# Quicktour


> [!TIP]
> We recommend using the `--help` flag to get more information about the
> available options for each command.
> `lighteval --help`

Lighteval can be used with a few different commands.

- `lighteval accelerate` : evaluate models on CPU or one or more GPUs using [🤗
  Accelerate](https://github.com/huggingface/accelerate)
- `lighteval nanotron`: evaluate models in distributed settings using [⚡️
  Nanotron](https://github.com/huggingface/nanotron)
- `lighteval vllm`: evaluate models on one or more GPUs using [🚀
  VLLM](https://github.com/vllm-project/vllm)
- `lighteval endpoint`
    - `inference-endpoint`: evaluate models on one or more GPUs using [🔗
  Inference Endpoint](https://huggingface.co/inference-endpoints/dedicated)
    - `tgi`: evaluate models on one or more GPUs using [🔗 Text Generation Inference](https://huggingface.co/docs/text-generation-inference/en/index)
    - `openai`: evaluate models on one or more GPUs using [🔗 OpenAI API](https://platform.openai.com/)

## Basic usage

To evaluate `GPT-2` on the Truthful QA benchmark with [🤗
  Accelerate](https://github.com/huggingface/accelerate) , run:

```bash
lighteval accelerate \
     "model_name=openai-community/gpt2" \
     "leaderboard|truthfulqa:mc|0|0"
```

Here, we first choose a backend (either `accelerate`, `nanotron`, or `vllm`), and then specify the model and task(s) to run.

The syntax for the model arguments is `key1=value1,key2=value2,etc`.
Valid key-value pairs correspond with the backend configuration, and are detailed [below](#Model Arguments).

The syntax for the task specification might be a bit hard to grasp at first. The format is as follows:

```txt
{suite}|{task}|{num_few_shot}|{0 for strict `num_few_shots`, or 1 to allow a truncation if context size is too small}
```

If the fourth value is set to 1, lighteval will check if the prompt (including the few-shot examples) is too long for the context size of the task or the model.
If so, the number of few shot examples is automatically reduced.

All officially supported tasks can be found at the [tasks_list](available-tasks) and in the
[extended folder](https://github.com/huggingface/lighteval/tree/main/src/lighteval/tasks/extended).
Moreover, community-provided tasks can be found in the
[community](https://github.com/huggingface/lighteval/tree/main/community_tasks) folder.
For more details on the implementation of the tasks, such as how prompts are constructed, or which metrics are used, you can have a look at the
[file](https://github.com/huggingface/lighteval/blob/main/src/lighteval/tasks/default_tasks.py)
implementing them.

Running multiple tasks is supported, either with a comma-separated list, or by specifying a file path.
The file should be structured like [examples/tasks/recommended_set.txt](https://github.com/huggingface/lighteval/blob/main/examples/tasks/recommended_set.txt).
When specifying a path to file, it should start with `./`.

```bash
lighteval accelerate \
     "model_name=openai-community/gpt2" \
     ./path/to/lighteval/examples/tasks/recommended_set.txt
# or, e.g., "leaderboard|truthfulqa:mc|0|0|,leaderboard|gsm8k|3|1"
```

## Evaluate a model on one or more GPUs

#### Data parallelism

To evaluate a model on one or more GPUs, first create a multi-gpu config by running.

```bash
accelerate config
```

You can then evaluate a model using data parallelism on 8 GPUs like follows:

```bash
accelerate launch --multi_gpu --num_processes=8 -m \
    lighteval accelerate \
    "model_name=openai-community/gpt2" \
    "leaderboard|truthfulqa:mc|0|0"
```

Here, `--override_batch_size` defines the batch size per device, so the effective
batch size will be `override_batch_size * num_gpus`.

#### Pipeline parallelism

To evaluate a model using pipeline parallelism on 2 or more GPUs, run:

```bash
lighteval accelerate \
    "model_name=openai-community/gpt2,model_parallel=True" \
    "leaderboard|truthfulqa:mc|0|0"
```

This will automatically use accelerate to distribute the model across the GPUs.

