GitLab

* added tasks and task family descriptors

* continue work on task list w/ links; slightly reorganize README

* Apply suggestions from code review

* Rename file so that it'll preview in Github when viewing lm_eval/tasks folder

* Update new_task_guide.md

* Update README.md

* run linter

* Add language column to task table; Add missing tasks to task table; fix nq_open and storycloze READMEs

* fix typo

* Apply suggestions from code review
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* apply format

---------
Co-authored-by: Harish Vadaparty <harishvadaparty@gmail.com>
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>
Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

* dataset card initial

* few fixes

* adds groups for math, mmlu, gpqa

* added summary agrs

* moved sanitize_list to utils

* readme update

* recreate metadata moved

* multiple model support

* results latest split fix

* readme update and small refactor

* fix grouping

* add comments

* added pathlib

* corrected pathlib approach

* check whether to create a metadata card

* convert posix paths to str

* default hf org from token

* hf token value error

* Add logs after successful upload

* logging updates

* dataset card example in the readme

---------
Co-authored-by: Nathan Habib <nathan.habib@huggingface.com>
Co-authored-by: Alina Lozovskaia <alinailozovskaya@gmail.com>

* Initial support for Unitxt datasets in LM Eval Harness

See  https://github.com/IBM/unitxt



The script 'generate_yamls.py' creates LM Eval Harness yaml files corresponding to Unitxt datasets specified in the 'unitxt_datasets' file.

The glue code required to register Unitxt metrics is in 'unitxt_wrapper.py'.

* Added dataset loading check to generate_yaml

Improved error messages.

* Speed up generate_yaml

Added printouts and improved error message

* Added output printout

* Simplified integration of unitxt datasets

Store all the common yaml configuration in a yaml include shared by all datasets of the same task.

* Post code review comments - part 1

1. Made sure include files don't end wth 'yaml' so they won't be marked as tasks
2. Added more datasets and tasks (NER, GEC)
3. Added README

* Post code review comments - part 2

1. Added install unitxt install option in pyproject.toml:
pip install 'lm_eval[unitxt]'
2. Added a check that unitxt is installed and print a clear error message if not

* Commited missing pyproject change

* Added documentation on adding datasets

* More doc changes

* add unitxt extra to readme

* run precommit

---------
Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

fix `----hf_hub_log_args` to `--hf_hub_log_args`

* evaluation tracker implementation

* OVModelForCausalLM test fix

* typo fix

* moved methods args

* multiple args in one flag

* loggers moved to dedicated dir

* improved filename sanitization

https://github.com/EleutherAI/lm-evaluation-harness/issues/1698

* Add neuralmagic models for SparseML and DeepSparse

* Update to latest and add test

* Format

* Fix list to List

* Format

* Add deepsparse/sparseml to automated testing

* Update pyproject.toml

* Update pyproject.toml

* Update README

* Fixes for dtype and device

* Format

* Fix test

* Apply suggestions from code review
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* Address review comments!

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* added delta weights

* removed debug

* readme update

* better error handling

* autogptq warn

* warn update

* peft and delta error, explicitly deleting _model_delta

* linter fix

* claude3

* supply for anthropic claude3

* supply for anthropic claude3

* anthropic config changes

* add callback options on anthropic

* line passed

* claude3 tiny change

* help anthropic installation

* mention sysprompt / being careful with format in readme

---------
Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

* Integration of NeMo models into LM Evaluation Harness library

* rename nemo model as nemo_lm

* move nemo section in readme after hf section

* use self.eot_token_id in get_until()

