Models
Authentication
Use Case
Predict
- POSTModel predictions by use case
- POSTEnglish language model text encoder
- POSTMultilingual language model text encoder
- POSTCustom model prediction
- POSTClassification use case
- POSTPassthrough use case
- POSTRAG use case
- POSTStandalone query rewriter
- POSTSummarization use case
- POSTKeyword extraction use case
- POSTNamed Entity Recognition (NER) use case
- POST
Async Chunking
Async Predict
Prompt Preview
Signals
Tokenization
Summarization use case
In the summarization use case, the LLM ingests text and returns a summary of the text as a response.
The context length is 2048 tokens. No options can be configured.
Headers
The authentication and authorization access token.
application/json
"application/json"
Path Parameters
Unique identifier for the model.
"6a092bd4-5098-466c-94aa-40bf6829430\""
Body
The batch of key:value pairs used as inputs in the prediction. Up to 32 inputs per request are allowed.
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The content the model analyzes.
"Nearly ten years had passed since the Dursleys had woken up to find their nephew on the front step, but Privet Drive had hardly changed at all. The sun rose on the same tidy front gardens and lit up the brass number four on the Dursleys''' front door; it crept into their living room, which was almost exactly the same as it had been on the night when Mr. Dursley had seen that fateful news report about the owls. Only the photographs on the mantelpiece really showed how much time had passed. Ten years ago, there had been lots of pictures of what looked like a large pink beach ball wearing different-colored bonnets - but Dudley Dursley was no longer a baby, and now the photographs showed a large blond boy riding his first bicycle, on a carousel at the fair, playing a computer game with his father, being hugged and kissed by his mother. The room held no sign at all that another boy lived in the house, too."
Provides fields and values that specify ranges for tokens. Fields used for specific use cases and models are specified. The default values are used if other values are not specified.
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A sampling temperature between 0 and 2. A higher sampling temperature such as 0.8, results in more random (creative) output. A lower value such as 0.2 results in more focused (conservative) output. A lower value does not guarantee the model returns the same response for the same input. We recommend staying at or below a temperature of 1.0. Values above 1.0 might return nonsense unless the topP value is lowered to be more deterministic.
0 <= x <= 20.8
A floating-point number between 0 and 1 that controls the cumulative probability of the top tokens to consider, known as the randomness of the LLM's response. This parameter is also referred to as top probability. Set topP to 1 to consider all tokens. A higher value specifies a higher probability threshold and selects tokens whose cumulative probability is greater than the threshold. The higher the value, the more diverse the output.
0 <= x <= 11
An integer that controls the number of top tokens to consider. Set top_k to -1 to consider all tokens.
-1
A floating-point number between -2.0 and 2.0 that penalizes new tokens based on whether they have already appeared in the text. This increases the model's use of diverse tokens. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens. This is applicable for all OpenAI, Mistral, and Llama models.
-2 <= x <= 22
A floating-point number between -2.0 and 2.0 that penalizes new tokens based on their frequency in the generated text. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens. This is applicable for all OpenAI, Mistral, and Llama models.
-2 <= x <= 21
The maximum number of tokens to generate per output sequence. The value is different for each model. Review individual model specifications when the value exceeds 2048.
1
This optional parameter is only required when using the model for prediction. You can find this value in your model's settings:
-
OpenAI: Copy and paste the API key found in your organization's settings. For more information, see OpenAI Authentication API keys.
-
Azure OpenAI: Copy and paste the API key found in your Azure portal. See Authenticate with API key.
-
Anthropic: Copy and paste the API key found in your Anthrophic console or by using the Anthropic API.
-
Google Vertex AI: Copy and paste the base64-encoded service account key JSON found in your Google Cloud console. This service account key must have the Vertex AI user role enabled. For more information, see generate service account key.
"API key specific to use case and model"
This optional parameter is the name of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.
"DEPLOYMENT_NAME"
This optional parameter is the URL endpoint of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.
"https://azure.endpoint.com"
This parameter is optional, and is only required when a Google Vertex AI model is used for prediction.
"[GOOGLE_PROJECT_ID]"
This parameter is optional, and is only required when a Google Vertex AI model is used for prediction. The possible region values are:
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast
"[GOOGLE_PROJECT_REGION_OF_MODEL_ACCESS]"
Response
OK
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The number of tokens generated to prompt the model to continue generating results.
148
The number of tokens used until the model completes.
27
The sum of the prompt and completion tokens used in the model.
175
The results returned from the request.
"A decade after the Dursleys found their nephew on their doorstep, Privet Drive remains largely unchanged. The only noticeable difference is in the family photographs, which now depict Dudley Dursley as a large blond boy engaged in various activities, replacing his baby pictures. There is no indication of another boy living in the house."
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