- Support for models loaded via [transformers](https://github.com/huggingface/transformers/)(including quantization via [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)), [GPT-NeoX](https://github.com/EleutherAI/gpt-neox), and [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/), with a flexible tokenization-agnostic interface.
- Support for models loaded via [transformers](https://github.com/huggingface/transformers/)(including quantization via [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)), [GPT-NeoX](https://github.com/EleutherAI/gpt-neox), and [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/), with a flexible tokenization-agnostic interface.
- Support for commercial APIs including [OpenAI](https://openai.com), [Anthropic](https://goose.ai), [Cohere](https://goose.ai), and [goose.ai](https://textsynth.com/).
- Support for commercial APIs including [OpenAI](https://openai.com), [Anthropic](https://goose.ai), and [goose.ai](https://textsynth.com/).
- Support for evaluation on adapters (e.g. LoRa) supported in [HuggingFace's PEFT library](https://github.com/huggingface/peft).
- Support for evaluation on adapters (e.g. LoRa) supported in [HuggingFace's PEFT library](https://github.com/huggingface/peft).
- Support for local models and benchmark datasets.
- Support for local models and benchmark datasets.
- Evaluating with publicly available prompts ensures reproducibility and comparability between papers.
- Evaluating with publicly available prompts ensures reproducibility and comparability between papers.
...
@@ -43,7 +43,7 @@ pip install -e ".[all]"
...
@@ -43,7 +43,7 @@ pip install -e ".[all]"
## Support
## 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.
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 in general.
## Basic Usage
## Basic Usage
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@@ -71,8 +71,7 @@ python -m lm_eval \
...
@@ -71,8 +71,7 @@ python -m lm_eval \
--device cuda:0 \
--device cuda:0 \
--batch_size 8
--batch_size 8
```
```
Just like in the transformers library, a local path can be provided to run models that are not on the Hub. We support all mdoels that are loadable using either `transformers.AutoModelForCausalLM` (autoregressive, decoder-only GPT style models) or `transformers.AutoModelForSeq2SeqLM` (such as encoder-decoder models like T5).
Models that are loaded via either `transformers.AutoModelForCausalLM` (autoregressive, decoder-only GPT style models) or `transformers.AutoModelForSeq2SeqLM` (such as encoder-decoder models like T5) in Huggingface are supported via Support for this model type is currently pending.
Batch size selection can be automated by setting the ```--batch_size``` flag to ```auto```. This will perform automatic detection of the largest batch size that will fit on your device. On tasks where there is a large difference between the longest and shortest example, it can be helpful to periodically recompute the largest batch size, to gain a further speedup. To do this, append ```:N``` to above flag to automatically recompute the largest batch size ```N``` times. For example, to recompute the batch size 4 times, the command would be:
Batch size selection can be automated by setting the ```--batch_size``` flag to ```auto```. This will perform automatic detection of the largest batch size that will fit on your device. On tasks where there is a large difference between the longest and shortest example, it can be helpful to periodically recompute the largest batch size, to gain a further speedup. To do this, append ```:N``` to above flag to automatically recompute the largest batch size ```N``` times. For example, to recompute the batch size 4 times, the command would be:
