Hacker News Reader

Yes you can finetune the model with reference output for any kind of language task. For translation, you are better off starting from a model specifically trained for that purpose such as facebook/nllb-200-distilled-1.3B. It will be faster and more accurate.

View on HN

> For translation, you are better off starting from a model specifically trained for that purpose

Yes, but wasn't the whole point of the recent LLM research to show that you didn't need to fine-tine for a specific task?

You could just use a much bigger model to perform arbitrary tasks without fine-tuning.

Sure, but you will get better results at a smaller number of parameters from a specifically trained model right now if you are trying to train/host it yourself.

Remember that GPT3 is 175 billion parameters so many times bigger than both the above models (and gpt4 is rumoured to be bigger still), which also allows it to be more generalisable.

If GPT3 was trained at 7 billion parameters it might also lose it's language translation capabilities.

Do you know any guide on how to train these models? Any thoughts on the hardware requirements?

Reply on news.ycombinator.com