update pyproject.toml

cohere-ai · Oct 31, 2024 · d90c272 · d90c272
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diff --git a/README.md b/README.md
@@ -1,5 +1,5 @@
 # cohere-finetune
-Cohere-finetune is a tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's family of Command R models on users' own data to serve their own use cases.
+Cohere-finetune is a tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's models on users' own data to serve their own use cases.
 
 Currently, we support the following base models for fine-tuning:
 - [Cohere's Command R in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
@@ -121,7 +121,7 @@ The `<finetune_name>` must be exactly the same as that used in [Step 3](#step-3-
 
 | Hyperparameter               | Definition                                                                                               | Default value        | Valid values or range                                                                                                                                     |
 |:-----------------------------|:---------------------------------------------------------------------------------------------------------|:---------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
-| base_model_name_or_path      | The name of the base model or the path to the checkpoint of a customized base model that you will train  | "command-r-08-2024"  | "command-r", "command-r-08-2024", "command-r-plus", "command-r-plus-08-2024", "aya-expanse-8b", "aya-expanse-32b", "/opt/finetuning/<path_to_checkpoint>" |
+| base_model_name_or_path      | The name of the base model or the path to the checkpoint of a customized base model                      | "command-r-08-2024"  | "command-r", "command-r-08-2024", "command-r-plus", "command-r-plus-08-2024", "aya-expanse-8b", "aya-expanse-32b", "/opt/finetuning/<path_to_checkpoint>" |
 | parallel_strategy            | The strategy to use multiple GPUs for training                                                           | "fsdp"               | "vanilla", "fsdp", "deepspeed"                                                                                                                            |
 | finetune_strategy            | The strategy to train the model                                                                          | "lora"               | "lora"                                                                                                                                                    |
 | use_4bit_quantization        | Whether to apply 4-bit quantization to the model                                                         | "false"              | "false", "true"                                                                                                                                           |
@@ -138,7 +138,7 @@ The `<finetune_name>` must be exactly the same as that used in [Step 3](#step-3-
 | lora_config.rslora           | Whether to use rank-stabilized LoRA (rsLoRA)                                                             | "true"               | "false", "true"                                                                                                                                           |
 
 Note that you can set `base_model_name_or_path` as either the name of a supported model or the path to the checkpoint of a customized base model. However, if it is a path, the following requirements must be satisfied:
-- The customized base model must have the same architecture as one of the supported models (the weights can be different). For example, it can be a model obtained by fine-tuning a supported model like "command-r-08-2024".
+- The customized base model must have the same architecture as one of the supported models (the weights can be different). For example, it can be a model obtained by fine-tuning a supported model like Command R 08-2024.
 - The checkpoint of the customized base model must be a HuggingFace checkpoint like [this](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024/tree/main). It must contain a `config.json` file, as we will use it to infer the type of your model.
 - The checkpoint must be put in `<finetune_root_dir>/<path_to_checkpoint>` on your host, and the `base_model_name_or_path` must be in the format of `/opt/finetuning/<path_to_checkpoint>` (recall that we mount `<finetune_root_dir>` on the host to `/opt/finetuning` in the container).
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "cohere-finetune"
 version = "1.0.0"
-description = "A tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's family of Command R models"
+description = "A tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's models"
 authors = [{name = "Cohere"}]
 dependencies = [
     "accelerate==0.34.0",