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GitHub - microsoft/Phi-3CookBook: This is a Phi-3 book for getting started with Phi-3 Phi-3, a family of open AI models developed by Microsoft Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks

This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks. - GitHub - microsoft/Phi-3CookBook: This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.

GitHub - microsoft/monitors4codegen: Code and Data artifact for NeurIPS 2023 paper - Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context `multispy` is a lsp client library in Python intended to be used to build applications around language servers

GitHub - microsoft/monitors4codegen: Code and Data artifact for NeurIPS 2023 paper - Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context `multispy` is a lsp client library in Python intended to be used to build applications around language servers
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GitHub - microsoft/aici: AICI: Prompts as (Wasm) Programs

GitHub - microsoft/FASTER: Fast persistent recoverable log and key-value store + cache, in C# and C++

Fast persistent recoverable log and key-value store + cache, in C# and C++. - microsoft/FASTER

GitHub - microsoft/LLMLingua: To speed up LLMs inference and enhance LLM s perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss

To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. - GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.

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