@@ -49,6 +48,13 @@ To support loading GPTQ quantized models, install the package with the `gptq` ex
pip install-e".[gptq]"
```
To install the package with all extras, run
```bash
pip install-e".[all]"
```
## Support
The best way to get support is to open an issue on this repo or join the EleutherAI discord server](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.
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@@ -93,6 +99,8 @@ python main.py \
--batch_size auto:4
```
Alternatively, you can use `lm-eval` instead of `python main.py` to call lm eval from anywhere.
### Multi-GPU Evaluation with Hugging Face `accelerate`
To parallelize evaluation of HuggingFace models across multiple GPUs, we allow for two different types of multi-GPU evaluation.
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@@ -128,30 +136,43 @@ Using this setting helps for massive models like BLOOM which require, or to avoi
**Note that this option requires launching evaluation via `python main.py` rather than `accelerate launch main.py`.**
To use `accelerate` with the `lm-eval` command, use
```
accelerate launch --no_python lm-eval --model ...
```
### Commercial APIs
Our library also supports language models served via the OpenAI API:
Our library also supports the evaluation of models served via several commercial APIs, and hope to implement support for common performant local/self-hosted inference servers.
A full accounting of the supported and planned libraries + APIs can be seen below:
| API or Inference Server | Implemented? | `--model <xxx>` name | Models supported: | Request Types: |
| vLLM | :x: Not yet - needs help! | N/A | All HF models | `greedy_until` (no logprobs) |
| Your inference server here! | ... | ... | ... | ... | | ... |
It is on our roadmap to create task variants designed to enable models which do not serve logprobs/loglikelihoods to be compared with generation performance of open-source models.
Our library supports language models served via the OpenAI Completions API as follows:
```bash
export OPENAI_API_SECRET_KEY=YOUR_KEY_HERE
python main.py \
--model openai \
--model openai-completions\
--model_argsengine=davinci \
--tasks lambada_openai,hellaswag
```
While this functionality is only officially maintained for the official OpenAI API, it tends to also work for other hosting services that use the same API such as [goose.ai](goose.ai) with minor modification. We also have an implementation for the [TextSynth](https://textsynth.com/index.html) API, using `--model textsynth`.
To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the `--check_integrity` flag:
```bash
python main.py \
--model openai \
--model_argsengine=davinci \
--tasks lambada_openai,hellaswag \
--check_integrity
```
### Other Frameworks
A number of other libraries contain scripts for calling the eval harness through their library. These include [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py), [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md), and [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
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@@ -172,6 +193,16 @@ python write_out.py \
This will write out one text file for each task.
To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the `--check_integrity` flag:
```bash
python main.py \
--model openai \
--model_argsengine=davinci \
--tasks lambada_openai,hellaswag \
--check_integrity
```
## Advanced Usage
For models loaded with the HuggingFace `transformers` library, any arguments provided via `--model_args` get passed to the relevant constructor directly. This means that anything you can do with `AutoModel` can be done with our library. For example, you can pass a local path via `pretrained=` or use models finetuned with [PEFT](https://github.com/huggingface/peft) by taking the call you would run to evaluate the base model and add `,peft=PATH` to the `model_args` argument:
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@@ -201,6 +232,14 @@ To implement a new task in the eval harness, see [this guide](./docs/new_task_gu
As a start, we currently only support one prompt per task, which we strive to make the "standard" as defined by the benchmark's authors. If you would like to study how varying prompts causes changes in the evaluation score, we support prompts authored in the [Promptsource Library](https://github.com/bigscience-workshop/promptsource/tree/main) as described further in https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/lm_eval/docs/new_task_guide.md and https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/lm_eval/docs/advanced_task_guide.md and welcome contributions of novel task templates and task variants.
## How to Contribute or Learn More?
For more information on the library and how everything fits together, check out all of our [documentation pages](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/docs)! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.
You can also ask for help, or discuss new features with the maintainers in the #lm-thunderdome channel of the EleutherAI discord! If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!
