Tracking progress on revamping documentation pages for the refactor of LM-Evaluation-Harness.
# Eval Harness Documentation
Welcome to the docs for the LM Evaluation Harness!
## Table of Contents
* To learn how to add a new library, API, or model type to the library, as well as a quick explainer on the types of ways to evaluate an LM, see the [Model Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/model_guide.md).
* For a crash course on adding new tasks to the library, see our [New Task Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/new_task_guide.md).
* To learn more about pushing the limits of task configuration that the Eval Harness supports, see the [Advanced Task Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/advanced_task_guide.md).
## Progress on Revamp
Tracking progress on revamping documentation pages for the refactor of LM-Evaluation-Harness.
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:
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
classMyCustomLM(LM):
#...
defloglikelihood(self,requests):
defloglikelihood_rolling(self,requests):
defgreedy_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
fromlm_eval.api.registryimportregister_model
@register_model("<name1>","<name2>")
classMyCustomLM(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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doc_to_text:"{{question_stem}}"
doc_to_target:"{{gold}}"# this will be cast to an int.
