test
vite-plugin-elm
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| test | vite-plugin-elm | |
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test
- Measuring Multitask Language Understanding
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Mixtral 7B MoE beats LLaMA2 70B in MMLU
Sources [1] MMLU Benchmark (Multi-task Language Understanding) | Papers With Code https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu [2] MMLU Dataset | Papers With Code https://paperswithcode.com/dataset/mmlu [3] hendrycks/test: Measuring Massive Multitask Language Understanding | ICLR 2021 - GitHub https://github.com/hendrycks/test [4] lukaemon/mmlu · Datasets at Hugging Face https://huggingface.co/datasets/lukaemon/mmlu [5] [2009.03300] Measuring Massive Multitask Language Understanding - arXiv https://arxiv.org/abs/2009.03300
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BREAKING: Google just released its ChatGPT Killer
With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.
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[Colab Notebook] Launch quantized MPT-30B-Chat on Vast.ai using text-generation-inference, integrated with ConversationChain
One method for comparison is the MMLU https://arxiv.org/abs/2009.03300.
- Partial Solution To AI Hallucinations
- Announcing GPT-4.
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Show HN: Llama-dl – high-speed download of LLaMA, Facebook's 65B GPT model
Because there are many benchmarks that measure different things.
You need to look at the benchmark that reflects your specific interest.
So in this case ("I wasn't impressed that 30B didn't seem to know who Captain Picard was") the closest relevant benchmark they performed is MMLU (Massive Multitask Language Understanding"[1].
In the LLAMA paper they publish a figure of 63.4% for the 5-shot average setting without fine tuning on the 65B model, and 68.9% after fine tuning. This is significantly better that the original GPT-3 (43.9% under the same conditions) but as they note:
> "[it is] still far from the state-of-the-art, that is 77.4 for GPT code-davinci-002 on MMLU (numbers taken from Iyer et al. (2022))"
InstructGPT[2] (which OpenAI points at as most relevant ChatGPT publication) doesn't report MMLU performance.
[1] https://github.com/hendrycks/test
[2] https://arxiv.org/abs/2203.02155
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DeepMind's newest language model, Chinchilla (70B parameters), significantly outperforms Gopher (280B) and GPT-3 (175B) on a large range of downstream evaluation tasks
Benchmark result is 67.6% which is 7.6% improvement from Gopher. MMLU is multiple choice Q&A over various subjects. Questions can be found linked in this github repo (see data).
vite-plugin-elm
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Live reloading code using vite-plugin-elm
I am using this vite plugin to work with Elm https://github.com/hmsk/vite-plugin-elm, but when running npm run dev and making changes to Main.elm, it doesn't recompile my code. Here is my vite.config.js:
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What is the right way to style an Elm application in 2023?
Also, start with a build manager that integrates everything for you. I currently use vite.
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Setting up an Elm project in 2022
Vite: vite-plugin-elm
What are some alternatives?
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
elm-architecture-tutorial - How to create modular Elm code that scales nicely with your app
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
elm-live - ⚡ A flexible dev server for Elm. Live reload included.
RAD - RAD Expansion Unit for C64/C128
elm-spa - single page apps made easy
ut - C++20 μ(micro)/Unit Testing Framework
node-test-runner - Runs elm-test suites from Node.js. Get it with npm install -g elm-test
elm-test-rs - Fast and portable executable to run your Elm tests
elm-review - Analyzes Elm projects, to help find mistakes before your users find them.
egghead - discord bot for ai stuff