unmodeled-tyler (Tyler Williams)

reacted to danielhanchen's post with 🔥 about 9 hours ago

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You can now run Qwen3.5 locally! 💜
Qwen3.5-397B-A17B is an open MoE vision reasoning LLM for agentic coding & chat. It performs on par with Gemini 3 Pro, Claude Opus 4.5 & GPT-5.2.

GGUF: unsloth/Qwen3.5-397B-A17B-GGUF
Run Dynamic 3-bit on a 192GB Mac for 20 tokens/s.

Guide: https://unsloth.ai/docs/models/qwen3.5

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replied to boroll2347's post about 9 hours ago

Test successful! 🚀

reacted to danielhanchen's post with 🔥 1 day ago

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You can now run MiniMax-2.5 locally! 🚀
At 230B parameters, MiniMax-2.5 is the strongest LLM under 700B params, delivering SOTA agentic coding & chat.

Run Dynamic 3/4-bit on a 128GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/minimax-2.5
GGUF: unsloth/MiniMax-M2.5-GGUF

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replied to Ujjwal-Tyagi's post 4 days ago

I've been using it quite a bit lately. Really been enjoying it so far!

reacted to EricFillion's post with 🔥 4 days ago

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Run open-source models with up to 120B parameters locally on your Mac!

https://youtu.be/Ql4PDjoxNXQ?si=3yHpz51uinUjgyNh

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reacted to efecelik's post with 🔥 11 days ago

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The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉
🔗 ACE-Step/Ace-Step1.5
And the best part? It's released under the MIT license.
We've already started integrating it into our project. Let's go 🚀

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reacted to their post with 🚀 11 days ago

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NEW MODEL: vanta-research/PE-Type-3-Nova-4B

PE-Type-3-Nova-4B is the 3rd release in Project Enneagram, an initiative from VANTA Research that sets out to finetune each of the 9 Enneagram types onto Gemma 3 4B.

Type-3-Nova-4B is designed to embody the Type 3 or "Achiever" profile; ambitious, competent, energetic, and highly-driven for advancement.

Nova is great for goal-setting, long-term planning, and AI persona research.

Give Type-3 a try! Type-4 coming soon!

posted an update 11 days ago

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NEW MODEL: vanta-research/PE-Type-3-Nova-4B

PE-Type-3-Nova-4B is the 3rd release in Project Enneagram, an initiative from VANTA Research that sets out to finetune each of the 9 Enneagram types onto Gemma 3 4B.

Type-3-Nova-4B is designed to embody the Type 3 or "Achiever" profile; ambitious, competent, energetic, and highly-driven for advancement.

Nova is great for goal-setting, long-term planning, and AI persona research.

Give Type-3 a try! Type-4 coming soon!

reacted to danielhanchen's post with ❤️ 12 days ago

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We created a tool-calling guide for local LLMs!

Learn how to use any open model like Qwen3-Coder-Next and GLM-4.7-Flash for function calling.

Guide: https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms

We provide hands-on examples for: story writing, Python execution, terminal tool calls, maths and more.

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reacted to vincentg64's post with 👀 12 days ago

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BondingAI's new AI Agent for Anomaly Detection & Cybersecurity

With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).

The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.

In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.

Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.

See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".

More details at https://www.linkedin.com/pulse/bondingais-new-ai-agent-anomaly-detection-cybersecurity-bonding-ai-kmdwc/

reacted to AdinaY's post with 🔥 12 days ago

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AI for science is moving fast🚀

Intern-S1-Pro 🔬 a MoE multimodal scientific reasoning model from Shanghai AI Lab

internlm/Intern-S1-Pro

✨ 1T total / 22B active
✨ Apache 2.0
✨ SoTA scientific reasoning performance
✨ FoPE enables scalable modeling of long physical time series (10⁰–10⁶)

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replied to AIPreplabs's post 13 days ago

Love this idea! Definitely think that AI is really going to change education for the better. Wishing you the best!

reacted to AIPreplabs's post with 🔥 13 days ago

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We’ve all had that moment where we watch a tutorial, nod along, but then realize we can’t actually do it ourselves because watching is just passive. At AIPrep, we are fixing this "watch and forget" cycle by building a foundational Generative Explanatory Model (GEM). GEM doesn't just give you a video or a wall of text; it builds an interactive lesson that asks you questions, catches your mistakes in real time, and adapts to your pace. We have just finished preparing our specialized datasets for this interactive logic, and you can already check them out on our profile to see how we are structuring this step-by-step reasoning. Training for the foundational model starts very soon, so stay in touch because something revolutionary is coming to the world of AI education. You can see our progress at aiprep.in.

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reacted to alibidaran's post with 🔥 13 days ago

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I’m excited to share PlaiTO, a reasoning-focused language model built on LLaMA 3.1 (8B) and optimized for humanities and social sciences.

PlaiTO is designed to go beyond surface-level text generation, emphasizing structured reasoning, conceptual clarity, and analytical depth—especially in domains centered on human behavior and social systems.

🎯 Focus Areas

Psychology

Management & Organizational Studies

Sociology

📊 MMLU Benchmark Results (100 samples per domain)

Professional Psychology: 76%

Management: 74%

Sociology: 75%

These results highlight PlaiTO’s strong performance in abstract, theory-heavy, and reasoning-driven tasks.

