Self-instruct fine-tuning of LLMs (Alpaca) : The Introduction
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Fine - tuning and "INSTRUCTION
Fine - tuning and "INSTRUCTION fine-tuning" your LLM has significant advantages.
"Instruct fine-tuning" can be a powerful technique for improving the performance of language models, particularly for tasks where the input data has a specific structure or format. By providing the model with guidance on how the input data is structured, we can help the model better understand the relationships between different parts of the input and improve its ability to make accurate predictions.
It's important to note that "instruct fine-tuning" requires structured training data, which can take additional effort to prepare. However, the benefits of improved performance can be significant, particularly for tasks where the structure of the input data is important.
Overall, instruct fine-tuning is a valuable tool in the arsenal of techniques for fine-tuning language models, and it can be particularly effective for tasks with structured input data.
Self-instruct is a method to generate instruction data sets, where ChatGPT /GPT-4 or other LLMs generate synthetic data sets according to our needs for fine tuning or instruct fine tuning our LLM for specific tasks (like summarization, translation, Q+A..).
SELF-INSTRUCT: Aligning Language Model
with Self Generated Instructions
https://arxiv.org/pdf/2212.10560.pdf
Stanford ALPACA:
https://crfm.stanford.edu/2023/03/13/...
https://github.com/tatsu-lab/stanford...
#ai
#naturallanguageprocessing
#finetuning
#chatgpt
#machinelearning
"Instruct fine-tuning" can be a powerful technique for improving the performance of language models, particularly for tasks where the input data has a specific structure or format. By providing the model with guidance on how the input data is structured, we can help the model better understand the relationships between different parts of the input and improve its ability to make accurate predictions.
It's important to note that "instruct fine-tuning" requires structured training data, which can take additional effort to prepare. However, the benefits of improved performance can be significant, particularly for tasks where the structure of the input data is important.
Overall, instruct fine-tuning is a valuable tool in the arsenal of techniques for fine-tuning language models, and it can be particularly effective for tasks with structured input data.
Self-instruct is a method to generate instruction data sets, where ChatGPT /GPT-4 or other LLMs generate synthetic data sets according to our needs for fine tuning or instruct fine tuning our LLM for specific tasks (like summarization, translation, Q+A..).
SELF-INSTRUCT: Aligning Language Model
with Self Generated Instructions
https://arxiv.org/pdf/2212.10560.pdf
Stanford ALPACA:
https://crfm.stanford.edu/2023/03/13/...
https://github.com/tatsu-lab/stanford...
#ai
#naturallanguageprocessing
#finetuning
#chatgpt
#machinelearning
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