crabml

crabml is focusing on the reimplementation of GGML using the Rust programming language.

The project is currently an active experiment with the capability to run inference on a Q8_0 quantized Llama 3B model. While the inference is currently not optimized for speed, crabml is a promising endeavor for efficient machine learning inferencing.

Project Goals

crabml is designed with the following objectives in mind:

• Focus on inference only.
• Limit tensor operators to the bare minimum required for LLM inference.
• Achieve fast enough inferencing on cheap hardwares.
• mmap() from day one.
• Prioritize SIMD ahead of GPU.

Usage

Building the Project

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.

Running an Example

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.

License

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