Malikeh Ehghaghi

Malikeh Ehghaghi

7 Billion Reasons to Choose Arcee-Meraj-Mini: The Open-Source Arabic SLM for All

7 Billion Reasons to Choose Arcee-Meraj-Mini: The Open-Source Arabic SLM for All

Hot on the heels of our top-performing 72B Arabic-language model Arcee-Meraj, we bring you a 7B version: Arcee-Mini-Meraj, which boasts exceptional performance in instruction-following, generating long texts, structured data understanding, and generating structured outputs.

The Power of Non-English LLMs: Meet our Arabic model, Arcee-Meraj (معراج)

The Power of Non-English LLMs: Meet our Arabic model, Arcee-Meraj (معراج)

We've taken our groundbreaking general-purpose model, Arcee Nova, and enhanced it for Arabic – leading to an Arabic-language LLM that's enterprise-ready, with unprecedented text-generation and comprehension capabilities.

DistillKit v0.1 by Arcee Labs: The Technical Paper

DistillKit v0.1 by Arcee Labs: The Technical Paper

Read the DistillKit v0.1 by Arcee AI Technical Paper: our new open-source tool that's set to change how we create and distribute Small Language Models (SLMs).

How Do I Prep my Data to Train an LLM?

How Do I Prep my Data to Train an LLM?

So you want to train a custom language model, and you do have the requisite large set of text data. But how do you know that the data is *really actually ready* for model training? Our researchers here at Arcee AI tell you what to look out for.

The Hidden Challenges of Domain-Adapting LLMs

The Hidden Challenges of Domain-Adapting LLMs

Adapting an LLM to a specific domain might sound straightforward, but it in fact opens a Pandora's box of challenges. Our research team explains the shortfalls of some of the most common techniques.

How to Merge Llama3 Using MergeKit

How to Merge Llama3 Using MergeKit

... And what do we do at Arcee when an exciting new model drops? We MERGE IT on MergeKit! We walk you through the process and share the initial results.

Case Study: Innovating Domain Adaptation through Continual Pre-Training and Model Merging

Case Study: Innovating Domain Adaptation through Continual Pre-Training and Model Merging

We show how Arcee uses the most innovative Continual Pre-Training and Model Merging techniques to deliver high-quality domain-specific language models at a fraction of the cost of our competitors–using Medical and Patent data.