Skip to content

docs: fix references to config files #430

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
le1nux merged 1 commit into from
Feb 13, 2026
+3 −3
Merged

docs: fix references to config files #430

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -174,7 +174,7 @@ Explanation:

* `$(which modalities) run`: This part dynamically finds the path to the Modalities executable and runs it. The run command triggers the main process to start the training.

* `--config_file_path config_files/training/config_lorem_ipsum_long_fsdp2.yaml`: The --config_file_path argument provides the path to the configuration file for the training job. In the example above, it is given by `config_files/training/config_lorem_ipsum_long_fsdp2.yaml`. A configuraton file contains an exhaustive parameterization for all the training components (e.g., dataset, model, optimizer, etc.), making training fully reproducible. An example configuration file can be found [here](tutorials/getting_started/example_config.yaml), and a complete list of components available in Modalities is provided [here](docs/components/components.md).
* `--config_file_path config_files/training/config_lorem_ipsum_long_fsdp2.yaml`: The --config_file_path argument provides the path to the configuration file for the training job. In the example above, it is given by `config_files/training/config_lorem_ipsum_long_fsdp2.yaml`. A configuraton file contains an exhaustive parameterization for all the training components (e.g., dataset, model, optimizer, etc.), making training fully reproducible. An example configuration file can be found [here](tutorials/getting_started/configs/example_config.yaml), and a complete list of components available in Modalities is provided [here](docs/components/components.md).

If you are a VSCode user, you may want to add this to your `launch.json`:
```json
Expand Down Expand Up @@ -221,7 +221,7 @@ The `modalities data create_raw_index` command triggers the process of creating

### Raw Training Dataset Tokenization

Tokenization is the process of converting raw text data into a sequence of tokens that can be used as input to the model. The tokenization requires a configuration file, fully describing the tokenization process, making it fully reproducible. An example tokenization config can be found [here](tutorials/getting_started/example_dataset_config_train.yaml).
Tokenization is the process of converting raw text data into a sequence of tokens that can be used as input to the model. The tokenization requires a configuration file, fully describing the tokenization process, making it fully reproducible. An example tokenization config can be found [here](tutorials/getting_started/configs/example_dataset_config_train.yaml).

Example:
```sh
Expand All @@ -230,7 +230,7 @@ modalities data pack_encoded_data configs/tokenization_config.yaml

### Inference

For inference on a model checkpoint, we have to pass a configuration file that specifies the full inference setup. An example inference config can be found [here](tutorials/getting_started/example_text_generation_config.yaml).
For inference on a model checkpoint, we have to pass a configuration file that specifies the full inference setup. An example inference config can be found [here](tutorials/getting_started/configs/example_text_generation_config.yaml).

Example:

Expand Down