---
name: evaluating-llms-harness
description: "lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.)."
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies: [lm-eval, transformers, vllm]
metadata:
  hermes:
    tags: [Evaluation, LM Evaluation Harness, Benchmarking, MMLU, HumanEval, GSM8K, EleutherAI, Model Quality, Academic Benchmarks, Industry Standard]

---

# lm-evaluation-harness - LLM Benchmarking

## What's inside

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.

## 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](references/benchmark-guide.md) for detailed description of all 60+ tasks, what they measure, and interpretation.

**Custom tasks**: See [references/custom-tasks.md](references/custom-tasks.md) for creating domain-specific evaluation tasks.

**API evaluation**: See [references/api-evaluation.md](references/api-evaluation.md) for evaluating OpenAI, Anthropic, and other API models.

**Multi-GPU strategies**: See [references/distributed-eval.md](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

- GitHub: https://github.com/EleutherAI/lm-evaluation-harness
- Docs: https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs
- Task library: 60+ tasks including MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, ARC, WinoGrande, etc.
- Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard (uses this harness)



