evaluating-llms-harness

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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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lm-evaluation-harness - LLM Benchmarking

Quick start

lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.
Installation:
bash
pip install lm-eval
Evaluate any HuggingFace model:
bash
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag \
  --device cuda:0 \
  --batch_size 8
View available tasks:
bash
lm_eval --tasks list

Common workflows

Workflow 1: Standard benchmark evaluation

Evaluate model on core benchmarks (MMLU, GSM8K, HumanEval).
Copy this checklist:
Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model
- [ ] Step 3: Run evaluation
- [ ] Step 4: Analyze results
Step 1: Choose benchmark suite
Core reasoning benchmarks:
  • MMLU (Massive Multitask Language Understanding) - 57 subjects, multiple choice
  • GSM8K - Grade school math word problems
  • HellaSwag - Common sense reasoning
  • TruthfulQA - Truthfulness and factuality
  • ARC (AI2 Reasoning Challenge) - Science questions
Code benchmarks:
  • HumanEval - Python code generation (164 problems)
  • MBPP (Mostly Basic Python Problems) - Python coding
Standard suite (recommended for model releases):
bash
--tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge
Step 2: Configure model
HuggingFace model:
bash
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf,dtype=bfloat16 \
  --tasks mmlu \
  --device cuda:0 \
  --batch_size auto  # Auto-detect optimal batch size
Quantized model (4-bit/8-bit):
bash
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf,load_in_4bit=True \
  --tasks mmlu \
  --device cuda:0
Custom checkpoint:
bash
lm_eval --model hf \
  --model_args pretrained=/path/to/my-model,tokenizer=/path/to/tokenizer \
  --tasks mmlu \
  --device cuda:0
Step 3: Run evaluation
bash
# Full MMLU evaluation (57 subjects)
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \  # 5-shot evaluation (standard)
  --batch_size 8 \
  --output_path results/ \
  --log_samples  # Save individual predictions

# Multiple benchmarks at once
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge \
  --num_fewshot 5 \
  --batch_size 8 \
  --output_path results/llama2-7b-eval.json
Step 4: Analyze results
Results saved to 
results/llama2-7b-eval.json
:
json
{
  "results": {
    "mmlu": {
      "acc": 0.459,
      "acc_stderr": 0.004
    },
    "gsm8k": {
      "exact_match": 0.142,
      "exact_match_stderr": 0.006
    },
    "hellaswag": {
      "acc_norm": 0.765,
      "acc_norm_stderr": 0.004
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=meta-llama/Llama-2-7b-hf",
    "num_fewshot": 5
  }
}

Workflow 2: Track training progress

Evaluate checkpoints during training.
Training Progress Tracking:
- [ ] Step 1: Set up periodic evaluation
- [ ] Step 2: Choose quick benchmarks
- [ ] Step 3: Automate evaluation
- [ ] Step 4: Plot learning curves
Step 1: Set up periodic evaluation
Evaluate every N training steps:
bash
#!/bin/bash
# eval_checkpoint.sh

CHECKPOINT_DIR=$1
STEP=$2

lm_eval --model hf \
  --model_args pretrained=$CHECKPOINT_DIR/checkpoint-$STEP \
  --tasks gsm8k,hellaswag \
  --num_fewshot 0 \  # 0-shot for speed
  --batch_size 16 \
  --output_path results/step-$STEP.json
Step 2: Choose quick benchmarks
Fast benchmarks for frequent evaluation:
  • HellaSwag: ~10 minutes on 1 GPU
  • GSM8K: ~5 minutes
  • PIQA: ~2 minutes
Avoid for frequent eval (too slow):
  • MMLU: ~2 hours (57 subjects)
  • HumanEval: Requires code execution
Step 3: Automate evaluation
Integrate with training script:
python
# In training loop
if step % eval_interval == 0:
    model.save_pretrained(f"checkpoints/step-{step}")

    # Run evaluation
    os.system(f"./eval_checkpoint.sh checkpoints step-{step}")
Or use PyTorch Lightning callbacks:
python
from pytorch_lightning import Callback

class EvalHarnessCallback(Callback):
    def on_validation_epoch_end(self, trainer, pl_module):
        step = trainer.global_step
        checkpoint_path = f"checkpoints/step-{step}"

        # Save checkpoint
        trainer.save_checkpoint(checkpoint_path)

        # Run lm-eval
        os.system(f"lm_eval --model hf --model_args pretrained={checkpoint_path} ...")
Step 4: Plot learning curves
python
import json
import matplotlib.pyplot as plt

# Load all results
steps = []
mmlu_scores = []

for file in sorted(glob.glob("results/step-*.json")):
    with open(file) as f:
        data = json.load(f)
        step = int(file.split("-")[1].split(".")[0])
        steps.append(step)
        mmlu_scores.append(data["results"]["mmlu"]["acc"])

# Plot
plt.plot(steps, mmlu_scores)
plt.xlabel("Training Step")
plt.ylabel("MMLU Accuracy")
plt.title("Training Progress")
plt.savefig("training_curve.png")

Workflow 3: Compare multiple models

Benchmark suite for model comparison.
Model Comparison:
- [ ] Step 1: Define model list
- [ ] Step 2: Run evaluations
- [ ] Step 3: Generate comparison table
Step 1: Define model list
bash
# models.txt
meta-llama/Llama-2-7b-hf
meta-llama/Llama-2-13b-hf
mistralai/Mistral-7B-v0.1
microsoft/phi-2
Step 2: Run evaluations
bash
#!/bin/bash
# eval_all_models.sh

