You mean as in LoRA (low-rank adaptation)? You are factorizing the weight matrices but only during finetuning. The self-attention mechanism computation is still the same (if you ignore that the weight matrices are different).
LLMS
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Multi-query attention experiences revival with Falcon model
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Also, multi-query attention has a little comeback (thanks to Falcon)
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Self-Attention Alternatives Failed; FlashAttention Gains Popularity
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You mean alternatives to self-attention specifically (versus parameter efficient finetuning etc.)? I think none of them stood the test of time. I think the problem is that they are all approximations. The relatively recent FlashAttention mechanism is super popular though.
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Reward Model Training vs User Feedback: Preferences and Finetuning
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Good question. In their original finetuning, they train a reward model based on relative preference (rankings among multiple responses). And from the user feedback, there's only thumbs up & down. You can probably use that for supervised finetuning I guess.
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Reasons Behind OpenAI Models’ Superior Performance Remain Unknown
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Yeah. And we still don't know why OpenAI models are better: 1. More and better data?
2. Larger pretrained base model?
3. RLHF instead of supervised finetuning? My guess is it's probably all of these above. -
Cohere Command Model Finetuning for Custom AI Responses
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With Command model finetuning, you can train a custom model to respond to a specific command in a natural and fluid way. In this blog post, we'll go through how to set up finetuning and discuss some of the benefits of using this feature. https://
short.cohere.ai/izRrzl?utm_sou
rce=twitter&utm_medium=social
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Dataset Importance for LLM Fine-tuning: Camels Paper Insights
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And the recent "How Far Can Camels Go" paper (
https://
arxiv.org/abs/2306.04751) is a nice example of how important the dataset for LLM finetuning is. -
Build Python Book Summarizer with GPT-3.5 in Minutes
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Create Book Summarizer in Python with GPT-3.5 in 10 Minutes: Are you tired of reading lengthy books that take up much of your time? Do you wish to get a summary of the main points without having to go through the entire book? Well, look no further… https://
analyticsvidhya.com/blog/2023/05/c
reate-book-summarizer-in-python-with-gpt-3-5-in-10-minutes/?utm_source=dlvr.it&utm_medium=twitter
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LLM Enhanced with Search and Symbolic Math Tools
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The important part is when the LLM will be combined with search and symbolic math tool – the latter will enhance logical reasoning.
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XGen: Salesforce’s Llama Alternative with 8K Context Length
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XGen is the llama-equivalent from Salesforce but with 8k context length and an Apache 2.0 licence! In this video, I show you how you can build a simple summarizer app using this model and gradio: https://
youtube.com/watch?v=Y_93Og
hB8bY&ab_channel=AbhishekThakur
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