In the field of natural language processing, the evaluation of language models is crucial for developers to push the boundaries of language understanding and generation.LLM AutoEval is a tool designed to simplify and accelerate the process of evaluating language models (LLMs), customized for developers seeking to quickly and efficiently evaluate the performance of LLMs.
LLM AutoEval has the following key features.
1. **Automated setup and execution:** LLM AutoEval simplifies the setup and execution process through the use of RunPod, providing a convenient Colab notebook for seamless deployment.
2. **Customizable Evaluation Parameters:** Developers can fine-tune their evaluation by choosing from two benchmark suites - nous or openllm. This provides flexible evaluation of LLM performance.
3. **Summary Generation and GitHub Gist Upload:** LLM AutoEval generates a summary of the evaluation results to quickly demonstrate the performance of the model. The summary is then conveniently uploaded to GitHub Gist for easy sharing and reference.
LLM AutoEval provides a user-friendly interface with customizable evaluation parameters to meet the diverse needs of developers when evaluating the performance of language models. Two benchmark suites, nous and openllm, provide different task lists for evaluation. nous suite includes tasks such as AGIEval, GPT4ALL, TruthfulQA, and Bigbench, and is recommended for comprehensive evaluation.
On the other hand, the openllm suite contains tasks such as ARC, HellaSwag, MMLU, Winogrande, GSM8K, and TruthfulQA that utilize vllm to achieve enhanced speed. Developers can select a specific model ID from the Hugging Face, chooseFirst choiceGPUs, specify the number of GPUs, set the container disk size, choose to use community or secure cloud on RunPod, and toggle the Trusted Remote Code flag for models like Phi. Additionally, developers can activate debug mode, although it's not recommended to keep the Pod active after an evaluation.
In order to achieve seamless token integration in LLM AutoEval, the user must use Colab's Secrets tab, where two secrets named runpod and github are created, containing the tokens required for RunPod and GitHub, respectively.
Two benchmark suites, nous and openllm, for different evaluation needs.
1. Nous Suite:* Developers can compare their LLM results with models such as OpenHermes-2.5-Mistral-7B, Nous-Hermes-2-SOLAR-10.7B, or Nous-Hermes-2-Yi-34B.Teknium's LLM-Benchmark-Logs can be serve as a valuable reference for evaluating comparisons.
2. Open LLM Suite: This suite allows developers to benchmark their models against those listed in the Open LLM leaderboard, facilitating broader comparisons within the community.
Troubleshooting in LLM AutoEval provides clear guidance on common problems. For example, the "Error: File does not exist" scenario prompts the user to activate debug mode and rerun the evaluation, making it easy to check the logs to identify and correct issues related to the missing JSON file. In the case of the "700Killed" error, the user is warned that the hardware may be insufficient, especially when attempting to run the Open LLM benchmark suite on a GPU like the RTX3070. Finally, in the unfortunate case of outdated CUDA drivers, users are advised to start a new pod to ensure compatibility and smooth operation of the LLM AutoEval tool.
LM AutoEval is a promising tool for developers navigating the complex field of LLM evaluation. As an evolving project designed for personal use, developers are encouraged to use it with caution and contribute to its development to ensure continued growth and utility in the natural language processing community.
Project website:https://github.com/mlabonne/llm-autoeval?tab=readme-ov-file