Model card readability

Small edits to improve the readability of the model card.

README.md

@@ -1,6 +1,6 @@
 # Model description
 
-This repo contains over 500 model checkpoints ranging in size from 20M parameters up to 3.3B parameters and FLOP budgets from 2e17 to 1e21 FLOPs across 6 different pretraining datasets.
+This repository contains over 500 model checkpoints ranging in size from 20M parameters up to 3.3B parameters and FLOP budgets from 2e17 to 1e21 FLOPs across 6 different pretraining datasets.
 
 Each subdirectory name contains four different parameters to identify the model in that subdirectory:
 
@@ -11,13 +11,13 @@ Each subdirectory name contains four different parameters to identify the model
 
 For example, a model trained on `starcoder` with 1.1e08 parameters on 3.0e08 tokens for a total of 2.0e17 FLOPs would have the name: `L2L_starcoder_N1.1e08_D3.0e08_C2.0e17/`
 
-Full training details for the models can be found in the training repo or paper.
+Full training details for the models can be found in the [training repository](https://github.com/KempnerInstitute/loss-to-loss-olmo/) or paper.
 
 # How to load a model
 
-First, follow the instruction to install our fork of the [OLMo](https://github.com/allenai/OLMo) package from here: https://github.com/KempnerInstitute/loss-to-loss-olmo/tree/main
+First, follow the instructions in the [training repository](https://github.com/KempnerInstitute/loss-to-loss-olmo/) to install our fork of the [OLMo](https://github.com/allenai/OLMo) package.
 
-With this installed, you can then use the huggingface hub and transformers to load a model with the following snippet:
+With this installed, you can then use the `huggingface_hub` and `transformers` packages to load a model with the following snippet:
 ```python
 from olmo.model import HFMixinOLMo
 from huggingface_hub import snapshot_download