@@ -145,6 +126,9 @@ For more advanced users or even larger models, we allow for the following argume
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These two options (`accelerate launch` and `parallelize=True`) are mutually exclusive.
These two options (`accelerate launch` and `parallelize=True`) are mutually exclusive.
**Note: we do not currently support multi-node evaluations natively, and advise using either an externally hosted server to run inference requests against, or creating a custom integration with your distributed framework [as is done for the GPT-NeoX library](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py).**
### Tensor + Data Parallel and Optimized Inference with `vLLM`
### Tensor + Data Parallel and Optimized Inference with `vLLM`
We also support vLLM for faster inference on [supported model types](https://docs.vllm.ai/en/latest/models/supported_models.html), especially faster when splitting a model across multiple GPUs. For single-GPU or multi-GPU — tensor parallel, data parallel, or a combination of both — inference, for example:
We also support vLLM for faster inference on [supported model types](https://docs.vllm.ai/en/latest/models/supported_models.html), especially faster when splitting a model across multiple GPUs. For single-GPU or multi-GPU — tensor parallel, data parallel, or a combination of both — inference, for example:
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| Mamba | :heavy_check_mark: | `mamba_ssm` | [Mamba architecture Language Models via the `mamba_ssm` package](https://huggingface.co/state-spaces) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
| Mamba | :heavy_check_mark: | `mamba_ssm` | [Mamba architecture Language Models via the `mamba_ssm` package](https://huggingface.co/state-spaces) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
| Your local inference server! | :heavy_check_mark: | `local-completions` or `local-chat-completions` (using `openai-chat-completions` model type) | Any server address that accepts GET requests using HF models and mirror's OpenAI's ChatCompletions interface | `generate_until` || ... |
| Huggingface Optimum (Causal LMs) | ✔️ | `openvino` | Any decoder-only AutoModelForCausalLM converted with Huggingface Optimum into OpenVINO™ Intermediate Representation (IR) format|`generate_until`, `loglikelihood`, `loglikelihood_rolling`| ... |
| `local-completions` (using `openai-completions` model type) | Any server address that accepts GET requests using HF models and mirror's OpenAI's Completions interface | `generate_until` | | ... |
| Your local inference server! | :heavy_check_mark: | `local-completions` or `local-chat-completions` (using `openai-chat-completions` model type) | Any server address that accepts GET requests using HF models and mirror's OpenAI's Completions or ChatCompletions interface | `generate_until` | | ... |
Models which do not supply logits or logprobs can be used with tasks of type `generate_until` only, while models that are local or APIs that supply logprobs/logits can be run on all task types: `generate_until`, `loglikelihood`, `loglikelihood_rolling`, and `multiple_choice`.
Models which do not supply logits or logprobs can be used with tasks of type `generate_until` only, while local models, or APIs that supply logprobs/logits of their prompts, can be run on all task types: `generate_until`, `loglikelihood`, `loglikelihood_rolling`, and `multiple_choice`.
For more information on the different task `output_types` and model request types, see [our documentation](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/model_guide.md#interface).
For more information on the different task `output_types` and model request types, see [our documentation](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/model_guide.md#interface).
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@@ -203,6 +187,8 @@ A number of other libraries contain scripts for calling the eval harness through
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@@ -203,6 +187,8 @@ A number of other libraries contain scripts for calling the eval harness through
To create your own custom integration you can follow instructions from [this tutorial](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage).
To create your own custom integration you can follow instructions from [this tutorial](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage).
### Additional Features
### Additional Features
> [!Note]
> For tasks unsuitable for direct evaluation — either due risks associated with executing untrusted code or complexities in the evaluation process — the `--predict_only` flag is available to obtain decoded generations for post-hoc evaluation.
If you have a Metal compatible Mac, you can run the eval harness using the MPS back-end by replacing `--device cuda:0` with `--device mps` (requires PyTorch version 2.1 or higher).
If you have a Metal compatible Mac, you can run the eval harness using the MPS back-end by replacing `--device cuda:0` with `--device mps` (requires PyTorch version 2.1 or higher).
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For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md) guide in our documentation!
For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md) guide in our documentation!
