
amperecomputingai/llama.cpp!llama.cpp
!llama.cpp pull count
Ampere® optimized build of llama.cpp with full support for rich collection of GGUF models available on HuggingFace: GGUF models Ampere collections
This Docker image can be run on bare metal Ampere® CPUs and Ampere® based VMs available in the cloud.
Release notes and binary executables are available on our GitHub
Default entrypoint runs the server binary of llama.cpp, mimicking behavior of original llama.cpp server image: docker image
To launch shell instead, do this:
bashsudo docker run --privileged=true --name llama --entrypoint /bin/bash -it amperecomputingai/llama.cpp:latest
Quick start example will be presented at docker container launch:
!quick start
Make sure to visit us at Ampere Solutions Portal!
Ampere® optimized build of llama.cpp provides support for two new quantization methods, Q4_K_4 and Q8R16, offering model size and perplexity similar to Q4_K and Q8_0, respectively, but performing up to 1.5-2x faster on inference.
First, you'll need to convert the model to the GGUF format using this script:
bashpython3 convert-hf-to-gguf.py [path to the original model] --outtype [f32, f16, bf16 or q8_0] --outfile [output path]
For example:
bashpython3 convert-hf-to-gguf.py path/to/llama2 --outtype f16 --outfile llama-2-7b-f16.gguf
Next, you can quantize the model using the following command:
bash./llama-quantize [input file] [output file] [quantization method]
For example:
bash./llama-quantize llama-2-7b-f16.gguf llama-2-7b-Q8R16.gguf Q8R16
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