5 posts tagged “olmo”
2025
Olmo 3 is a fully open LLM
Olmo is the LLM series from Ai2—the Allen institute for AI. Unlike most open weight models these are notable for including the full training data, training process and checkpoints along with those releases.
[... 1,834 words]What people get wrong about the leading Chinese open models: Adoption and censorship (via) While I've been enjoying trying out Alibaba's Qwen 3 a lot recently, Nathan Lambert focuses on the elephant in the room:
People vastly underestimate the number of companies that cannot use Qwen and DeepSeek open models because they come from China. This includes on-premise solutions built by people who know the fact that model weights alone cannot reveal anything to their creators.
The root problem here is the closed nature of the training data. Even if a model is open weights, it's not possible to conclusively determine that it couldn't add backdoors to generated code or trigger "indirect influence of Chinese values on Western business systems". Qwen 3 certainly has baked in opinions about the status of Taiwan!
Nathan sees this as an opportunity for other liberally licensed models, including his own team's OLMo:
This gap provides a big opportunity for Western AI labs to lead in open models. Without DeepSeek and Qwen, the top tier of models we’re left with are Llama and Gemma, which both have very restrictive licenses when compared to their Chinese counterparts. These licenses are proportionally likely to block an IT department from approving a model.
This takes us to the middle tier of permissively licensed, open weight models who actually have a huge opportunity ahead of them: OLMo, of course, I’m biased, Microsoft with Phi, Mistral, IBM (!??!), and some other smaller companies to fill out the long tail.
mlx-community/OLMo-2-0325-32B-Instruct-4bit (via) OLMo 2 32B claims to be "the first fully-open model (all data, code, weights, and details are freely available) to outperform GPT3.5-Turbo and GPT-4o mini". Thanks to the MLX project here's a recipe that worked for me to run it on my Mac, via my llm-mlx plugin.
To install the model:
llm install llm-mlx
llm mlx download-model mlx-community/OLMo-2-0325-32B-Instruct-4bit
That downloads 17GB to ~/.cache/huggingface/hub/models--mlx-community--OLMo-2-0325-32B-Instruct-4bit.
To start an interactive chat with OLMo 2:
llm chat -m mlx-community/OLMo-2-0325-32B-Instruct-4bit
Or to run a prompt:
llm -m mlx-community/OLMo-2-0325-32B-Instruct-4bit 'Generate an SVG of a pelican riding a bicycle' -o unlimited 1
The -o unlimited 1 removes the cap on the number of output tokens - the default for llm-mlx is 1024 which isn't enough to attempt to draw a pelican.
The pelican it drew is refreshingly abstract:
Today we release OLMo 2 32B, the most capable and largest model in the OLMo 2 family, scaling up the OLMo 2 training recipe used for our 7B and 13B models released in November. It is trained up to 6T tokens and post-trained using Tulu 3.1. OLMo 2 32B is the first fully-open model (all data, code, weights, and details are freely available) to outperform GPT3.5-Turbo and GPT-4o mini on a suite of popular, multi-skill academic benchmarks.
— Ai2, OLMo 2 32B release announcement
2024
Open Language Models (OLMos) and the LLM landscape (via) OLMo is a newly released LLM from the Allen Institute for AI (AI2) currently available in 7b and 1b parameters (OLMo-65b is on the way) and trained on a fully openly published dataset called Dolma.
The model and code are Apache 2, while the data is under the “AI2 ImpACT license”.
From the benchmark scores shared here by Nathan Lambert it looks like this may be the highest performing model currently available that was built using a fully documented training set.
What’s in Dolma? It’s mainly Common Crawl, Wikipedia, Project Gutenberg and the Stack.