Creating bounding boxes and OCR work both well. Creating an SVG of an image does not work well.

With Q4 everything below 150B should be fine. You can also run the -Flash variant of this model in Q1, but it is probably not usable.

It's a good model, way better than the models from half a year ago.

You posted about it here: https://sh.itjust.works/post/11722444 This image is different to your last image.

The images you posted looks like this in gray scale:

This map seems to be unrelated to the actual numbers that i found online, but i also have not found any good statistics for the whole world.

You can already get a taste of the future with this finetune: https://huggingface.co/TheDrummer/Rivermind-24B-v1

Here is a small snippet from their description:

Why Rivermind 24B v1? While other AIs struggle with basic tasks, Rivermind 24B v1 handles complex queries with the precision of a Dyson vacuum cleaning every last speck of dust. It’s not just an AI—it’s your future, optimized.

Ready to upgrade? Try Rivermind 24B v1 today and experience the difference—because tomorrow’s AI is here, and it’s powered by Intel’s cutting-edge processors. 🚀

There are not many models that support any-to-any, currently the best seems to be Qwen3-Omni, the audio quality is not great and it is not supported by llama.cpp: https://github.com/ggml-org/llama.cpp/issues/16186

Breakout 71 is a fun game.

I dont know what you mean with steering?

• Do you want a given output structure, like json or toml?
• Do you want to align the model, with your dataset of question and answer pairs?

First of all, have you tried giving the model multiple examples of input output pairs in the context, this already helps the model a lot to output the correct format.

Second you can force a specific output structure by using a regex or grammar: https://python.langchain.com/docs/integrations/chat/outlines/#constrained-generation https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md

And third, in case you want to train a model to respond differently and the previous steps were not good enough, you can fine-tune. I can recommend this project to you, as it teaches how to fine-tune a model: https://github.com/huggingface/smol-course

Depending on the size of the model, that you want to fine-tune and the amount of compute that you have available you can either train by updating all parameters like ORPO or you can train via PEFT (LoRA)

First of all i think it is a great idea to give the model access to a map. Unfortunately it seems like, that the script is missing a huge part at the end, the loop does not have any content and the Tools class is missing.

I have found the problem with the cut off, by default aider only sends 2048 tokens to ollama, this is why i have not noticed it anywhere else except for coding.

When running /tokens in aider:

$ 0.0000   16,836 tokens total
           15,932 tokens remaining in context window
           32,768 tokens max context window size

Even though it will only send 2048 tokens to ollama.

To fix it i needed to add a file .aider.model.settings.yml to the repository:

- name: aider/extra_params
  extra_params:
    num_ctx: 32768

Split Horizon with Poison Reverse

This is probably the only reason microsoft recall exists, as it is completely useless for anything else.