Basic Usage#

Simple Evaluation#

Evaluate a model on specified datasets using default configurations. This framework supports two ways to initiate evaluation tasks: via command line or using Python code.

Method 1. Using Command Line#

Use the eval command

Execute the eval command from any directory:

evalscope eval \
 --model Qwen/Qwen2.5-0.5B-Instruct \
 --datasets gsm8k arc \
 --limit 5

Run run.py

Execute from the evalscope root directory:

python evalscope/run.py \
 --model Qwen/Qwen2.5-0.5B-Instruct \
 --datasets gsm8k arc \
 --limit 5

Method 2. Using Python Code#

When using Python code for evaluation, submit the evaluation task with the run_task function by passing in a TaskConfig as a parameter. It can also be a Python dictionary, a YAML file path, or a JSON file path, for example:

Using Python Dictionary

from evalscope.run import run_task

task_cfg = {
    'model': 'Qwen/Qwen2.5-0.5B-Instruct',
    'datasets': ['gsm8k', 'arc'],
    'limit': 5
}

run_task(task_cfg=task_cfg)

Using TaskConfig

from evalscope.run import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='Qwen/Qwen2.5-0.5B-Instruct',
    datasets=['gsm8k', 'arc'],
    limit=5
)

run_task(task_cfg=task_cfg)

Using yaml file

config.yaml#

model: Qwen/Qwen2.5-0.5B-Instruct
datasets:
  - gsm8k
  - arc
limit: 5

from evalscope.run import run_task

run_task(task_cfg="config.yaml")

Using json file

config.json#

{
    "model": "Qwen/Qwen2.5-0.5B-Instruct",
    "datasets": ["gsm8k", "arc"],
    "limit": 5
}

from evalscope.run import run_task

run_task(task_cfg="config.json")

Basic Parameter Descriptions#

--model: Specifies the model_id of the model in ModelScope, which can be automatically downloaded, for example, Qwen/Qwen2.5-0.5B-Instruct; it can also be a local path to the model, e.g., /path/to/model.
--datasets: Dataset names, supporting multiple datasets separated by spaces. Datasets will be automatically downloaded from ModelScope; refer to the Dataset List for supported datasets.
--limit: Maximum amount of evaluation data per dataset. If not specified, it defaults to evaluating all data, which can be used for quick validation.

Output Results#

+-----------------------+-------------------+-----------------+
| Model                 | ai2_arc           | gsm8k           |
+=======================+===================+=================+
| Qwen2.5-0.5B-Instruct | (ai2_arc/acc) 0.6 | (gsm8k/acc) 0.6 |
+-----------------------+-------------------+-----------------+ 

Complex Evaluation#

For more customized evaluations, like setting custom model parameters or dataset parameters, you can use the following command. The method to initiate evaluation is the same as in simple evaluation. Below is an example using the eval command to start the evaluation:

evalscope eval \
 --model Qwen/Qwen2.5-0.5B-Instruct \
 --model-args revision=master,precision=torch.float16,device_map=auto \
 --generation-config do_sample=true,temperature=0.5 \
 --dataset-args '{"gsm8k": {"few_shot_num": 0, "few_shot_random": false}}' \
 --datasets gsm8k \
 --limit 10

Parameter Descriptions#

--model-args: Model loading parameters, separated by commas in the key=value format, default parameters:
- revision: Model version, defaults to master
- precision: Model precision, defaults to auto
- device_map: Device allocation for the model, defaults to auto
--generation-config: Generation parameters, separated by commas in the key=value format, default parameters:
- do_sample: Whether to use sampling, defaults to false
- max_length: Maximum length, defaults to 2048
- max_new_tokens: Maximum length for generation, defaults to 512
--dataset-args: Settings parameters for the evaluation dataset, provided in json format, where the key is the dataset name and the value is the parameter. Note that these must correspond one-to-one with the values in the --datasets parameter:
- few_shot_num: Number of few-shot samples
- few_shot_random: Whether to randomly sample few-shot data; if not set, defaults to true

Output Results#

+-----------------------+-----------------+
| Model                 | gsm8k           |
+=======================+=================+
| Qwen2.5-0.5B-Instruct | (gsm8k/acc) 0.2 |
+-----------------------+-----------------+

Using Local Datasets and Models#

By default, datasets are hosted on ModelScope and require internet access for loading. If you are in an offline environment, you can use local datasets. The process is as follows:

Assume the current local working path is /path/to/workdir.

Download the Dataset Locally#

Execute the following commands:

wget https://modelscope.oss-cn-beijing.aliyuncs.com/open_data/benchmark/data.zip
unzip data.zip

The extracted dataset will be in the /path/to/workdir/data directory. This directory will be passed as the value of the local_path parameter in subsequent steps.

Download the Model Locally#

Model files are hosted on the ModelScope Hub and require internet access for loading. If you need to create evaluation tasks in an offline environment, you can download the model to your local machine in advance:

For example, use Git to download the Qwen2.5-0.5B-Instruct model locally:

git lfs install
git clone https://www.modelscope.cn/Qwen/Qwen2.5-0.5B-Instruct.git

Execute Evaluation Task#

Run the following command to perform the evaluation, passing in the local dataset path and model path. Note that local_path must correspond one-to-one with the values in the --datasets parameter:

evalscope eval \
 --model /path/to/workdir/Qwen2.5-0.5B-Instruct \
 --datasets arc \
 --dataset-args '{"arc": {"local_path": "/path/to/workdir/data/arc"}}' \
 --limit 10