Merge pull request #1066 from EleutherAI/haileyschoelkopf-patch-4

Updating docs hyperlinks 

docs/model_guide.md

@@ -12,7 +12,6 @@ To get started contributing, go ahead and fork the main repo, clone it, create a
 # After forking...
 git clone https://github.com/<YOUR-USERNAME>/lm-evaluation-harness.git
 cd lm-evaluation-harness
-git checkout big-refactor
 git checkout -b <model-type>
 pip install -e ".[dev]"
 ```
@@ -46,7 +45,7 @@ class MyCustomLM(LM):
         #...
     #...
 ```
-Where `Instance` is a dataclass defined in [`lm_eval.api.instance`](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/lm_eval/api/instance.py) with property `args` of request-dependent type signature described below.
+Where `Instance` is a dataclass defined in [`lm_eval.api.instance`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/api/instance.py) with property `args` of request-dependent type signature described below.
 
 We support three types of requests, consisting of different interactions / measurements with an autoregressive LM.
 

docs/new_task_guide.md

@@ -2,9 +2,9 @@
 
 `lm-evaluation-harness` is a framework that strives to support a wide range of zero- and few-shot evaluation tasks on autoregressive language models (LMs).
 
-This documentation page provides a walkthrough to get started creating your own task, on the `big-refactor` branch of the repository (which will be v0.4.0 in the future.)
+This documentation page provides a walkthrough to get started creating your own task, in `lm-eval` versions v0.4.0 and later.
 
-A more interactive tutorial is available as a Jupyter notebook [here](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/examples/lm-eval-overview.ipynb).
+A more interactive tutorial is available as a Jupyter notebook [here](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/examples/lm-eval-overview.ipynb).
 
 ## Setup
 
@@ -14,12 +14,11 @@ If you haven't already, go ahead and fork the main repo, clone it, create a bran
 # After forking...
 git clone https://github.com/<YOUR-USERNAME>/lm-evaluation-harness.git
 cd lm-evaluation-harness
-git checkout big-refactor
 git checkout -b <task-name>
 pip install -e ".[dev]"
 ```
 
-In this document, we'll walk through the basics of implementing a static benchmark evaluation in two formats: a *generative* task which requires sampling text from a model, such as [`gsm8k`](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/lm_eval/tasks/gsm8k/gsm8k.yaml), and a *discriminative*, or *multiple choice*, task where the model picks the most likely of several fixed answer choices, such as [`sciq`](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/lm_eval/tasks/sciq/sciq.yaml).
+In this document, we'll walk through the basics of implementing a static benchmark evaluation in two formats: a *generative* task which requires sampling text from a model, such as [`gsm8k`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/gsm8k/gsm8k.yaml), and a *discriminative*, or *multiple choice*, task where the model picks the most likely of several fixed answer choices, such as [`sciq`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/sciq/sciq.yaml).
 
 ## Creating a YAML file