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evaluating-llms-harness 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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Zipをダウンロード ダウンロード中... name evaluating-llms-harness description 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. version 1.0.0 author Orchestra Research license MIT dependencies ["lm-eval","transformers","vllm"] metadata {"gauss":{"tags":["Evaluation","LM Evaluation Harness","Benchmarking","MMLU","HumanEval","GSM8K","EleutherAI","Model Quality","Academic Benchmarks","Industry Standard"]}}
lm-evaluation-harness - LLM Benchmarking
Quick start
lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.
Evaluate any HuggingFace model :
lm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf \
--tasks mmlu,gsm8k,hellaswag \
--device cuda:0 \
--batch_size 8
Common workflows
Workflow 1: Standard benchmark evaluation Evaluate model on core benchmarks (MMLU, GSM8K, HumanEval).
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 choiceGSM8K - Grade school math word problemsHellaSwag - Common sense reasoningTruthfulQA - Truthfulness and factualityARC (AI2 Reasoning Challenge) - Science questions
HumanEval - Python code generation (164 problems)MBPP (Mostly Basic Python Problems) - Python coding Standard suite (recommended for model releases):
--tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge
lm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf,dtype=bfloat16 \
--tasks mmlu \
--device cuda:0 \
--batch_size auto
Quantized model (4-bit/8-bit) :
lm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf,load_in_4bit=True \
--tasks mmlu \
--device cuda:0
lm_eval --model hf \
--model_args pretrained=/path/to/my-model,tokenizer=/path/to/tokenizer \
--tasks mmlu \
--device cuda:0
lm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf \
--tasks mmlu \
--num_fewshot 5 \
--batch_size 8 \
--output_path results/ \
--log_samples
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
Results saved to results/llama2-7b-eval.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:
#!/bin/bash
CHECKPOINT_DIR=$1
STEP=$2
lm_eval --model hf \
--model_args pretrained=$CHECKPOINT_DIR /checkpoint-$STEP \
--tasks gsm8k,hellaswag \
--num_fewshot 0 \
--batch_size 16 \
--output_path results/step-$STEP .json
Step 2: Choose quick benchmarks
Fast benchmarks for frequent evaluation:
HellaSwag : ~10 minutes on 1 GPUGSM8K : ~5 minutesPIQA : ~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:
if step % eval_interval == 0 :
model.save_pretrained(f"checkpoints/step-{step} " )
os.system(f"./eval_checkpoint.sh checkpoints step-{step} " )
Or use PyTorch Lightning callbacks:
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} "
trainer.save_checkpoint(checkpoint_path)
os.system(f"lm_eval --model hf --model_args pretrained={checkpoint_path} ..." )
Step 4: Plot learning curves
import json
import matplotlib.pyplot as plt
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" ])
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
meta-llama/Llama-2-7b-hf
meta-llama/Llama-2-13b-hf
mistralai/Mistral-7B-v0.1
microsoft/phi-2
#!/bin/bash
TASKS="mmlu,gsm8k,hellaswag,truthfulqa"
while read model; do
echo "Evaluating $model "
model_name=$(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
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:
metrics = data["results" ][task]
if "acc" in metrics:
row[task.upper()] = f"{metrics['acc' ]:.3 f} "
elif "exact_match" in metrics:
row[task.upper()] = f"{metrics['exact_match' ]:.3 f} "
results.append(row)
df = pd.DataFrame(results)
print (df.to_markdown(index=False ))
| 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 2: Configure vLLM backend
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
vLLM is 5-10× faster than standard HuggingFace:
lm_eval --model hf \
--model_args pretrained=meta-llama/Llama-2-7b-hf \
--tasks mmlu \
--batch_size 8
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 judgesMT-Bench : Conversational multi-turn evaluationCustom scripts : Domain-specific evaluation
Common issues Issue: Evaluation too slow
lm_eval --model vllm \
--model_args pretrained=model-name,tensor_parallel_size=2
Or reduce fewshot examples:
Or evaluate subset of MMLU:
--model_args pretrained=model-name,load_in_8bit=True
--model_args pretrained=model-name,device_map=auto,offload_folder=offload
Issue: Different results than reported
Verify model and tokenizer match:
--model_args pretrained=model-name,tokenizer=same-model-name
Issue: HumanEval not executing code
Install execution dependencies:
lm_eval --model hf \
--model_args pretrained=model-name \
--tasks humaneval \
--allow_code_execution
Advanced topics
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 