<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=2429892&amp;fmt=gif">

Cloudera acquires Verta Operational AI Platform Read more

January Model Roundup


Screenshot 2024-02-06 at 11.14.14 AM-1


The rapidly evolving GenAI models market can be overwhelming to keep track of, but fear not! In our monthly roundup, we present the top 5 open source GenAI models that deserve a spot on your radar. The trend is clear – the emergence of SLMs ranging from approximately 1 to 3 billion parameters is on the rise, and we anticipate this trend will persist. Let's delve into the latest releases from established names in the OSS community:


  • Built by: Meta
  • Size: 70B
  • Why it's interesting: This model, based on Llama 2, represents the latest and more performant version of the CodeLlama model for code generation.
  • Where to use it: Three variants are available – Code Completion, Instruct, and Python.
  • More here 


  • Built by: Mistral AI
  • Size: 46.7B
  • Why it's interesting: A Sparse Mixture-of-Experts model (SMoE) with open weights, Mixtral-8x7B outperforms Llama 2 70B on most benchmarks with 6x faster inference.
  • Where to use it: Boasting a large context size of 32k tokens, it excels in English, French, Italian, German, and Spanish, particularly showcasing robust performance in code generation. 
  • Learn more about the model here.

Eagle-7B by RWKV Open Source Group

  • Built by: RWKV Open Source Group
  • Size: 7B
  • Why it's interesting: Featuring the novel RWKV-v5 architecture (Receptance Weighted Key Value), Eagle-7B surpasses Falcon, LLaMA2, and Mistral in English evaluations. Notably, it is the most energy-efficient 7B model in terms of joules per token.
  • Where to use it: Ideal for various multilingual applications with support for 23 languages, outperforming other 7B counterparts. 
  • Dive into the details of Eagle 7B.

Phi-2 by Microsoft

  • Built by: Microsoft
  • Size: 2.7B
  • Why it's interesting: As part of the Phi series, Phi-2 builds on the success of its predecessors, including Orca 2, in teaching SLMs to reason. Achieving state-of-the-art performance among base language models, it competes with or outperforms models up to 25 times larger.
  • Where to use it: A cost-effective SLM suitable for specialized fine-tuning and distillation applications. Notably, the license has been updated to MIT, simplifying its use and commercialization. 
  • Uncover the surprising power of small language models.

StableLM 2 by StabilityAI

  • Built by: StabilityAI
  • Size: 1.6B
  • Why it's interesting: Drawing inspiration from Phi-2, StableLM 2 pushes the limits of SLMs further, outperforming Falcon-40B-Instruct on MTBench and its counterparts in the same range. It also excels in multilingual benchmarks.
  • Where to use it: An alternative to Phi-2 for fine-tuning and distillation, requiring a Stability AI membership for commercialization. 
  • Explore the capabilities of Stable LM 2.

Learn more

For an in-depth look at our capabilities, you can read our full launch blog post and check out the platform here.

Subscribe To Our Blog

Get the latest from Verta delivered directly to you email.

Try Verta today