# Difference Between Inspect-AI and Lighteval for Model Evaluation

> Compare Inspect-AI and Lighteval for model evaluation. Discover Inspect-AI's safety benchmarks and Lighteval's high-throughput multi-tasking for efficient model assessment.

- Repository: [Hugging Face/skills](https://github.com/huggingface/skills)
- Tags: deep-dive
- Published: 2026-03-08

---

**Inspect-AI originates from the UK AI Safety Institute and provides standardized safety-focused benchmarks with deterministic sampling defaults, while Lighteval is Hugging Face's leaderboard evaluation framework designed for high-throughput multi-task runs with flexible pipeline syntax and native chat template support.**

The `huggingface/skills` repository integrates both frameworks to give researchers comprehensive benchmarking capabilities. Understanding the architectural distinctions between **inspect-ai** and **lighteval** helps you select the appropriate tool for safety-oriented standardized testing versus flexible, high-volume leaderboard evaluation.

## Origins and Design Philosophy

**Inspect-AI** emerges from the UK AI Safety Institute's evaluation library, prioritizing reproducible safety assessments and standardized benchmark suites like MMLU and GSM-8K. The framework emphasizes deterministic scoring and consistent evaluation environments for AI safety research.

**Lighteval** serves as the evaluation engine behind Hugging Face's Open LLM Leaderboard, optimized for throughput and flexibility. It supports rapid iteration across multiple tasks using a concise "suite|task|shots" syntax and integrates seamlessly with the Hugging Face ecosystem.

## Backend Architecture and Inference Options

Both frameworks support **vLLM** for fast CUDA inference, but their secondary backends and configuration defaults differ significantly.

### Inspect-AI Backend Configuration

In [`skills/hugging-face-evaluation/scripts/inspect_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/skills/hugging-face-evaluation/scripts/inspect_vllm_uv.py), the wrapper constructs model specifications by prefixing the model ID with either `vllm/` or `hf/` depending on the selected backend. The **vLLM backend** enforces **temperature 0.0** for deterministic evaluation, while the **HF (Hugging Face) backend** defaults to **temperature 0.001** and reduces `--max-connections` from 4 to 1 (see lines 90-96 and 41-45).

### Lighteval Backend Configuration

The [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py) script offers two distinct execution paths: `run_lighteval_vllm` for vLLM inference and `run_lighteval_accelerate` for multi-GPU distributed evaluation using Hugging Face Accelerate. Unlike inspect-ai, lighteval does not expose a `--max-connections` parameter; parallelism is controlled entirely through the underlying engine's tensor parallelism or accelerate configuration (see the command builders in [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py)).

## Task Specification and Workflow

The frameworks use fundamentally different approaches to task definition and execution batching.

**Inspect-AI** expects a single task name via the `--task` argument (e.g., `--task mmlu`). The task definitions reside in the upstream `inspect-evals` package, and each command invocation processes one benchmark at a time.

**Lighteval** utilizes a **pipeline string** format: `suite|task|shots`. This syntax enables multiple tasks in a single run, such as `"leaderboard|mmlu|5,leaderboard|gsm8k|5"`, making it efficient for comprehensive leaderboard evaluations. The parser handles this string in [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py) to construct the final CLI command.

## Chat Templates and Concurrency Controls

**Lighteval** provides explicit support for instruction-tuned models through the `--use-chat-template` and `--system-prompt` flags (implemented around lines 95-101 of [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py)). This allows direct evaluation of chat-formatted models without manual preprocessing.

**Inspect-AI** lacks explicit chat template flags; you must rely on the underlying Hugging Face model's default tokenizer behavior or preprocess inputs externally.

Regarding concurrency, **inspect-ai** offers granular control via `--max-connections`, defaulting to 4 for vLLM and 1 for the HF backend. **Lighteval** delegates concurrency management to the inference engine itself—vLLM handles parallelism through tensor parallelism settings, while Accelerate distributes across GPUs.

