Evaluating Llms Harness — lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.)
Evaluating Llms Harness — lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.)
Start with meaning, then move to detail.
This lesson explains Evaluating Llms Harness — lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.) as part of extending Hermes and connecting external tools. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, What's inside, then verify failure modes and version compatibility.
Complete installation and one successful task before adding new capabilities.
A clear outcome before you read.
- Understand Evaluating Llms Harness — lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.) without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.)
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03What's inside
Read this after the foundation, then connect it to the previous step.
- 04Quick start
Read this after the foundation, then connect it to the previous step.
- 05Common workflows
Read this after the foundation, then connect it to the previous step.
- 06Workflow 1: Standard benchmark evaluation
Read this after the foundation, then connect it to the previous step.
- 07Workflow 2: Track training progress
Read this after the foundation, then connect it to the previous step.
- 08Workflow 3: Compare multiple models
Read this after the foundation, then connect it to the previous step.
- 09Workflow 4: Evaluate with vLLM (faster inference)
Read this after the foundation, then connect it to the previous step.
- 10When to use vs alternatives
Finish here to verify the result and special cases.
Copy only after you understand the effect.
pip install lm-evallm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf \
--tasks mmlu,gsm8k,hellaswag \
--device cuda:0 \
--batch_size 8lm-eval ls tasksRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.