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llm.c

LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the [GPT-2](https://github.com/openai/gpt-2) and [GPT-3](https://arxiv.org/abs/2005.14165) miniseries, along with a parallel PyTorch reference implementation

dot files and such

.vscode

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LLMSurvey

> A collection of papers and resources related to Large Language Models.

Byte-compiled / optimized / DLL files

__pycache__/

<h1 align="center">The Practical Guides for Large Language Models </h1>

Open LLMs

These LLMs (Large Language Models) are all licensed for commercial use (e.g., Apache 2.0, MIT, OpenRAIL-M). Contributions welcome!

[🏠Home](README.md)

Due to projects like [Explore the LLMs](https://llm.extractum.io/) specializing in model indexing, the custom list has been removed.

[🏠Home](README.md)

- Want to know which one is "the best"? Have a look at the [🏆 Leaderboards](llm-tools.md#benchmarking) in the Benchmarking section.

[🏠Home](README.md)

Collection of all things "Data Science" leaning towards Marketing & Advertisement (Digital Marketers, Agencies, Web Designers and Web Analysts)

[🏠Home](README.md)

[🏠Home](README.md)

- [ModelScope Text to video synthesis](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis)

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<h1>Awesome ML</h1>

[🏠Home](README.md)

- [EnCodec](https://github.com/facebookresearch/encodec) SOTA deep learning based audio codec supporting both mono 24 kHz audio and stereo 48 kHz audio