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Congratulations to the amazing team at Arcee.ai on their first LLM built from scratch! 🔥 I got to see the beginning and I’m happy with final results. Arcee Foundation Model (AFM) is an SLM that punches above its weight class 🚀 Wonderful work! Lucas Atkins Fernando Fernandes Neto

"Pretraining is dead" is dead

Our first foundation model, AFM-4.5B, is not even 24 hours old, and our users are already going wild. "Don't sleep on Arcee" seems to be the motto. We love that, because we haven't slept much lately 😃 You can try the model in our playground (afm.arcee.ai/#Chat-UI) and on

Congratulations to the DatologyAI team on powering the data for AFM-4B by Arcee.ai - competitive with Qwen3 - using way way less data! This is exactly why I'm so excited to be joining DatologyAI this summer to push the frontier of data curation 🚀

Last week, we launched AFM-4.5B, our first foundation model. In this post by Charles Goddard , you will learn how we extended the context length of AFM-4.5B from 4k to 64k context through aggressive experimentation, model merging, distillation, and a concerning amount of soup. Bon

this release is pure class. arcee using their data to do some short-term continued pretraining on GLM 32b. long context support has gone from effectively 8k -> 32k, and all base model evaluations (including short context ones) have improved

You can't overstate how impressive this is. Arcee took one of the strongest base models, GLM-4, a product of many years of Tsinghua R&D (THUDM/THUKEG/Z.ai, GLM-130B was maybe *the first* real open weights attack on OpenAI, Oct 2022)… and made it plain better. And told us how.

“First of many blogs” from Arcee. AFM-4.5B scaled from 4K → 64K context. ⮕ arcee.ai/blog/extending… 𝑱𝑼𝑺𝑻 𝑴𝑬𝑹𝑮𝑬, 𝑫𝑰𝑺𝑻𝑰𝑳𝑳, 𝑹𝑬𝑷𝑬𝑨𝑻. Proof it scales: Same merge–distill cycle applied to GLM-4-32B. Fixes 8K degradation in the 0414 release. +5% overall, strong

In this post, Mariam Jabara, one of our Field Engineers, walks you through three real-life use cases for model merging, recently published in research papers: ➡️ Model Merging in Pre-training of Large Language Models ➡️ PatientDx: Merging Large Language Models for Protecting

We’re beyond thrilled to share that Arcee AI Conductor has been named “LLM Application of the Year” at the 2025 AI Breakthrough Awards. This recognition isn’t just a shiny badge—it’s a celebration of a vision we’ve been chasing for years: making AI smarter, more accessible, and

Today, we're excited to announce the integration of Arcee.ai Conductor, our SLM/LLM model routing solution, into the Zerve AI platform, an agent-driven operating system for Data & AI teams 😃 This collaboration enables data scientists, engineers, and AI developers to build,

Today, we're happy to announce the open-weights release of five language models, including three enterprise-grade production models that have been powering customer workloads through our SaaS platform (SuperNova, Virtuoso-Large, Caller), and two cutting-edge research models

Save the date! A week from now, please join us live to discover how Zerve AI is leveraging our model routing solution, Arcee Conductor, to improve its agentic platform for data science workflows. This should be a super interesting discussion, and of course, we'll do demos!

In this new video, I introduce two new research-oriented models that Arcee.ai recently released on Hugging Face Face. Homunculus is a 12 billion-parameter instruction model distilled from Qwen3-235B onto the Mistral AI Nemo backbone. It was purpose-built to preserve Qwen’s

In this video, I introduce and demonstrate three production-grade models that Arcee.ai recently opened and released on Hugging Face . Arcee-SuperNova-v1 (70B) is a merged model built from multiple advanced training approaches. At its core is a distilled version of

In this fun demonstration, you can witness the impressive capabilities of Arcee.ai AFM-4.5B-Preview, Arcee's first foundation model, across diverse domains. The demo showcases the model tackling complex knowledge questions, creating sophisticated creative writing, and

As generative AI becomes increasingly central to business applications, the cost, complexity, and privacy concerns associated with language models are becoming significant. At Arcee.ai, we’ve been asking a critical question: Can CPUs actually handle the demands of language