skills.rest
eliottbusiness
eliottbusinessCOMMUNITY
Explore all skills by this author →
GitHub

evaluating-llms-harness

Benchmark LLM performance across academic benchmarks using lm-eval.

Updated May 3, 2026
Search Tags:#llm#benchmarking#academic research#performance evaluation#model quality
One-click install
npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill evaluating-llms-harness-eliottbusiness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/eliottbusiness/DeptFlow-Agent/tree/main/profile/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill evaluating-llms-harness-eliottbusiness

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for benchmarking and comparing large language models across a wide range of academic benchmarks, helping users to assess model quality and identify areas for improvement.

Core Features & Use Cases

  • Benchmarking: Evaluate LLMs on 60+ academic benchmarks such as MMLU, GSM8K, HellaSwag, and more.
  • Comparison: Compare model performance across different benchmarks and models.
  • Training Progress Tracking: Monitor training progress by evaluating checkpoints during training.
  • Custom Tasks: Define custom benchmark tasks for domain-specific evaluation.
  • API Evaluation: Support for evaluating API-based models like OpenAI, Anthropic, and custom API endpoints.

Quick Start

Evaluate a model on the MMLU benchmark with the following command:

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --output_path results/

Frequently Asked Questions about evaluating-llms-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate an LLM on academic benchmarks like MMLU?▼

To evaluate an LLM on academic benchmarks, run the lm_eval command specifying the model via Hugging Face, the target task like mmlu, and output path. This Skill supports evaluating models across 60+ academic benchmarks to assess quality and compare performance.

Can I benchmark API-based models from OpenAI or Anthropic?▼

Yes, you can benchmark API-based models from OpenAI, Anthropic, and custom API endpoints. The framework supports API evaluation natively, allowing you to compare proprietary models directly against local open-source models using the same academic benchmark tasks.

Do I need vllm and transformers installed to benchmark model quality?▼

Yes, you need the Python libraries lm-eval, transformers, and vllm installed. These dependencies provide the necessary environment for loading models, executing inference, and calculating performance metrics across the diverse set of benchmark tasks.

What is the best way to monitor LLM training progress with academic benchmarks?▼

The best way to monitor LLM training progress is by evaluating checkpoints during training using this benchmarking framework. By running periodic evaluations on tasks like GSM8K or HellaSwag, you can track model quality improvements and identify training anomalies early.

How do I define custom benchmark tasks for domain-specific LLM evaluation?▼

You can define custom benchmark tasks for domain-specific LLM evaluation within the framework. This allows you to extend beyond the 60+ preconfigured academic benchmarks and assess model performance on specialized tasks relevant to your specific application.

Are there limitations when comparing local models to API models using lm-eval?▼

When comparing local models to API models, limitations include dependency requirements for local execution using vllm and transformers, and potential API rate limits or endpoint variability for remote models. Consistent batch sizing and few-shot configurations are necessary for accurate performance comparison.

Related Skills

Explore More Software Engineering Skills→
whichguywhichguy

evaluating-llms-harness

Evaluate large language models on standardized benchmarks using Python libraries.

Community
Advanced
handsomelong922handsomelong922

evaluating-llms-harness

Benchmarks LLMs across 60+ academic tasks producing standardized performance metrics.

Community
Advanced
lenadlmlenadlm

evaluating-llms-harness

Evaluate LLMs across 60+ academic benchmarks using lm-eval harnesses.

Community
Advanced
moltis-orgmoltis-org
2.8k

evaluating-llms-harness

Evaluate LLM performance across 60+ academic benchmarks with multiple backends.

Official
Advanced