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- Most kernels just copy themselfs to get the shapes correct - Matmul works only in 1 case and simply empty allocates otherwise - Logits and randomized to make the demo finish itself. Performance is quite bad (30ms/token), but lot's of prints and allocs and some actual sending to metal. Couln't get it super high by removing the obvious blockers (println + the actual running matmuls). Allocations takes between 1us and 100us and seems very stable, Maybe metal doesn't really have a smart allocator and we'll need to own it.
candle-quantized-llama: Fast Inference of quantized LLaMA models
This example provides a quantized LLaMA model similar to llama.cpp. This is based on candle built-in quantization methods. Supported features include:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support.
- SIMD optimizations on Apple Silicon and x86.
- Support using the
ggufandggmlfile formats.
The weights are automatically downloaded for you from the HuggingFace
Hub on the first run. There are various command line
flags to use local files instead, run with --help to learn about them.
Running some example.
cargo run --example quantized --release -- --prompt "The best thing about coding in rust is "
> avx: true, neon: false, simd128: false, f16c: true
> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
> loaded 291 tensors (3.79GB) in 2.17s
> params: HParams { n_vocab: 32000, n_embd: 4096, n_mult: 256, n_head: 32, n_layer: 32, n_rot: 128, ftype: 2 }
> The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
Command-line flags
Run with --help to see all options.
--which: specify the model to use, e.g.7b,13-chat,7b-code.--prompt interactive: interactive mode where multiple prompts can be entered.--model mymodelfile.gguf: use a local model file rather than getting one from the hub.