Parameters | EvalScope (original) (raw)

Run evalscope eval --help to get a complete list of parameter descriptions.

Model Parameters#

• --model: The name of the model being evaluated.
  • Specify the model’s id in ModelScope, and it will automatically download the model, for example, Qwen/Qwen2.5-0.5B-Instruct;
  • Specify the local path to the model, for example, /path/to/model, to load the model from the local environment;
  • When the evaluation target is the model API endpoint, it needs to be specified as model_id, for example, Qwen2.5-0.5B-Instruct.
• --model-id: An alias for the model being evaluated. Defaults to the last part of model, for example, the model-id for Qwen/Qwen2.5-0.5B-Instruct is Qwen2.5-0.5B-Instruct.
• --model-task: The task type of the model, defaults to text_generation, options are text_generation, image_generation.
• --model-args: Model loading parameters, separated by commas in key=value format, with default parameters:
  • revision: Model version, defaults to master
  • precision: Model precision, defaults to torch.float16
  • device_map: Device allocation for the model, defaults to auto
• --generation-config: Generation parameters, separated by commas, in the form of key=value or passed in as a JSON string, which will be parsed into a dictionary:
  • If using local model inference (based on Transformers), the following parameters are included (Full parameter guide):
    * do_sample: Whether to use sampling, default is false
    * max_length: Maximum length, default is 2048
    * max_new_tokens: Maximum length of generated text, default is 512
    * num_return_sequences: Number of sequences to generate, default is 1; when set greater than 1, multiple sequences will be generated, requires setting do_sample=True
    * temperature: Generation temperature
    * top_k: Top-k for generation
    * top_p: Top-p for generation
  • If using model API service for inference (eval-type set to service), the following parameters are included (please refer to the deployed model service for specifics):
    * max_tokens: Maximum length of generated text, default is 512
    * temperature: Generation temperature, default is 0.0
    * n: number of generated sequences, default is 1 (Note: currently, lmdeploy only supports n=1)

`# For example, pass arguments in the form of key=value
--model-args revision=master,precision=torch.float16,device_map=auto
--generation-config do_sample=true,temperature=0.5

Or pass more complex parameters using a JSON string

--model-args '{"revision": "master", "precision": "torch.float16", "device_map": "auto"}'
--generation-config '{"do_sample":true,"temperature":0.5,"chat_template_kwargs":{"enable_thinking": false}}'
`

• --chat-template: Model inference template, defaults to None, indicating the use of transformers’ apply_chat_template; supports passing in a jinja template string to customize the inference template.
• --template-type: Model inference template, deprecated, refer to --chat-template.

The following parameters are only valid when eval-type=service:

• --api-url: Model API endpoint, default is None; supports local or remote OpenAI API format endpoints, for example http://127.0.0.1:8000/v1.
• --api-key: Model API endpoint key, default is EMPTY
• --timeout: Model API request timeout, default is None
• --stream: Whether to use streaming transmission, default is False

Dataset Parameters#

• --datasets: Dataset name, supports inputting multiple datasets separated by spaces, datasets will automatically be downloaded from ModelScope, supported datasets refer to Dataset List.
• --dataset-args: Configuration parameters for the evaluation dataset, passed in json format, where the key is the dataset name and the value is the parameter, note that it needs to correspond one-to-one with the values in the --datasets parameter:
  • dataset_id (or local_path): Local path for the dataset, once specified, it will attempt to load local data.
  • prompt_template: The prompt template for the evaluation dataset. When specified, it will be used to generate prompts. For example, the template for the gsm8k dataset is Question: {query}\nLet's think step by step\nAnswer:. The question from the dataset will be filled into the query field of the template.
  • query_template: The query template for the evaluation dataset. When specified, it will be used to generate queries. For example, the template for general_mcq is Question: {question}\n{choices}\nAnswer: {answer}\n\n. The questions from the dataset will be inserted into the question field of the template, options will be inserted into the choices field, and answers will be inserted into the answer field (answer insertion is only effective for few-shot scenarios).
  • system_prompt: System prompt for the evaluation dataset.
  • model_adapter: The model adapter for the evaluation dataset. Once specified, the given model adapter will be used for evaluation. Currently, it supports generation, multiple_choice_logits, and continuous_logits. For service evaluation, only generation is supported at the moment. Some multiple-choice datasets support logits output.
  • subset_list: List of subsets for the evaluation dataset, once specified, only subset data will be used.
  • few_shot_num: Number of few-shots.
  • few_shot_random: Whether to randomly sample few-shot data, defaults to False.
  • metric_list: A list of metrics for evaluating the dataset. When specified, the evaluation will use the given metrics. Currently supported metrics include AverageAccuracy, AveragePass@1, and Pass@[1-16]. For example, for the humaneval dataset, you can specify ["Pass@1", "Pass@5"]. Note that in this case, you need to set n=5 to make the model return 5 results.
  • filters: Filters for the evaluation dataset. When specified, these filters will be used to process the evaluation results. They can be used to handle the output of inference models. Currently supported filters are:
    * remove_until {string}: Removes the part of the model’s output before the specified string.
    * extract {regex}: Extracts the part of the model’s output that matches the specified regular expression. For example, the ifeval dataset can specify {"remove_until": "</think>"}, which will filter out the part of the model’s output before </think>, avoiding interference with scoring.

