commit WIP model guide docs

docs/model_guide.md

-This is a placeholder.
+# New Model Guide
+
+The `lm-evaluation-harness` is intended to be a model-agnostic framework for evaluating . We provide first-class support for HuggingFace `AutoModelForCausalLM` and `AutoModelForSeq2SeqLM` type models, but
+
+This guide may be of special interest to users who are using the library outside of the repository, via installing the library via pypi and calling `lm_eval.evaluator.evaluate()` to evaluate an existing model.
+
+In order to properly evaluate a given LM, we require implementation of a wrapper class subclassing the `lm_eval.api.model.LM` class, that defines how the Evaluation Harness should interface with your model. This guide walks through how to write this `LM` subclass via adding it to the library!
+
+## Setup
+
+To get started contributing, go ahead and fork the main repo, clone it, create a branch with the name of your task, and install the project requirements in your environment:
+
+```sh
+# 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]"
+```
+
+Now, we'll create a new file where we'll be adding our model:
+
+```sh
+touch lm_eval/models/<my_model_filename>.py
+```
+
+**Tip: this filename should not shadow package names! For example, naming your file `anthropic.py` is disallowed since the API's name on pypi is `anthropic`, but naming it `anthropic_llms.py` works with no problems.**
+
+## Interface
+
+All models must subclass the `lm_eval.api.model.LM` class.
+
+The LM class enforces a common interface via which we can extract responses from a model:
+
+```python
+class MyCustomLM(LM):
+    #...
+    def loglikelihood(self, requests):
+
+
+    def loglikelihood_rolling(self, requests):
+
+
+    def greedy_until(self, requests):
+    #...
+```
+
+We support 
+
+The three types of 
+
+
+
+smth smth tokenizer-agnostic
+
+3 reqtypes
+- greedy_until, and the arguments passed to it
+
+- loglikelihood, and args passed to it
+
+- loglikelihood_rolling, and args passed to it
+
+
+## Registration
+
+Congrats on implementing your model! Now it's time to test it out. 
+
+To make your model usable via the command line interface to `lm-eval` using `main.py`, you'll need to tell `lm-eval` what your model's name is. 
+
+This is done via a *decorator*, `lm_eval.api.registry.register_model`. Using `register_model()`, one can both tell the package what the model's name(s) to be used are when invoking it with `python main.py --model <name>` and alert `lm-eval` to the model's existence.
+
+```python
+from lm_eval.api.registry import register_model
+
+@register_model("<name1>", "<name2>")
+class MyCustomLM(LM):
+```
+
+Using this decorator results in the class being added to an accounting of the usable LM types maintained internally to the library at `lm_eval.api.registry.MODEL_REGISTRY`. See `lm_eval.api.registry` for more detail on what sorts of registries and decorators exist in the library!
+
+
+
+## Other
+
+**Pro tip**: In order to make the Evaluation Harness overestimate total runtimes rather than underestimate it, HuggingFace models come in-built with the ability to provide responses on data points in *descending order by total input length* via `lm_eval.utils.Reorderer`. Take a look at `lm_eval.models.hf_causal.HFLM` to see how this is done, and see if you can implement it in your own model!
+
+## Conclusion
+
+After reading this guide, you should be able to add new model APIs or implementations to the Eval Harness library! 
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