Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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The evaluating-llms-harness skill provides a comprehensive solution for evaluating large language models (LLMs) across a wide range of academic benchmarks. It addresses the need for standardized evaluation tools in the AI research community, enabling comparisons across models and tracking progress throughout training. The skill supports the benchmarking of models like Llama-2, providing key insights into model quality, effectiveness, and accuracy through the use of various metrics. It is an industry standard, with usage in major research labs such as EleutherAI and HuggingFace.
The skill offers powerful functionality for performing benchmarking evaluations on popular benchmarks such as MMLU, GSM8K, HumanEval, and TruthfulQA. These benchmarks cover a variety of tasks from common-sense reasoning to grade school math and code generation, providing a well-rounded understanding of model performance. The integration with libraries like HuggingFace and vLLM enhances flexibility, making it easy to use with different models and APIs. Additionally, users can configure evaluation tasks, track training progress, and automate the benchmarking process, which is invaluable for model development, academic research, and performance tracking.
This skill is ideal for AI researchers, data scientists, and developers focused on improving LLM performance. It's also a great tool for organizations evaluating multiple models, comparing them on a common ground, or tracking training progress throughout model development.
You can evaluate a model by specifying the benchmark tasks and model details in the command line. For example, using the command 'lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k,hellaswag --device cuda:0 --batch_size 8' will evaluate a HuggingFace model on the specified tasks.
The evaluating-llms-harness supports over 60 academic benchmarks, including MMLU, GSM8K, HumanEval, TruthfulQA, and HellaSwag. It also supports core reasoning, code generation, and truthfulness tasks.
Yes, you can evaluate a custom model by specifying the path to your model and tokenizer in the 'model_args' argument. For instance, 'lm_eval --model hf --model_args pretrained=/path/to/my-model,tokenizer=/path/to/tokenizer --tasks mmlu --device cuda:0'.
Yes, the skill supports evaluating quantized models, such as those in 4-bit or 8-bit formats. Use the '--model_args pretrained=meta-llama/Llama-2-7b-hf,load_in_4bit=True' argument to evaluate a quantized model.
To track training progress, you can set up periodic evaluations during training. You can configure your script to evaluate checkpoints every N steps and track the learning curves of the model.
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