SLM in Action: SuperNova-Medius, a high-performance 14B model @juliensimonfr

Uploaded November 2024 | Updated September 2026, 2 weeks ago

In this video, you will learn about SuperNova-Medius, a powerful 14-billion parameter model based on the Qwen2.5 architecture

⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos. Follow me on Medium at julsimon.medium.com or Substack at https://julsimon.substack.com. ⭐️⭐️⭐️

At release time, SuperNova-Medius was the best 14B model according to the HuggingFace Leaderboard and is still one of the top models in its class, with performance close to the one of 70B models.

I show you how to deploy SuperNova-Medius from the AWS Marketplace, first in a single click with AWS CloudFormation, then with a Jupyter notebook.

* Blog post: blog.arcee.ai/introducing-arcee-supernova-medius-a-14b-model-that-rivals-a-70b-2
* Model page: huggingface.co/arcee-ai/SuperNova-Medius
* Arcee AI on the AWS Marketplace: aws.amazon.com/marketplace/seller-profile?id=seller-r7b33ivdczgs6
* Notebooks: github.com/arcee-ai/aws-samples

00:00 Introduction
03:25 Finding Arcee models on the AWS Marketplace
07:05 Deploying with AWS CloudFormation
11:20 Finding our AWS developer resources
12:20 Testing the endpoint
15:15 Deploying with a Jupyter notebook
19:10 Testing the endpoint again, with streaming inference

#ai #aws #slm #llm #openai #chatgpt #opensource #huggingface

SLM in Action: SuperNova-Medius, a high-performance 14B model

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$Trinity Large: An Open 400B MoE Model I built a code review app powered by @ArceeAIs new Trinity Large model and deployed it to a @HuggingFace Space. ⭐️⭐️⭐️ More content on Substack at https://julsimon.substack.com ⭐️⭐️⭐️ In this video, I walk through a live demo — paste a GitHub PR URL, hit review, and watch it catch real issues: security flaws, logic bugs, missing edge cases. Its fast, free to try, and the 512K context window lets it process entire files without chunking. Code Review App (HF Space): https://huggingface.co/spaces/juliensimon/trinity-code-reviewer Trinity Large is a 400B parameter Mixture-of-Experts model trained in 33 days for $20M — a fraction of what frontier labs spend. It has 256 experts but activates only 4 per token, so only 13B active parameters do the work. That translates to 2-3x faster inference than anything in its weight class, which you can feel in the demo when reviews come back in seconds. In this video: - 🔴 Live demo: code review app catching real issues on real PRs - 🏗️ How I built the app — stack, prompting strategy, handling large diffs - 🧠 Architecture deep dive: 256 experts, 4 active, extreme sparsity - 📦 Why three checkpoints exist: Preview, Base, and TrueBase - ⚡ Why MoE makes this practical to actually deploy and run at scale Try the app yourself and let me know what it catches in your code. Hugging Face model page: https://huggingface.co/arcee-ai/Trinity-Large-Preview OpenRouter model page: https://openrouter.ai/arcee-ai/trinity-large-preview Arcee AI blog post: https://www.arcee.ai/blog/trinity-large Arcee AI technical report: https://github.com/arcee-ai/trinity-large-tech-report VentureBeat article: https://venturebeat.com/technology/arcees-u-s-made-open-source-trinity-large-and-10t-checkpoint-offer-rare-look$

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