* improve progress bar showing loglikelihood requests

* data replication or tensor/pipeline replication working fine within one node

* run pre-commit on modified files

* check whether dependencies are installed

* clarify usage of torchrun in README

* Link to vllm integration

* add pip install .[vllm] cmd

* make `WandbLogger` init args optional

* nit

* nit

* nit

* move import warning to `WandbLogger`

* nit

* update docs

* nit

* add wandb as extra dependency

* wandb metrics logging

* refactor

* log samples as tables

* fix linter

* refactor: put in a class

* change dir

* add panels

* log eval as table

* improve tables logging

* improve reports logging

* precommit run

* ruff check

* handle importing reports api gracefully

* ruff

* compare results

* minor pre-commit fixes

* build comparison report

* ruff check

* log results as artifacts

* remove comparison script

* update dependency

* type annotate and docstring

* add example

* update readme

* fix typo

* teardown

* handle outside wandb run

* gracefully fail reports creation

* precommit checks

* add report url to summary

* use wandb  printer for better url stdout

* fix ruff

* handle N/A and groups

* fix eval table

* remove unused var

* update wandb version req + disable reports stdout

* remove reports feature to TODO

* add label to multi-choice question data

* log model predictions

* lints

* loglikelihood_rolling

* log eval result for groups

* log tables by group for better handling

* precommit

* choices column for multi-choice

* graciously fail wandb

* remove reports feature

* track system metrics + total eval time + stdout

---------
Co-authored-by: Lintang Sutawika <lintang@eleuther.ai>

* add hf_transfer

* update dependencies

* Delete stale `[linting]` extra

* Update README.md with extras table

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* initial commit

* remove overwrite bs

* adding neuronx dependencies

* Update README.md

* update neuronx

* Update CITATION.bib

* Create CONTRIBUTING.md

* add disclaimer re: multi node

* flesh out some sections more

* Flesh out contributor guide

* revert CITATION.bib

* appease pre-commit

---------
Co-authored-by: lintangsutawika <lintang@eleuther.ai>

* add bypass metric

* fixed `bypass` metric.

* add task attributes if predict_only

* add `predict_only` checks

* add docs

* added `overide_metric`, `override_config` to `Task`

* nits

* nit

* changed --predict_only to generations; nits

* nits

* nits

* change gen_kwargs warning

* add note about `--predict_only` in README.md

* added `predict_only`

* move table to bottom

* nit

* change null aggregation to bypass (conflict)

* bugfix; default `temp=0.0`

* typo

* added intel optimum

* added intel optimum in readme

* modified intel optimum

* modified intel optimum

* modified intel optimum

* modified install optimum

* modified path of IR file

* added openvino_device

* added openvino_device2

* changed optimum-causal to openvino-causal

* Update README.md

* Update README.md

* remove `lm_eval.base` import

* update openvino-causal -> openvino ; pass device through super().__init__()

* Update README.md

* Add optimum to tests dependencies

* apply pre-commit

* fix so tests pass

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>
Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

* Update README.md

* [!Tip]

* don't use get_task_dict() as a helper, it will download the dataset!

* pre-commit

* Update README.md

---------
Co-authored-by: lintangsutawika <lintang@eleuther.ai>

* Add `local-completions` support using OpenAI interface

* Refactor oa_completion

* Address tokenizer comments and change request chunks to batch size

* Add warning message for tiktoken backend

* fix formatting

* fix whitespace

* Update README.md

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* Update README.md

* punctuation

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

It looks like Google Scholar has [already noticed](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C9&authuser=2&q=%22A+framework+for+few-shot+language+model+evaluation%2C+12+2023%22&btnG=) the updated citation block so let's add it back in.

* Make parallelize=True distinction clearer in documentation.

* run linter

Over a dozen papers have used the updated citation block, but Google Scholar has noticed none of them. Since it does understand this citation, I think we should use it going forward until we have a way to ensure the newer citations are actually logged.

* modularize HFLM code

* pass through extra kwargs to AutoModel.from_pretrained call

* remove explicit model_kwargs

* rename gptq -> autogptq

* fix tokenizer pad token errors

* ensure model always respects device_map and autogptq's selected devices

* add a _get_config helper fn

* add mambaLMWrapper

* add mamba extra

* add mamba extra

* fix conditional import

* Fix botched merge commit

* Remove beginning-of-file comment for consistency

* Add docstring for mambaLM re: supported kwargs

* Alphabetize extras

* Update extras table

* appease precommit

* run precommit on mamba_lm

* Update README.md

Add a not about running on apple arm gpus

* Update README.md

* Update README.md

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* LocalChatCompletionsLM add

* clean up completions class

* clean up completions class

* update tokens

* README

* fix constructor

* eos token

* folding local-chat-completions into OpenAIChatCompletions

* refactoring to include gen_kwargs as passable option

* add todo on chat completion kwarg validation

* Ruff and README fix

* generalize to **kwargs

* remove unnecessary kwargs

* README and remove kwargs

* README

* add ruff and isort. remove black and flake8

* remove unnecessary dependencies

* remove dependency from table

* change order

* ran ruff

* check 3.9

* exclude evaluator

* update CI workflow

* use ruff config in pyproject.toml

* test

* add isort rules to ruff

* sort imports

* import `make_table`

* try stages for no-commit-to-branch

* turn on mypy for pre-commit

* test

* test

* test

* change no-commit-to-branch to default

* nits

* fixed dependency