12/29/2023 0 Comments Tiny bunny change language![]() ![]() One of my open questions about LLaMA was how difficult and expensive it would be to fine-tune it such that it could respond better to instructions. Overall, always keep in mind that models are very sensitive to prompts (particularly when they have not been finetuned). As such, they should be prompted so that the expected answer is the natural continuation of the prompt. Keep in mind these models are not finetuned for question answering. The LLaMA models had not been through this process. “Write me a poem about pandas!” now works as a prompt, instead of “Here is a poem about pandas:”. Thanks to instruction tuning you can be a lot more, well, human in the way you interact with the model. Prior to this, you had to think very carefully about how to construct your prompts. We then use this data to fine-tune GPT-3. On prompts submitted by our customers to the API, our labelers provide demonstrations of the desired model behavior, and rank several outputs from our models. To make our models safer, more helpful, and more aligned, we use an existing technique called reinforcement learning from human feedback (RLHF). One of the great innovations from OpenAI was their application of instruction tuning to GPT-3: You give it a sequence of words, “The first man on the moon was”, and it completes that sentence, hopefully with useful content. The biggest weakness in the LLaMA models released by Meta research last month is their lack of instruction-tuning.Ī language model is a sentence completion engine. Alpaca behaves similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce (<600$). We introduce Alpaca 7B, a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. Here’s the introduction to the Alpaca announcement: When I talked about a “Stable Diffusion moment” this is the kind of thing I meant: the moment this stuff is available for people to experiment with, things accelerate. Also today: a team at Stanford released Alpaca: A Strong Open-Source Instruction-Following Model-fine-tuned from the LLaMA 7B model.Update 14th March: Now 1 second per token on an older Pixel 5! 13th March (today): Anish Thite reports llama.cpp running on a Pixel 6 phone (26 seconds per token).Sunday 12th March: cocktailpeanut releases Dalai, a “dead simple way to run LLaMA on your computer”: npx dalai llama and npx dalai serve.Later on Saturday: Artem Andreenko reports that llama.cpp can run the 4-bit quantized 7B LLaMA language model model on a 4GB RaspberryPi-at 10 seconds per token, but still hugely impressive.Let’s look at what’s happened in the past three days. On Saturday 11th March I wrote about how Large language models are having their Stable Diffusion moment. Stanford Alpaca, and the acceleration of on-device large language model development
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