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danielhanchen 
posted an update 1 day ago
NJX-njx 
posted an update 3 days ago
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7259
Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.

On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.

On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.

Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.

This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it

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mayafree 
posted an update about 20 hours ago
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1510
I built a Space that lets you switch between all three Qwen3.5 official collection models in a single interface.

MAYA-AI/QWEN-3_5-CHAT

The architecture is the key part. Instead of using Gradio as the UI, I use it purely as an API engine. FastAPI serves a fully custom HTML/JS frontend that calls /gradio_api/call/chat via SSE streaming. No DOM conflicts, no layout constraints.

Four main features: instant model switching with automatic spec adjustment (max tokens, temperature ceiling, Vision availability all update per model), Thinking Mode via /think prefix with collapsible reasoning chain, Vision image upload via base64 conversion, and HF OAuth implemented directly at the FastAPI level.

For model selection: 122B-A10B with Thinking Mode for math, logic, and agents. 27B for writing, translation, and instruction following. 35B-A3B for fast everyday questions.

A few surprises during development — Gradio 6.x removed several parameters quietly, base64 image strings broke gr.Image(type="pil") so I switched to gr.Textbox with backend PIL conversion, and Thinking Mode parsing needed a full rewrite with indexOf instead of regex.

Thanks to the Qwen team for making this possible. Try it out and let me know what you think.

#Qwen3 #Qwen35 #OpenSourceAI #HuggingFace #LLM #ThinkingAI #vidraft #MultimodalAI
SeaWolf-AI 
posted an update about 4 hours ago
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285
ALL Bench — Global AI Model Unified Leaderboard

FINAL-Bench/all-bench-leaderboard

If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and — uniquely — all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale.
Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots.
The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench — the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind.
Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores.
All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.
OzTianlu 
posted an update 3 days ago
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4621
🔥 UPGRADE in Kai: 30B Scaling! 🔥
NoesisLab/Kai-30B-Instruct
NoesisLab/Kai-3B-Instruct
We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! 🚀
If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we.
Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training.
The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward pass—no external scaffolding required.
At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-3B-Instruct
📦 Model Weights:
NoesisLab/Kai-30B-Instruct
Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! 🧱💥
  • 1 reply
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hannayukhymenko 
posted an update 2 days ago
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1696
Do you translate your benchmarks from English correctly? 🤔
Turns out, for many languages it is much harder than you can imagine!

Introducing Recovered in Translation 🌍 together with @aalexandrov
ritranslation.insait.ai

Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!

Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.

We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️

We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗

Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
  • 1 reply
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prithivMLmods 
posted an update 1 day ago
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2303
QIE-Object-Remover-Bbox Demo removes objects and artifacts from selected regions using bounding box grounding. Built on Qwen-Image-Edit-2509 with Rapid Diffusers acceleration, it delivers fast 4-step inference via the QIE-2509 adapter. 🤗🔥

🔗Demo Space: prithivMLmods/QIE-Object-Remover-Bbox
🔗Qwen-Image-Edit-Rapid-AIO: prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V4
🔗Adapter-(LoRA): prithivMLmods/QIE-2509-Object-Remover-Bbox

🔗Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox

To learn more, visit the app page or the respective model pages.
nightmedia 
posted an update 1 day ago
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1123
Qwen3.5 Performance Metrics

With the 3.5 architecture, a lot of the old quanting methods don't work as before. I noticed this when benchmarking Deckard(qx) quants and by mistake ran a q8 that was better. That only happens if the qx sucked--and it did--enhancing layers just because they look interesting doesn't work anymore, so until I get a clear understanding of the architecture, I will publish mxfp4 and mxfp8 of the 3.5 models, that seem very stable and high performant

I will start posting here the metrics I gather from the series, starting with the smallest. If I have numbers from previous or similar models, I will post them in comparison

Qwen3.5-0.8B

quant    arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.351,0.501,0.733,0.462,0.348,0.682,0.573
mxfp4    0.339,0.489,0.738,0.433,0.330,0.672,0.553

Old model performance

Qwen3-0.6B
bf16     0.298,0.354,0.378,0.415,0.344,0.649,0.534
q8-hi    0.296,0.355,0.378,0.416,0.348,0.652,0.529
q8       0.299,0.354,0.378,0.414,0.346,0.650,0.535
q6-hi    0.301,0.356,0.378,0.415,0.350,0.651,0.541
q6       0.300,0.367,0.378,0.416,0.344,0.647,0.524
mxfp4    0.286,0.364,0.609,0.404,0.316,0.626,0.531

Quant    Perplexity     Peak memory
mxfp8    6.611 ± 0.049  7.65 GB
mxfp4    7.455 ± 0.057  6.33 GB


Detailed metrics by model

nightmedia/Qwen3.5-0.8B-mxfp8-mlx

nightmedia/Qwen3.5-2B-mxfp8-mlx

nightmedia/Qwen3.5-4B-mxfp8-mlx

nightmedia/Qwen3.5-9B-mxfp8-mlx

nightmedia/Qwen3.5-27B-Text

nightmedia/Qwen3.5-122B-A10B-Text-mxfp4-mlx

More metrics coming soon.

I am running these on my Mac, an M4Max with 128GB RAM. Some performance numbers like tokens/second reflect the performance on my box.

This post will be updated with every model that gets tested. The larger models take hours, the 27B a couple days, so it will be a long process.

-G
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appvoid 
posted an update 1 day ago
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1808
Let's keep the momentum for small models. I just published dot. It's the first pretrained causal model that is trained on math/symbols rather than english. The goal is to get an agnostic fewshot meta learner that learns from reality itself instead of language.

It's already decent at some tasks, with next version coming in a few weeks.


appvoid/dot
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abdurrahmanbutler 
posted an update about 20 hours ago
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234
🚀 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗞𝗮𝗻𝗼𝗻 𝟮 𝗘𝗻𝗿𝗶𝗰𝗵𝗲𝗿: 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗳𝗶𝗿𝘀𝘁 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗴𝗿𝗮𝗽𝗵𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹

Today we’re publicly releasing Kanon 2 Enricher, and with it, an entirely new class of AI model that we’re calling a hierarchical graphitization model.
This is fundamentally different from both universal extraction models and generative models.

As a hierarchical graphitization model, Kanon 2 Enricher natively outputs a 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗿𝗮𝗽𝗵 rather than tokens, which makes it architecturally incapable of hallucinating or inventing text that wasn’t present in the input.

What that enables in practice is unlike any other model or ML architecture on the market:

• 𝗡𝗼 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 🤖
It cannot hallucinate. All references and links are stored as spans, meaning exact character offsets anchored to the original text.

• 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 📑
It deconstructs a document’s full nested hierarchy, down to chapters, sections, clauses, schedules, signatures, and even singular sentences, and classifies each span with dozens of contextual features.

• 𝗘𝗻𝘁𝗶𝘁𝘆 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻, 𝗱𝗶𝘀𝗮𝗺𝗯𝗶𝗴𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 🔗
It resolves what references actually point to, then links entities, citations, and cross-references into a single coherent graph.

• 𝗚𝗿𝗮𝗽𝗵-𝗳𝗶𝗿𝘀𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 🏃‍➡️
Small enough to run locally on a consumer PC with sub-second latency, and it stays reliable on long documents where front

To read more about our new model, check out our latest Hugging Face article:
https://huggingface.co/blog/isaacus/introducing-kanon-2-enricher
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