From 467ea74a347ad854c96051edb48e09e9da9b7cf3 Mon Sep 17 00:00:00 2001 From: konstantinosKokos Date: Thu, 9 Jan 2025 14:39:14 +0100 Subject: [PATCH] fix indentation --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 97e3977..2b4ae38 100644 --- a/README.md +++ b/README.md @@ -9,21 +9,21 @@ Quill is a WIP attempt at a structurally faithful neural representation of Agda **1.** Clone or download this repository. ```shell - git clone git@github.com:konstantinosKokos/quill.git +git clone git@github.com:konstantinosKokos/quill.git ``` **2.** Initialize a fresh python environment (e.g. using [Miniconda](https://docs.anaconda.com/miniconda/)). ```shell - conda create --name quill python=3.12 +conda create --name quill python=3.12 ``` **3.** Activate the environment, and install the package and its dependencies. ```shell - conda activate quill - pip install . +conda activate quill +pip install . ``` **Note**: *This will install the cpu version of pytorch by default. For CUDA acceleration, you will need to manually @@ -56,18 +56,18 @@ The second part of this process is partially streamlined through a CLI API. First, deploy a server running the model in inference mode: ```shell - agda-quill serve -config PATH_TO_MODEL_CONFIG -weights PATH_TO_MODEL_WEIGHTS +agda-quill serve -config PATH_TO_MODEL_CONFIG -weights PATH_TO_MODEL_WEIGHTS ``` You can then optionally precompute representations of the lemmas defined in various files/libraries: ```shell - agda-quill cache -files ./data/stdlib/Data.List.* +agda-quill cache -files ./data/stdlib/Data.List.* ``` Finally, you can query the model for suggestions, optionally using the cached representations: ```shell - agda-quill query -file ./data/stdlib/Algebra.Construct.NaturalChoice.Min.json --max_suggestions 2 --use_cache +agda-quill query -file ./data/stdlib/Algebra.Construct.NaturalChoice.Min.json --max_suggestions 2 --use_cache ``` ### 🤖 Train a model ...