ggml is a high-performance tensor library written in C that supports large models and high performance on commodity hardware.

What platforms is ggml optimized for?

ggml is optimized for Apple Silicon and x86 architectures and supports WebAssembly for web deployment.

What unique features does ggml offer?

ggml offers 16-bit float support, integer quantization, automatic differentiation, built-in optimization algorithms, zero memory allocations during runtime, and guided language output support.

What are some use cases for ggml?

ggml is used for applications such as short voice command detection on Raspberry Pi, running multiple model instances on Apple devices, and deploying high-efficiency models on GPUs.

Yes, ggml is open-source and available under the MIT license. The development process is open, and community contributions are encouraged.

What are some related projects to ggml?

Related projects include whisper.cpp for high-performance speech recognition and llama.cpp for efficient inference of Meta's LLaMA language model.

How can I contribute to ggml?

You can contribute to the ggml codebase or financially support the project by becoming a sponsor to contributors of llama.cpp, whisper.cpp, or ggml.

ggml.ai was founded by Georgi Gerganov with pre-seed funding from Nat Friedman and Daniel Gross.

Are there career opportunities at ggml.ai?

Yes, ggml.ai is seeking full-time developers who share their vision and have contributed to related projects. Interested candidates can contact jobs@ggml.ai.

How can I contact ggml.ai for business inquiries?

For business-related topics, you can contact ggml.ai through the information provided on their website.

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GGML

Name: GGML
Brand: GGML
Rating: 1 (1 reviews)
Author: GGML

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Last updated: August 8, 2024

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ggml is a machine learning tensor library written in C that provides high performance and large model support on commodity hardware. The library supports 16-bit floats, integer quantization, automatic differentiation, and built-in optimization algorithms like ADAM and L-BFGS. It is optimized for Apple Silicon, utilizes AVX/AVX2 intrinsics on x86 architectures, offers WebAssembly support, and performs zero memory allocations during runtime. Use cases include voice command detection on Raspberry Pi, running multiple instances on Apple devices, and deploying high-efficiency models on GPUs. ggml promotes simplicity, openness, and exploration while fostering community contributions and innovation.

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GGML's Top Features

Written in C

16-bit float support

Integer quantization support (4-bit, 5-bit, 8-bit)

Automatic differentiation

Built-in optimization algorithms (ADAM, L-BFGS)

Optimized for Apple Silicon

Supports AVX/AVX2 intrinsics on x86 architectures

WebAssembly and WASM SIMD support

No third-party dependencies

Zero memory allocations during runtime

Guided language output support

Frequently asked questions about GGML

GGML's pricing

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