evaluating-llms-harness

Files (5)

• SKILL.md
• api-evaluation.md · references/
• benchmark-guide.md · references/
• custom-tasks.md · references/
• distributed-eval.md · references/

SKILL.md (first lines)

# 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": {

From Orchestra-Research/AI-Research-SKILLs, MIT.

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