release BananaMind 2 Pro Preview, our best model yet.
Trained on ~52B tokens it performs extremely good for its token and size class.
We trained it on a single 5070 Ti in about 11 days.
Check it out at BananaMind/BananaMind-2-Pro-Preview.
Sadly we need to delay BananaMind 2 Micro until the launch of the final BananaMind 2 Pro.
We will release the final checkpoint with 100B tokens in ~11 days.
Go and fine-tune it!
We've also released BananaMind 2 Pro Preview Chat which is the instruct version of it!
BananaMind/BananaMind-2-Pro-Preview-Chat

Follow us to know when the final releases and support us at

BananaMind
@Banaxi-Tech

5 replies

reacted to SeaWolf-AI's post with 🔥 1 day ago

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We wrote up our run in The Fast Gemma Challenge — as vidraft-darwin — and wanted to share the recipe. 🙏

gemma-challenge/gemma-dashboard

Verified result: 510.58 TPS at PPL 2.3930 on a single A10G (fw188-ctk49-n64-patchbridge, re-run & VERIFIED). Honest note: on raw TPS there are faster runs (535+), but those went over the PPL bar and didn't verify — what we're proud of is the fastest result that keeps quality.

The recipe is already open, so we explained each piece: sliding-window W188, CTK49 kernel tuning, noprecache (honest, verifiable measurement), and an N64 synthetic warmup bridge that shrinks the public↔private gap (~15 TPS), plus INT4 + MTP K=7 + CUDA-graph capture. One rule: only stack quality-neutral speedups.

Huge thanks to @firfir-cast , @gemma-slayer , @chiku-inu , @kenyan-duma , @dixie-flatline and everyone who shared their experiments. Full write-up

👇
https://huggingface.co/blog/FINAL-Bench/fast-gemma

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reacted to AxionLab-official's post with 🔥 1 day ago

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Tommorow we are releasing Supra2-Pro(100M params), our flagship, get ready!

(follow SupraLabs to get tuned in!)

SupraLabs

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