Yeah, that's basically at the intersection of pretrained LLM and tool-use.
@rasbt
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Synthetic Data Structure Benefits and Knowledge Limitations
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Yeah, I think the main advantage is from the more refined nature of the synthetic data when it comes to response structure etc. Synthetic data can't really contain knowledge that raw data doesn't already include because the raw data was used to come up with the synthetic data in
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Synthetic Data Format Benefits Versus Raw Data Knowledge Quality
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Yes. I think the main benefit is that it comes in a more structured or refined format compared to raw data. But the knowledge is the same as in the raw data (and may even be more hallucinated), considering that raw data was used to generate the synthetic data through a model.
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Synthetic Data from High-Quality Models as Transfer Learning Jumpstart
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Yeah, I see it as some flavor of transfer learning (i.e., not starting from raw data). Synthetic data generated by a high-quality model (such as GPT-4o, which has already undergone extensive refinement) may serve as a kind of jumpstart to the model you are trying to train.
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Preventing Hallucinations in Synthetic Data Generation Models
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Since the model can't really explicitly distinguish between synthetic and non-synthetic data during training, the best way would tackle the problem at the root: ensuring that the synthetic data-generating model does not produce hallucinated contents.
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Microsoft’s Phi-4 14B Model Weights Now Publicly Available in LitGPT
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The model weights of Microsoft’s phi-4 14B LLM are now finally publicly available. Thanks to a kind community contribution, it’s now also available in LitGPT already (litgpt finetune microsoft/phi-4):
https://
github.com/Lightning-AI/l
itgpt/pull/1904
… I will have more to say about phi-4 in my upcoming AI -
December 2024 AI Research Papers Added to List
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In case you were looking for the December 2024 AI research papers, I just added them to the list! https://
magazine.sebastianraschka.com/i/152305086/de
cember
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LoRA Tutorial and Additional Tips from Readers
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Glad you found it useful, and thanks for the kind words! I think you probably already read that, but there is a LoRA from-scratch tutorial in the last Appendix. There was a reader how also had some useful additional tips regarding LoRA:
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No Need for Databases in LLM Pretraining Due to Embedding Changes
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I don't think you need databases to pretrain LLMs because you can basically throw out the embedding database after each batch since the embedding layer changes after each training iteration.
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GPT-2 Foundation with Llama 3 Advanced Implementation Guide
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The book is GPT-2 based, which is more beginner friendly. The Llama 3 code is bonus material. (It makes sense to implement GPT-2 first and than convert that to Llama 3 imho.) Llama 3 is dense, not an MoE though.