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Evaluate

POST 

/evaluate

Evaluates the model's likelihood to produce a completion given a prompt.

Request

Query Parameters

    nice boolean

    Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.

Body

required
    model stringrequired

    Name of model to use. A model name refers to a model architecture (number of parameters among others). Always the latest version of model is used. The model output contains information as to the model version.

    hosting Hostingnullable

    Possible values: [aleph-alpha, null]

    Optional paramter that specifies which datacenters may process the request. You can either set the parameter to "aleph-alpha" or omit it (defaulting to null).

    Not setting this value, or setting it to null, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximum availability.

    Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.

    prompt object required

    This field is used to send prompts to the model. A prompt can either be a text prompt or a multimodal prompt. A text prompt is a string of text. A multimodal prompt is an array of prompt items. It can be a combination of text, images, and token ID arrays.

    In the case of a multimodal prompt, the prompt items will be concatenated and a single prompt will be used for the model.

    Tokenization:

    • Token ID arrays are used as as-is.
    • Text prompt items are tokenized using the tokenizers specific to the model.
    • Each image is converted into 144 tokens.
    oneOf

    string

    completion_expected stringrequired

    The completion that you would expect to be completed. Unconditional completion can be used with an empty string (default). The prompt may contain a zero shot or few shot task.

    contextual_control_threshold numbernullable

    If set to null, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-null value, we apply the control parameters to similar tokens as well. Controls that have been applied to one token will then be applied to all other tokens that have at least the similarity score defined by this parameter. The similarity score is the cosine similarity of token embeddings.

    control_log_additive boolean

    Default value: true

    true: apply controls on prompt items by adding the log(control_factor) to attention scores. false: apply controls on prompt items by (attention_scores - -attention_scores.min(-1)) * control_factor

Responses

OK

Schema
    model_version string

    model name and version (if any) of the used model for inference

    result object

    dictionary with result metrics of the evaluation

    log_probability numbernullable

    log probability of producing the expected completion given the prompt. This metric refers to all tokens and is therefore dependent on the used tokenizer. It cannot be directly compared among models with different tokenizers.

    log_perplexity numbernullable

    log perplexity associated with the expected completion given the prompt. This metric refers to all tokens and is therefore dependent on the used tokenizer. It cannot be directly compared among models with different tokenizers.

    log_perplexity_per_token numbernullable

    log perplexity associated with the expected completion given the prompt normalized for the number of tokens. This metric computes an average per token and is therefore dependent on the used tokenizer. It cannot be directly compared among models with different tokenizers.

    log_perplexity_per_character numbernullable

    log perplexity associated with the expected completion given the prompt normalized for the number of characters. This metric is independent of any tokenizer. It can be directly compared among models with different tokenizers.

    correct_greedy booleannullable

    Flag indicating whether a greedy completion would have produced the expected completion.

    token_count integernullable

    Number of tokens in the expected completion.

    character_count integernullable

    Number of characters in the expected completion.

    completion stringnullable

    argmax completion given the input consisting of prompt and expected completion. This may be used as an indicator of what the model would have produced. As only one single forward is performed an incoherent text could be produced especially for long expected completions.

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