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- EleutherAI lm-evaluation-harness - GitHub
A user guide detailing the full list of supported arguments is provided here, and on the terminal by calling lm_eval -h Alternatively, you can use lm-eval instead of lm_eval
- lm-eval · PyPI
lm_eval --model sglang \ --model_args pretrained={model_name},dp_size ={data_parallel_size},tp_size ={tensor_parallel_size},dtype = auto \ --tasks gsm8k_cot \ --batch_size auto [!Tip] When encountering out of memory (OOM) errors (especially for multiple-choice tasks), try these solutions: Use a manual batch_size, rather than auto
- Evaluating LLMs — EleutherAI
Often, papers do not provide the necessary code or sufficient detail to replicate their evaluations fully To address these problems, we introduced the LM Evaluation Harness, a unifying framework that allows any causal language model to be tested on the same exact inputs and codebase
- lm-eval-overview. ipynb - Colab
By leveraging the YAML configs to configure evaluations, the refactored LM-Eval takes the methods of the Task object and makes them configurable by setting the appropriate attributes in the
- Model Guide - LM Evaluation Harness
To make your model usable via the command line interface to lm-eval using python -m lm_eval, you'll need to tell lm-eval what your model's name is This is done via a decorator, lm_eval api registry register_model
- LM-Evaluation Harness Evaluations — Quark 0. 8. 1 documentation
Below details how to run evaluations on LM-Evaluation-Harness tasks Summary of support: The --model hf arg is used to run lm-harness on all huggingface LLMs The --model hf multimodal arg is used to run lm-harness on supported VLMs
- Evaluating LLM Accuracy with lm-evaluation-harness for local . . . - Medium
When developing or deploying large language models (LLMs), accurately measuring performance is essential The lm-evaluation-harness by EleutherAI offers a reliable, quantitative tool to
- lm-evaluation-harness lm_eval tasks README. md at main - GitHub
Adversarial natural language inference tasks designed to test model robustness A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage Note that some of these tasks are machine-translated
- Getting started with LM-Eval :: TrustyAI
LM-Eval is a service for large language model evaluation underpinned by two open-source projects: lm-evaluation-harness and Unitxt LM-Eval is integrated into the TrustyAI Kubernetes Operator In this tutorial, you will learn:
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