TASKS="mmlu,gsm8k,hellaswag,truthfulqa"

while read model; do
    echo "Evaluating $model"

    # Extract model name for output file
    model_name=$(echo $model | sed 's/\//-/g')

    lm_eval --model hf \
      --model_args pretrained=$model,dtype=bfloat16 \
      --tasks $TASKS \
      --num_fewshot 5 \
      --batch_size auto \
      --output_path results/$model_name.json

done < models.txt
Step 3: Generate comparison table
python
import json
import pandas as pd

models = [
    "meta-llama-Llama-2-7b-hf",
    "meta-llama-Llama-2-13b-hf",
    "mistralai-Mistral-7B-v0.1",
    "microsoft-phi-2"
]

tasks = ["mmlu", "gsm8k", "hellaswag", "truthfulqa"]

results = []
for model in models:
    with open(f"results/{model}.json") as f:
        data = json.load(f)
        row = {"Model": model.replace("-", "/")}
        for task in tasks:
            # Get primary metric for each task
            metrics = data["results"][task]
            if "acc" in metrics:
                row[task.upper()] = f"{metrics['acc']:.3f}"
            elif "exact_match" in metrics:
                row[task.upper()] = f"{metrics['exact_match']:.3f}"
        results.append(row)

df = pd.DataFrame(results)
print(df.to_markdown(index=False))
Output:
| Model                  | MMLU  | GSM8K | HELLASWAG | TRUTHFULQA |
|------------------------|-------|-------|-----------|------------|
| meta-llama/Llama-2-7b  | 0.459 | 0.142 | 0.765     | 0.391      |
| meta-llama/Llama-2-13b | 0.549 | 0.287 | 0.801     | 0.430      |
| mistralai/Mistral-7B   | 0.626 | 0.395 | 0.812     | 0.428      |
| microsoft/phi-2        | 0.560 | 0.613 | 0.682     | 0.447      |

Workflow 4: Evaluate with vLLM (faster inference)

Use vLLM backend for 5-10x faster evaluation.
vLLM Evaluation:
- [ ] Step 1: Install vLLM
- [ ] Step 2: Configure vLLM backend
- [ ] Step 3: Run evaluation
Step 1: Install vLLM
bash
pip install vllm
Step 2: Configure vLLM backend
bash
lm_eval --model vllm \
  --model_args pretrained=meta-llama/Llama-2-7b-hf,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.8 \
  --tasks mmlu \
  --batch_size auto
Step 3: Run evaluation
vLLM is 5-10× faster than standard HuggingFace:
bash
# Standard HF: ~2 hours for MMLU on 7B model
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --batch_size 8

# vLLM: ~15-20 minutes for MMLU on 7B model
lm_eval --model vllm \
  --model_args pretrained=meta-llama/Llama-2-7b-hf,tensor_parallel_size=2 \
  --tasks mmlu \
  --batch_size auto

When to use vs alternatives

Use lm-evaluation-harness when:
  • Benchmarking models for academic papers
  • Comparing model quality across standard tasks
  • Tracking training progress
  • Reporting standardized metrics (everyone uses same prompts)
  • Need reproducible evaluation
Use alternatives instead:
  • HELM (Stanford): Broader evaluation (fairness, efficiency, calibration)
  • AlpacaEval: Instruction-following evaluation with LLM judges
  • MT-Bench: Conversational multi-turn evaluation
  • Custom scripts: Domain-specific evaluation

Common issues

Issue: Evaluation too slow
Use vLLM backend:
bash
lm_eval --model vllm \
  --model_args pretrained=model-name,tensor_parallel_size=2
Or reduce fewshot examples:
bash
--num_fewshot 0  # Instead of 5
Or evaluate subset of MMLU:
bash
--tasks mmlu_stem  # Only STEM subjects
Issue: Out of memory
Reduce batch size:
bash
--batch_size 1  # Or --batch_size auto
Use quantization:
bash
--model_args pretrained=model-name,load_in_8bit=True
Enable CPU offloading:
bash
--model_args pretrained=model-name,device_map=auto,offload_folder=offload
Issue: Different results than reported
Check fewshot count:
bash
--num_fewshot 5  # Most papers use 5-shot
Check exact task name:
bash
--tasks mmlu  # Not mmlu_direct or mmlu_fewshot
Verify model and tokenizer match:
bash
--model_args pretrained=model-name,tokenizer=same-model-name
Issue: HumanEval not executing code
Install execution dependencies:
bash
pip install human-eval
Enable code execution:
bash
lm_eval --model hf \
  --model_args pretrained=model-name \
  --tasks humaneval \
  --allow_code_execution  # Required for HumanEval

Advanced topics

Benchmark descriptions: See references/benchmark-guide.md for detailed description of all 60+ tasks, what they measure, and interpretation.
Custom tasks: See references/custom-tasks.md for creating domain-specific evaluation tasks.
API evaluation: See references/api-evaluation.md for evaluating OpenAI, Anthropic, and other API models.
Multi-GPU strategies: See references/distributed-eval.md for data parallel and tensor parallel evaluation.

Hardware requirements

  • GPU: NVIDIA (CUDA 11.8+), works on CPU (very slow)
  • VRAM:
    • 7B model: 16GB (bf16) or 8GB (8-bit)
    • 13B model: 28GB (bf16) or 14GB (8-bit)
    • 70B model: Requires multi-GPU or quantization
  • Time (7B model, single A100):
    • HellaSwag: 10 minutes
    • GSM8K: 5 minutes
    • MMLU (full): 2 hours
    • HumanEval: 20 minutes

Resources