> [!TIP]
> Both data and pipeline parallelism can be combined by setting
> `model_parallel=True` and using accelerate to distribute the data across the
GPUs.

## Backend configuration

The `model-args` argument takes a string representing a list of model
argument. The arguments allowed vary depending on the backend you use (vllm or
accelerate).

### Accelerate

- **pretrained** (str):
    HuggingFace Hub model ID name or the path to a pre-trained
    model to load. This is effectively the `pretrained_model_name_or_path`
    argument of `from_pretrained` in the HuggingFace `transformers` API.
- **tokenizer** (Optional[str]): HuggingFace Hub tokenizer ID that will be
    used for tokenization.
- **multichoice_continuations_start_space** (Optional[bool]): Whether to add a
    space at the start of each continuation in multichoice generation.
    For example, context: "What is the capital of France?" and choices: "Paris", "London".
    Will be tokenized as: "What is the capital of France? Paris" and "What is the capital of France? London".
    True adds a space, False strips a space, None does nothing
- **subfolder** (Optional[str]): The subfolder within the model repository.
- **revision** (str): The revision of the model.
- **max_gen_toks** (Optional[int]): The maximum number of tokens to generate.
- **max_length** (Optional[int]): The maximum length of the generated output.
- **add_special_tokens** (bool, optional, defaults to True): Whether to add special tokens to the input sequences.
   If `None`, the default value will be set to `True` for seq2seq models (e.g. T5) and
    `False` for causal models.
- **model_parallel** (bool, optional, defaults to None):
    True/False: force to use or not the `accelerate` library to load a large
    model across multiple devices.
    Default: None which corresponds to comparing the number of processes with
        the number of GPUs. If it's smaller => model-parallelism, else not.
- **dtype** (Union[str, torch.dtype], optional, defaults to None):):
    Converts the model weights to `dtype`, if specified. Strings get
    converted to `torch.dtype` objects (e.g. `float16` -> `torch.float16`).
    Use `dtype="auto"` to derive the type from the model's weights.
- **device** (Union[int, str]): device to use for model training.
- **quantization_config** (Optional[BitsAndBytesConfig]): quantization
    configuration for the model, manually provided to load a normally floating point
    model at a quantized precision. Needed for 4-bit and 8-bit precision.
- **trust_remote_code** (bool): Whether to trust remote code during model
    loading.

### VLLM

- **pretrained** (str): HuggingFace Hub model ID name or the path to a pre-trained model to load.
- **gpu_memory_utilization** (float): The fraction of GPU memory to use.
- **batch_size** (int): The batch size for model training.
- **revision** (str): The revision of the model.
- **dtype** (str, None): The data type to use for the model.
- **tensor_parallel_size** (int): The number of tensor parallel units to use.
- **data_parallel_size** (int): The number of data parallel units to use.
- **max_model_length** (int): The maximum length of the model.
- **swap_space** (int): The CPU swap space size (GiB) per GPU.
- **seed** (int): The seed to use for the model.
- **trust_remote_code** (bool): Whether to trust remote code during model loading.
- **use_chat_template** (bool): Whether to use the chat template or not.
- **add_special_tokens** (bool): Whether to add special tokens to the input sequences.
- **multichoice_continuations_start_space** (bool): Whether to add a space at the start of each continuation in multichoice generation.
- **subfolder** (Optional[str]): The subfolder within the model repository.

## Nanotron

To evaluate a model trained with nanotron on a single gpu.

> [!WARNING]
> Nanotron models cannot be evaluated without torchrun.


```bash
 torchrun --standalone --nnodes=1 --nproc-per-node=1  \
 src/lighteval/__main__.py nanotron \
 --checkpoint-config-path ../nanotron/checkpoints/10/config.yaml \
 --lighteval-config-path examples/nanotron/lighteval_config_override_template.yaml
 ```

The `nproc-per-node` argument should match the data, tensor and pipeline
parallelism configured in the `lighteval_config_template.yaml` file.
That is: `nproc-per-node = data_parallelism * tensor_parallelism *
pipeline_parallelism`.