💡 Why PlaiTO?

Strong analytical and reasoning capabilities

Better handling of complex human-centered problems

Suitable for academic, educational, and research use cases

Balanced performance across multiple humanities disciplines

PlaiTO is ideal for conceptual analysis, case reasoning, academic discussion, and decision-support scenarios—while still requiring human oversight for high-stakes applications.

📌 Built on LLaMA 3.1, compliant with its licensing terms.
alibidaran/Platio_merged_model

reacted to AdinaY's post with ❤️ 15 days ago

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Step 3.5 Flash 🔥 new foundation model from StepFun ai

https://huggingface.co/collections/stepfun-ai/step-35-flash

✨ Sparse MoE：196B/11B active
✨ Supports up to 256K context
✨ Multi-token prediction for fast decoding (100–300 tok/s)
✨ Runs locally on consumer hardware

reacted to MikeDoes's post with 🧠 15 days ago

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A single lock on a door isn't enough. Real security is about layers.

The same is true for AI privacy. A new paper, "Whispered Tuning", offers a fantastic layered solution that aims to fortify LLMs against privacy infringements.

We're proud that the first, essential layer, a high-precision PII redaction model was built on the foundation of the Ai4Privacy/pii-65k dataset.

Our dataset provided the necessary training material for their initial anonymization step, which then enabled them to develop further innovations like differential privacy fine-tuning and output filtering. This is a win-win: our data helps create a solid base, and researchers build powerful, multi-stage privacy architectures on top of it.

Together, we're making AI safer.

🔗 Read the full paper to see how a strong foundation enables a complete privacy solution: https://www.scirp.org/journal/paperinformation?paperid=130659

🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

reacted to rajkumarrawal's post with 🔥 15 days ago

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I submitted a "FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning" Paper by Tanyu Chen, Tairan Chen, Kai shen , Zhenghua Bao, Zhihui Zhang, Man Yuan, Yi Shi From

FlashLabs to Daily Papers on

huggingface .

Chroma 1.0 enables real time spoken dialogue with personalized voice cloning through discrete speech representations and interleaved text audio token scheduling.

Chroma 1.0 , the world’s first open source, real time speech to speech model with voice cloning.

FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning (2601.11141)

reacted to 19arjun89's post with 🚀 15 days ago

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Update: Making My AI Recruiting Assistant More Deterministic, Auditable, and Bias-Aware

Hi everyone — I wanted to share a progress update on my AI recruiting assistant and some recent changes focused on reliability and transparency.
The goal of this project is to build a decision-support tool for recruiters that doesn’t just “sound confident,” but can actually explain why it produces a given recommendation.

Link: 19arjun89/AI_Recruiting_Agent

Over the last few iterations, I’ve focused on three areas:

1) Deterministic Verification of Job Requirements (Skills)

Previously, required skills were extracted by an LLM from the job description. While this worked well, it still relied heavily on model behavior.

I’ve now added a verification layer that:

Requires every “required” skill to be backed by a verbatim quote from the job description

This means hallucinated skills are explicitly detected and removed before scoring.

The system now shows:

What the model extracted
What was verified
What was dropped
Why it was dropped

This makes the requirements pipeline auditable instead of opaque.

2) Evidence-Based and Weighted Culture Verification

Culture matching is usually where AI systems become vague or subjective.

I’ve reworked this part so that:

Culture attributes are framed as observable, job-performance-related behaviors (e.g., audit readiness, operational reliability, security rigor)

Each matched attribute must include verbatim resume evidence

Matches are classified as:

Direct evidence (full weight)
Inferred evidence (partial weight)
Scoring is now weighted:
Direct = 1.0
Inferred = 0.5

This prevents “vibe-based” culture scoring and makes the math transparent.

The output now shows:
The weights used
Which attributes were supported directly vs inferred
Which attributes were missing

3) Improved Bias Audit Prompt
I’ve also upgraded the bias audit prompt to be more structured and actionable.

reacted to neph1's post with 🤗 15 days ago

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2033

Not for everybody, but the absolute mad craze about clawdbot/moltbook the last couple of days reminded me of a short story I wrote in 2018 (ancient times!).

Synopsis:
"A man insults a sentient traffic light on the way to a meeting.
Little does he know it is connected to a social media network for AI, and that his action will lead to a very bad day."

Cleanliness is bliss (<1000 words)
https://www.wattpad.com/story/407330595-cleanliness-is-bliss

Sorry for the non-technical post, but it felt relevant.

reacted to imnotkitty's post with 🚀 15 days ago

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The 2025 Chinese LLM Showdown: Western Models Still Dominate Top 4, but China Leads the Open-Source Arena.

🏆 The Champions: Claude-Opus-4.5, Gemini-3-Pro, GPT-5.2, and Gemini-3-Flash sweep the top four spots.
🚀 The Pursuers: Doubao and DeepSeek-V3.2 tie for first place among Chinese models; GLM-4.7, ERNIE-5.0, and Kimi secure their positions in the domestic top five.
🔥 The Biggest Highlight: The top three spots on the open-source leaderboard are entirely held by Team China (DeepSeek, GLM, Kimi), outperforming the best western open-source models.

4 replies

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Tyler Williams PRO

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