[JimDabell]

That depends on what counts as “a handful of languages” for you.

You can use llm for this fairly easily:

    uv tool install llm

    # Set up your model however you like. For instance:
    llm install llm-ollama
    ollama pull mistral-small3.2

    llm --model mistral-small3.2 --system "Translate to English, no other output" --save english
    alias english="llm --template english"

    english "Bonjour"
    english "Hola"
    english "Γειά σου"
    english "你好"
    cat some_file.txt | english

https://llm.datasette.io

[usagisushi]

Tip: You might want to use `uv tool install llm --with llm-ollama`.

ref: https://github.com/simonw/llm/issues/575

[JimDabell]

Thanks!

[jan_Sate]

That's just the base/stock/instruct model for general use case. There gotta be a finetune specialized in translation, right? Any recommendations for that?

Plus, mistral-small3.2 has too many parameters. Not all devices can run it fast. That probably isn't the exact translation model being used by Chrome.

[JimDabell]

I haven’t tried it myself, but NLLB-200 has various sizes going down to 600M params:

https://github.com/facebookresearch/fairseq/tree/nllb/

If running locally is too difficult, you can use llm to access hosted models too.