> [!Tip]
> Running lm-evaluation-harness as an external library and can't find (almost) any tasks available? Run `lm_eval.tasks.initialize_tasks()` to load the library's stock tasks before calling `lm_eval.evaluate()` or `lm_eval.simple_evaluate()` !
## Visualizing Results
## Visualizing Results
You can use [Zeno](https://zenoml.com) to visualize the results of your eval harness runs.
You can use [Zeno](https://zenoml.com) to visualize the results of your eval harness runs.
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The best way to get support is to open an issue on this repo or join the [EleutherAI Discord server](https://discord.gg/eleutherai). The `#lm-thunderdome` channel is dedicated to developing this project and the `#release-discussion` channel is for receiving support for our releases. If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!
The best way to get support is to open an issue on this repo or join the [EleutherAI Discord server](https://discord.gg/eleutherai). The `#lm-thunderdome` channel is dedicated to developing this project and the `#release-discussion` channel is for receiving support for our releases. If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!
## Optional Extras
Extras dependencies can be installed via `pip install -e ".[NAME]"`
Welcome and thank you for your interest in the LM Evaluation Harness! We welcome contributions and feedback and appreciate your time spent with our library, and hope you find it useful!
We intend LM Evaluation Harness to be a broadly useful and
## Important Resources
There are several places information about LM Evaluation Harness is located:
- We occasionally use [GitHub Milestones](https://github.com/EleutherAI/lm-evaluation-harness/milestones) to track progress toward specific near-term version releases.
- We maintain a [Project Board](https://github.com/orgs/EleutherAI/projects/25) for tracking current work items and PRs, and for future roadmap items or feature requests.
- Further discussion and support conversations are located in the #lm-thunderdome channel of the [EleutherAI discord](discord.gg/eleutherai).
## Code Style
LM Evaluation Harness uses [ruff](https://github.com/astral-sh/ruff) for linting via [pre-commit](https://pre-commit.com/).
You can install linters and dev tools via
```pip install lm_eval[dev]```
Then, run
```pre-commit install```
in order to ensure linters and other checks will be run upon committing.
## Testing
We use [pytest](https://docs.pytest.org/en/latest/) for running unit tests. All library unit tests can be run via:
We ask that new contributors agree to a Contributor License Agreement affirming that EleutherAI has the rights to use your contribution to our library.
First-time pull requests will have a reply added by @CLAassistant containing instructions for how to confirm this, and we require it before merging your PR.
## Contribution Best Practices
We recommend a few best practices to make your contributions or reported errors easier to assist with.
**For Pull Requests:**
- PRs should be titled descriptively, and be opened with a brief description of the scope and intent of the new contribution.
- New features should have appropriate documentation added alongside them.
- Aim for code maintainability, and minimize code copying.
- If opening a task, try to share test results on the task using a publicly-available model, and if any public results are available on the task, compare to them.
**For Feature Requests:**
- Provide a short paragraph's worth of description. What is the feature you are requesting? What is its motivation, and an example use case of it? How does this differ from what is currently supported?
**For Bug Reports**:
- Provide a short description of the bug.
- Provide a *reproducible example*--what is the command you run with our library that results in this error? Have you tried any other steps to resolve it?
- Provide a *full error traceback* of the error that occurs, if applicable. A one-line error message or small screenshot snippet is unhelpful without the surrounding context.
- Note what version of the codebase you are using, and any specifics of your environment and setup that may be relevant.
**For Requesting New Tasks**:
- Provide a 1-2 sentence description of what the task is and what it evaluates.
- Provide a link to the paper introducing the task.
- Provide a link to where the dataset can be found.
- Provide a link to a paper containing results on an open-source model on the task, for use in comparisons and implementation validation.
- If applicable, link to any codebase that has implemented the task (especially the original publication's codebase, if existent).
## How Can I Get Involved?
To quickly get started, we maintain a list of good first issues, which can be found [on our project board](https://github.com/orgs/EleutherAI/projects/25/views/8) or by [filtering GH Issues](https://github.com/EleutherAI/lm-evaluation-harness/issues?q=is%3Aopen+label%3A%22good+first+issue%22+label%3A%22help+wanted%22). These are typically smaller code changes or self-contained features which can be added without extensive familiarity with library internals, and we recommend new contributors consider taking a stab at one of these first if they are feeling uncertain where to begin.
