for those of you wondering, what is a parameter? It’s a configuration variable that is internal to the model and whose value can be estimated from the given data. They are required by the model when making predictions. The values define the skill of the model on your problem.
LLMS
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PyTorch Optimum Achieves 4.5x Speedup for Transformer Models
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A collaboration with @PyTorch to make transformer-based models faster using optimum library! Up to 4.5x speedup for text, vision and audio models using a one liner! Try it out now: https://
huggingface.co/docs/optimum/b
ettertransformer/overview
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LangChain 0.0.18 Release with Documentation and Interface Updates
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LangChain version 0.0.18 Docstring cleanup from @Jim_Salmons (first time contributor!)
Insert ability to the docstore/vectorstore interfaces from @sjwhitmore -
Whisper Paper Reading: OpenAI’s Speech Recognition Model
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Don't forget to read the paper ahead of the reading: https://
openai.com/blog/whisper/ -
Transformers Finally Added: Critical AI Architecture Recognition
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Thank you for adding Transformers! Many miss it which is wrong in 2022!
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Comparison of GPT-3 and GPT-4 Parameter Counts
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Currently, GPT-3 has 175 billion parameters, which is 10x faster than any of its closest competitors. GPT-4 is rumored be about 100 trillion parameters.
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Crypto Collapse Frees Hype Capacity for Language Models
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The collapse of crypto is great news for large language models, because it frees up a lot of hype capacity.
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Transformers Possess Greater Compositional Power Than MLPs
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Those tasks are irrelevant to my point. You seem to be denying that transformers have more compositional power than MLPs.
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LangChain 0.0.17: Improved Prompt Documentation
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ICYMI: LangChain version 0.0.17 Refactored and improved documentation around prompts
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Transformers Outperform MLPs for Parsing Tasks
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Transformers are better than MLPs far from the limit. And these papers are about something else. All it takes for my statement to be correct is that a transformer can learn to parse correctly in some cases.