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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 train
by benob 4y ago
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.
- wsgeorge 4y ago> 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?
- inportb 4y agoYou could just use a much bigger model to perform arbitrary tasks without fine-tuning.
- Closi 4y agoSure, 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.
- holoduke 4y agoDo you know any guide on how to train these models? Any thoughts on the hardware requirements?