Merge pull request #1065 from EleutherAI/haileyschoelkopf-patch-4

docs/interface.md

@@ -10,7 +10,7 @@ Equivalently, running the library can be done via the `lm-eval` entrypoint at th
 
 This mode supports a number of command-line arguments, the details of which can be also be seen via running with `-h` or `--help`:
 
-* `--model` : Selects which model type or provider is evaluated. Must be a string corresponding to the name of the model type/provider being used. See [the main README](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor#commercial-apis) for a full list of enabled model names and supported libraries or APIs.
+* `--model` : Selects which model type or provider is evaluated. Must be a string corresponding to the name of the model type/provider being used. See [the main README](https://github.com/EleutherAI/lm-evaluation-harness/tree/main#commercial-apis) for a full list of enabled model names and supported libraries or APIs.
 
 * `--model_args` : Controls parameters passed to the model constructor. Accepts a string containing comma-separated keyword arguments to the model class of the format `"arg1=val1,arg2=val2,..."`, such as, for example `--model_args pretrained=EleutherAI/pythia-160m,dtype=float32`. For a full list of what keyword arguments, see the initialization of the `lm_eval.api.model.LM` subclass, e.g. [`HFLM`](https://github.com/EleutherAI/lm-evaluation-harness/blob/365fcda9b85bbb6e0572d91976b8daf409164500/lm_eval/models/huggingface.py#L66)
 
@@ -51,7 +51,7 @@ We also support using the library's external API for use within model training l
 `lm_eval` supplies two functions for external import and use: `lm_eval.evaluate()` and `lm_eval.simple_evaluate()`.
 
 
-`simple_evaluate()` can be used by simply creating an `lm_eval.api.model.LM` subclass that implements the methods described in the [Model Guide](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/docs/model_guide.md), and wrapping your custom model in that class as follows:
+`simple_evaluate()` can be used by simply creating an `lm_eval.api.model.LM` subclass that implements the methods described in the [Model Guide](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs/model_guide.md), and wrapping your custom model in that class as follows:
 
 ```python
 import lm_eval