base on a fast cross platform AI inference engine 🤖 using Rust 🦀 and WebGPU 🎮 # crabml [](https://discord.gg/wbzqddT3QC) `crabml` is a llama.cpp compatible (and equally fast!) AI inference engine written in 🦀 **Rust**, which runs everywhere with the help of 🎮 **WebGPU**. ## Project Goals `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. ## Supported Models `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](https://github.com/crabml/crabml/blob/main/docs/how-to-get-gguf-models.md) to learn how to download the GGUF files you need. ## Supported Quantization Methods `crabml` supports the following quantization methods on CPUs with SIMD acceleration for ARM (including Apple Silicon) and x86 architectures: | | Bits | Native CPU | NEON | AVX2 | RISC-V SIMD | WebGPU | |------|--------|------------|------|------|-------------|--------| | Q8_0 | 8 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q8_K | 8 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q6_K | 6 bits | ✅ | WIP | WIP | WIP | WIP | | Q5_0 | 5 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q5_1 | 5 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q5_K | 5 bits | ✅ | WIP | WIP | WIP | WIP | | Q4_0 | 4 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q4_1 | 4 bits | ✅ | ✅ | ✅ | WIP | WIP | | Q4_K | 4 bits | ✅ | WIP | WIP | WIP | WIP | | Q3_K | 3 bits | ✅ | WIP | WIP | WIP | WIP | | Q2_K | 2 bits | ✅ | WIP | WIP | WIP | WIP | 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! ## 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: ```bash 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: ```bash ./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](LICENSE) or <http://www.apache.org/licenses/LICENSE-2.0>) ", Assign "at most 3 tags" to the expected json: {"id":"8248","tags":[]} "only from the tags list I provide: []" returns me the "expected json"