There are a number of distinct ways to contribute to LM Evaluation Harness, and all are extremely helpful! A sampling of ways to contribute include:
- **Implementing and verifying new evaluation tasks**: Is there a task you'd like to see LM Evaluation Harness support? Consider opening an issue requesting it, or helping add it! Verifying and cross-checking task implementations with their original versions is also a very valuable form of assistance in ensuring standardized evaluation.
- **Improving documentation** - Improvements to the documentation, or noting pain points / gaps in documentation, are helpful in order for us to improve the user experience of the library and clarity + coverage of documentation.
- **Testing and devops** - We are very grateful for any assistance in adding tests for the library that can be run for new PRs, and other devops workflows.
- **Adding new modeling / inference library integrations** - We hope to support a broad range of commonly-used inference libraries popular among the community, and welcome PRs for new integrations, so long as they are documented properly and maintainable.
- **Proposing or Contributing New Features** - We want LM Evaluation Harness to support a broad range of evaluation usecases. If you have a feature that is not currently supported but desired, feel free to open an issue describing the feature and, if applicable, how you intend to implement it. We would be happy to give feedback on the cleanest way to implement new functionalities and are happy to coordinate with interested contributors via GH discussions or via discord.
We hope that this has been helpful, and appreciate your interest in contributing! Further questions can be directed to [our Discord](discord.gg/eleutherai).
@@ -44,6 +44,8 @@ This mode supports a number of command-line arguments, the details of which can
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*`--include_path` : Accepts a path to a folder. If passed, then all YAML files containing `lm-eval`` compatible task configurations will be added to the task registry as available tasks. Used for when one is writing config files for their own task in a folder other than `lm_eval/tasks/`
*`--include_path` : Accepts a path to a folder. If passed, then all YAML files containing `lm-eval`` compatible task configurations will be added to the task registry as available tasks. Used for when one is writing config files for their own task in a folder other than `lm_eval/tasks/`
* `--predict_only`: Generates the model outputs without computing metrics. Use with `--log_samples` to retrieve decoded results.
## External Library Usage
## External Library Usage
We also support using the library's external API for use within model training loops or other scripts.
We also support using the library's external API for use within model training loops or other scripts.
`aggregation` and `higher_is_better` can optionally be left out to default to the manually-set defaults if using a natively supported metric, otherwise it must be defined explicitly (for example, when using a custom metric implemented as a function).
`aggregation` and `higher_is_better` can optionally be left out to default to the manually-set defaults if using a natively supported metric, otherwise it must be defined explicitly (for example, when using a custom metric implemented as a function).
For a full list of natively supported metrics and aggregation functions see `docs/advanced_task_guide.md`. All metrics supported in [HuggingFace Evaluate](https://github.com/huggingface/evaluate/tree/main/metrics) can also be used, and will be loaded if a given metric name is not one natively supported in `lm-eval` or `hf_evaluate` is set to `true`.
For a full list of natively supported metrics and aggregation functions see [`docs/task_guide.md`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/task_guide.md). All metrics supported in [HuggingFace Evaluate](https://github.com/huggingface/evaluate/tree/main/metrics) can also be used, and will be loaded if a given metric name is not one natively supported in `lm-eval` or `hf_evaluate` is set to `true`.
### Optional, More Advanced Setup
### Optional, More Advanced Setup
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* Do you expect to compute metrics after applying multiple such processing steps on your model outputs?
* Do you expect to compute metrics after applying multiple such processing steps on your model outputs?
* Does your task rely on metrics that need a custom implementation?
* Does your task rely on metrics that need a custom implementation?
For more detail on the task system and advanced features, see `docs/advanced_task_guide.md` . If none of the above sound like they apply to your task, it's time to continue onto checking your task performance!
For more detail on the task system and advanced features, see [`docs/task_guide.md`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/task_guide.md) . If none of the above sound like they apply to your task, it's time to continue onto checking your task performance!