# For example --datasets gsm8k arc --dataset-args '{"gsm8k": {"few_shot_num": 4, "few_shot_random": false}, "arc": {"dataset_id": "/path/to/arc"}}, "ifeval": {"filters": {"remove_until": "</think>"}}'

• --dataset-dir: Dataset download path, defaults to ~/.cache/modelscope/datasets.
• --dataset-hub: Dataset download source, defaults to modelscope, alternative is huggingface.
• --limit: The maximum amount of evaluation data for each dataset. If not specified, the default is to evaluate the entire dataset, which can be useful for quick validation. It supports both int and float types. An int value indicates the first N entries of the dataset to be evaluated, while a float value represents the first N% of the dataset. For example, 0.1 means evaluating the first 10% of the dataset, and 100 means evaluating the first 100 entries.

Evaluation Parameters#

• --eval-batch-size: Evaluation batch size, default is 1; when eval-type=service, it indicates the number of concurrent evaluation requests, default is 8.
• --eval-stage: (Deprecated, refer to --use-cache) Evaluation stage, options are all, infer, review, default is all.
• --eval-type: Evaluation type, options are checkpoint, custom, service; defaults to checkpoint.
• --eval-backend: Evaluation backend, options are Native, OpenCompass, VLMEvalKit, RAGEval, ThirdParty, defaults to Native.
  • OpenCompass is used for evaluating large language models.
  • VLMEvalKit is used for evaluating multimodal models.
  • RAGEval is used for evaluating RAG processes, embedding models, re-ranking models, CLIP models.
  • ThirdParty is used for other special task evaluations, such as ToolBench, LongBench.
• --eval-config: This parameter needs to be passed when using a non-Native evaluation backend.

Judge Parameters#

The LLM-as-a-Judge evaluation parameters use a judge model to determine correctness, including the following parameters:

• --judge-strategy: The strategy for using the judge model, options include:
  • auto: The default strategy, which decides whether to use the judge model based on the dataset requirements
  • llm: Always use the judge model
  • rule: Do not use the judge model, use rule-based judgment instead
  • llm_recall: First use rule-based judgment, and if it fails, then use the judge model
• --judge-worker-num: The concurrency number for the judge model, default is 1
• --judge-model-args: Sets the parameters for the judge model, passed in as a json string and parsed as a dictionary, supporting the following fields:
  • api_key: The API endpoint key for the model. If not set, it will be retrieved from the environment variable MODELSCOPE_SDK_TOKEN, with a default value of EMPTY.
  • api_url: The API endpoint for the model. If not set, it will be retrieved from the environment variable MODELSCOPE_API_BASE, with a default value of https://api-inference.modelscope.cn/v1/.
  • model_id: The model ID. If not set, it will be retrieved from the environment variable MODELSCOPE_JUDGE_LLM, with a default value of Qwen/Qwen3-235B-A22B.
  • system_prompt: (Optional) System prompt for evaluating the dataset
  • prompt_template: (Optional) Prompt template for evaluating the dataset
  • generation_config: (Optional) Generation parameters
• --analysis-report: Specifies whether to generate an analysis report, with the default set to false. If this parameter is enabled, an analysis report will be generated using the judge model, providing interpretative insights and recommendations based on the model evaluation results. The output language of the report will be automatically determined by locale.getlocale().

Other Parameters#

• --work-dir: Output path for model evaluation, default is ./outputs/{timestamp}.
• --use-cache: Use local cache path, default is None; if a path is specified, such as outputs/20241210_194434, it will reuse the model inference results from that path. If inference is not completed, it will continue inference and then proceed to evaluation.
• --seed: Random seed, default is 42.
• --debug: Whether to enable debug mode, default is false.
• --ignore-errors: Whether to ignore errors during model generation, default is false.
• --dry-run: Pre-check parameters without performing inference, only prints parameters, default is false.