crabml is a llama.cpp compatible (and equally fast!) AI inference engine written in 🦀 Rust, which runs everywhere with the help of 🎮 WebGPU.

crabml is designed with the following objectives in mind:

• 🤖 Focus solely on inference.
• 🎮 Runs on browsers, desktops, and servers everywhere with the help of WebGPU.
• ⏩ SIMD-accelerated inference on inexpensive hardware.
• 💼 mmap() from day one, minimized memory requirement with various quantization support.
• 👾 Hackable & embeddable.

crabml supports the following models in GGUF format:

• 🦙 Llama
• 🦙 CodeLlama
• 🦙 Gemma
• 〽️ Mistral
• 🚄 On the way: Mistral MoE, Phi, QWen, StarCoder, Llava, and more!

For more information, you can visit How to Get GGUF Models to learn how to download the GGUF files you need.

crabml supports the following quantization methods on CPUs with SIMD acceleration for ARM (including Apple Silicon) and x86 architectures:

As the table above suggests, WebGPU-accelerated quantizations are still under busy development, and Q8_0， Q4_0， Q4_1 are currently the most recommended quantization methods on CPUs!

To build crabml, set the RUSTFLAGS environment variable to enable specific target features. For example, to enable NEON on ARM architectures, use RUSTFLAGS="-C target-feature=+neon". Then build the project with the following command:

cargo build --release

This command compiles the project in release mode, which optimizes the binary for performance.

After building the project, you can run an example inference by executing the crabml-cli binary with appropriate arguments. For instance, to use the tinyllamas-stories-15m-f32.gguf model to generate text based on the prompt "captain america", execute the command below:

./target/release/crabml-cli \
  -m ./testdata/tinyllamas-stories-15m-f32.gguf \
  "captain america" --steps 100 \
  -t 0.8 -p 1.0

In this command:

• -m specifies the checkpoint file.
• --steps defines the number of tokens to generate.
• -t sets the temperature, which controls the randomness of the output.
• -p sets the probability of sampling from the top-p.

This contribution is licensed under Apache License, Version 2.0, (LICENSE or http://www.apache.org/licenses/LICENSE-2.0)