Create Completion
Description
Given a user’s input, runs LLM inference to produce the model’s response.
Details
LLM completions have many use cases, such as content summarization, question-answering, and text generation.
The model parameter determines which LLM will be used to generate the completion. Keep in mind that different models have varying sizes, costs, and may perform differently across different tasks.
The user input, commonly referred to as the “prompt”, is a required field in the request body. The quality of the model’s response can vary greatly depending on the input prompt. Good prompt engineering can significantly enhance the response quality. If you encounter suboptimal results, consider writing more specific instructions or providing examples to the LLM before trying more expensive techniques such as swapping in other models or finetuning.
By default, the endpoint will return the entire response as one whole object. If you would prefer to stream the completion in real-time, you can achieve this by setting the stream flag to true.
Authorizations
Body
The ID of the model to use for completions.
Users have two options:
- Option 1: Use one of the supported models from the dropdown.
- Option 2: Enter the ID of a custom model.
Note: For custom models we currently only support models finetuned using using the Scale-hosted LLM-Engine API.
gpt-4, gpt-4-0613, gpt-4-32k, gpt-4-32k-0613, gpt-4-vision-preview, gpt-4o, gpt-4o-mini, gpt-4o-2024-08-06, gpt-3.5-turbo, gpt-3.5-turbo-0613, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, gemini-pro, gemini-1.5-pro-001, gemini-1.5-pro-002, gemini-1.5-pro-preview-0409, gemini-1.5-pro-preview-0514, text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001, claude-instant-1, claude-instant-1.1, claude-2, claude-2.0, llama-7b, llama-2-7b, llama-2-7b-chat, llama-2-13b, llama-2-13b-chat, llama-2-70b, llama-2-70b-chat, llama-3-8b, llama-3-8b-instruct, llama-3-1-8b-instruct, llama-3-1-70b-instruct, llama-3-70b-instruct, llama-3-2-1b-instruct, llama-3-2-3b-instruct, Meta-Llama-3-8B-Instruct-RMU, Meta-Llama-3-8B-Instruct-RR, Meta-Llama-3-8B-Instruct-DERTA, Meta-Llama-3-8B-Instruct-LAT, falcon-7b, falcon-7b-instruct, falcon-40b, falcon-40b-instruct, mpt-7b, mpt-7b-instruct, flan-t5-xxl, mistral-7b, mistral-7b-instruct, mixtral-8x7b, mixtral-8x7b-instruct, mixtral-8x22b-instruct, llm-jp-13b-instruct-full, llm-jp-13b-instruct-full-dolly, zephyr-7b-alpha, zephyr-7b-beta, zephyr-cat-merged, codellama-7b, codellama-7b-instruct, codellama-13b, codellama-13b-instruct, codellama-34b, codellama-34b-instruct, phi-3-mini-4k-instruct, phi-3-cat-merged, dolphin-2.9-llama3-8b, dolphin-2.9-llama3-70b Prompt for which to generate the completion.
Good prompt engineering is crucial to getting performant results from the model. If you are having trouble getting the model to perform well, try writing a more specific prompt here before trying more expensive techniques such as swapping in other models or finetuning the underlying LLM.
The account ID to use for usage tracking. This will be gradually enforced.
List of image urls to be used for image based completions. Leave empty for text based completions.
Configuration parameters for the completion model, such as temperature, max_tokens, and stop_sequences.
If not specified, the default value are:
- temperature: 0.2
- max_tokens: None (limited by the model's max tokens)
- stop_sequences: None
Whether or not to stream the response.
Setting this to True will stream the completion in real-time.