## Running Evaluations in the Skills Repository

### Single-Task Safety Evaluation with Inspect-AI

To run a deterministic safety benchmark using vLLM:

```bash
python skills/hugging-face-evaluation/scripts/inspect_vllm_uv.py \
    --model meta-llama/Llama-3.2-1B \
    --task mmlu \
    --backend vllm

```

The script assembles the following command (see lines 78-96 of [`inspect_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/inspect_vllm_uv.py)):

```python
cmd = [
    "inspect", "eval", "mmlu",
    "--model", "vllm/meta-llama/Llama-3.2-1B",
    "--log-level", "info",
    "--max-connections", "4",
    "--temperature", "0.0",
]

```

For Hugging Face backend inference with higher temperature tolerance:

```bash
python skills/hugging-face-evaluation/scripts/inspect_vllm_uv.py \
    --model meta-llama/Llama-3.2-1B \
    --task mmlu \
    --backend hf

```

This generates a command using `hf/meta-llama/Llama-3.2-1B`, `--max-connections 1`, and `--temperature 0.001`.

### Multi-Task Leaderboard Evaluation with Lighteval

To evaluate multiple leaderboard tasks with chat template support:

```bash
python skills/hugging-face-evaluation/scripts/lighteval_vllm_uv.py \
    --model meta-llama/Llama-3.2-1B \
    --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5" \
    --backend vllm \
    --batch-size 4 \
    --tensor-parallel-size 2 \
    --use-chat-template

```

The command builder (lines 76-99 of [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py)) produces:

```bash
lighteval vllm meta-llama/Llama-3.2-1B \
    "leaderboard|mmlu|5,leaderboard|gsm8k|5" \
    --batch-size 4 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.8 \
    --dtype auto \
    --use-chat-template

```

For distributed multi-GPU evaluation using Accelerate:

```bash
python skills/hugging-face-evaluation/scripts/lighteval_vllm_uv.py \
    --model meta-llama/Llama-3.2-1B \
    --tasks "leaderboard|mmlu|5" \
    --backend accelerate \
    --batch-size 8 \
    --dtype bfloat16 \
    --trust-remote-code

```

## Summary

- **Inspect-AI** is optimized for **safety-focused, single-task evaluation** with deterministic defaults (temperature 0.0) and originates from the UK AI Safety Institute.
- **Lighteval** excels at **high-throughput, multi-task leaderboard evaluation** using the "suite|task|shots" syntax and offers native chat template support.
- **Inspect-AI** exposes `--max-connections` tuning (defaults: 4 for vLLM, 1 for HF), while **lighteval** relies on backend-level parallelism.
- Both frameworks are accessible through the `huggingface/skills` repository via [`inspect_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/inspect_vllm_uv.py) and [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py), with detailed documentation in [`SKILL.md`](https://github.com/huggingface/skills/blob/main/SKILL.md).

## Frequently Asked Questions

### Which framework should I use for evaluating chat or instruction-tuned models?

**Use Lighteval.** It provides the `--use-chat-template` and `--system-prompt` flags (lines 95-101 of [`lighteval_vllm_uv.py`](https://github.com/huggingface/skills/blob/main/lighteval_vllm_uv.py)) to properly format prompts for conversational models. Inspect-AI lacks explicit chat template handling and relies on default tokenizer behavior.

### Can I run multiple benchmark tasks in a single command with inspect-ai?

**No.** Inspect-AI accepts only a single task name per invocation via the `--task` argument. For multiple tasks, you must execute separate commands or script sequential calls. Lighteval supports multiple tasks in one run using comma-separated pipeline strings like `"leaderboard|mmlu|5,leaderboard|hellaswag|0"`.

### How do the temperature defaults differ between the two frameworks?

**Inspect-AI enforces deterministic sampling:** temperature 0.0 for vLLM backend and 0.001 for the HF backend. **Lighteval imposes no default temperature**, allowing you to specify any value via command-line arguments or leave it to the model configuration.

### Where are the evaluation results stored for each framework?

**Inspect-AI** outputs results to stdout, which [`evaluation_manager.py`](https://github.com/huggingface/skills/blob/main/evaluation_manager.py) in the skills repository parses for model card integration. **Lighteval** can write structured outputs to an `--output-dir` in JSON or CSV format, compatible with the Open LLM Leaderboard data pipeline.