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@baberabb baberabb released this 08 Oct 21:06
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v0.4.5
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lm-eval v0.4.5 Release Notes

New Additions

Prototype Support for Vision Language Models (VLMs)

We're excited to introduce prototype support for Vision Language Models (VLMs) in this release, using model types hf-multimodal and vllm-vlm. This allows for evaluation of models that can process text and image inputs and produce text outputs. Currently we have added support for the MMMU (mmmu_val) task and we welcome contributions and feedback from the community!

New VLM-Specific Arguments

VLM models can be configured with several new arguments within --model_args to support their specific requirements:

  • max_images (int): Set the maximum number of images for each prompt.
  • interleave (bool): Determines the positioning of image inputs. When True (default) images are interleaved with the text. When False all images are placed at the front of the text. This is model dependent.

hf-multimodal specific args:

  • image_token_id (int) or image_string (str): Specifies a custom token or string for image placeholders. For example, Llava models expect an "<image>" string to indicate the location of images in the input, while Qwen2-VL models expect an "<|image_pad|>" sentinel string instead. This will be inferred based on model configuration files whenever possible, but we recommend confirming that an override is needed when testing a new model family
  • convert_img_format (bool): Whether to convert the images to RGB format.

Example usage:

  • lm_eval --model hf-multimodal --model_args pretrained=llava-hf/llava-1.5-7b-hf,attn_implementation=flash_attention_2,max_images=1,interleave=True,image_string=<image> --tasks mmmu_val --apply_chat_template

  • lm_eval --model vllm-vlm --model_args pretrained=llava-hf/llava-1.5-7b-hf,max_images=1,interleave=True --tasks mmmu_val --apply_chat_template

Important considerations

  1. Chat Template: Most VLMs require the --apply_chat_template flag to ensure proper input formatting according to the model's expected chat template.
  2. Some VLM models are limited to processing a single image per prompt. For these models, always set max_images=1. Additionally, certain models expect image placeholders to be non-interleaved with the text, requiring interleave=False.
  3. Performance and Compatibility: When working with VLMs, be mindful of potential memory constraints and processing times, especially when handling multiple images or complex tasks.

Tested VLM Models

We have currently most notably tested the implementation with the following models:

  • llava-hf/llava-1.5-7b-hf
  • llava-hf/llava-v1.6-mistral-7b-hf
  • Qwen/Qwen2-VL-2B-Instruct
  • HuggingFaceM4/idefics2 (requires the latest transformers from source)

New Tasks

Several new tasks have been contributed to the library for this version!

New tasks as of v0.4.5 include:

As well as several slight fixes or changes to existing tasks (as noted via the incrementing of versions).

Backwards Incompatibilities

Finalizing group versus tag split

We've now fully deprecated the use of group keys directly within a task's configuration file. The appropriate key to use is now solely tag for many cases. See the v0.4.4 patchnotes for more info on migration, if you have a set of task YAMLs maintained outside the Eval Harness repository.

Handling of Causal vs. Seq2seq backend in HFLM

In HFLM, logic specific to handling inputs for Seq2seq (encoder-decoder models like T5) versus Causal (decoder-only autoregressive models, and the vast majority of current LMs) models previously hinged on a check for self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM. Some users may want to use causal model behavior, but set self.AUTO_MODEL_CLASS to a different factory class, such as transformers.AutoModelForVision2Seq.

As a result, those users who subclass HFLM but do not call HFLM.__init__() may now also need to set the self.backend attribute to either "causal" or "seq2seq" during initialization themselves.

While this should not affect a large majority of users, for those who subclass HFLM in potentially advanced ways, see #2353 for the full set of changes.

Future Plans

We intend to further expand our multimodal support to a wider set of vision-language tasks, as well as a broader set of model types, and are actively seeking user feedback!

Thanks, the LM Eval Harness team (@baberabb @haileyschoelkopf @lintangsutawika)

What's Changed

New Contributors

Full Changelog: v0.4.4...v0.4.5

Contributors

dacorvo, giuliolovisotto, and 14 other contributors
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