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- OK: Not an error; returned on success.

HTTP Mapping: 200 OK
 - CANCELLED: The operation was cancelled, typically by the caller.

HTTP Mapping: 499 Client Closed Request
 - UNKNOWN: Unknown error.  For example, this error may be returned when
a `Status` value received from another address space belongs to
an error space that is not known in this address space.  Also
errors raised by APIs that do not return enough error information
may be converted to this error.

HTTP Mapping: 500 Internal Server Error
 - INVALID_ARGUMENT: The client specified an invalid argument.  Note that this differs
from `FAILED_PRECONDITION`.  `INVALID_ARGUMENT` indicates arguments
that are problematic regardless of the state of the system
(e.g., a malformed file name).

HTTP Mapping: 400 Bad Request
 - DEADLINE_EXCEEDED: The deadline expired before the operation could complete. For operations
that change the state of the system, this error may be returned
even if the operation has completed successfully.  For example, a
successful response from a server could have been delayed long
enough for the deadline to expire.

HTTP Mapping: 504 Gateway Timeout
 - NOT_FOUND: Some requested entity (e.g., file or directory) was not found.

Note to server developers: if a request is denied for an entire class
of users, such as gradual feature rollout or undocumented allowlist,
`NOT_FOUND` may be used. If a request is denied for some users within
a class of users, such as user-based access control, `PERMISSION_DENIED`
must be used.

HTTP Mapping: 404 Not Found
 - ALREADY_EXISTS: The entity that a client attempted to create (e.g., file or directory)
already exists.

HTTP Mapping: 409 Conflict
 - PERMISSION_DENIED: The caller does not have permission to execute the specified
operation. `PERMISSION_DENIED` must not be used for rejections
caused by exhausting some resource (use `RESOURCE_EXHAUSTED`
instead for those errors). `PERMISSION_DENIED` must not be
used if the caller can not be identified (use `UNAUTHENTICATED`
instead for those errors). This error code does not imply the
request is valid or the requested entity exists or satisfies
other pre-conditions.

HTTP Mapping: 403 Forbidden
 - UNAUTHENTICATED: The request does not have valid authentication credentials for the
operation.

HTTP Mapping: 401 Unauthorized
 - RESOURCE_EXHAUSTED: Some resource has been exhausted, perhaps a per-user quota, or
perhaps the entire file system is out of space.

HTTP Mapping: 429 Too Many Requests
 - FAILED_PRECONDITION: The operation was rejected because the system is not in a state
required for the operation's execution.  For example, the directory
to be deleted is non-empty, an rmdir operation is applied to
a non-directory, etc.

Service implementors can use the following guidelines to decide
between `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`:
 (a) Use `UNAVAILABLE` if the client can retry just the failing call.
 (b) Use `ABORTED` if the client should retry at a higher level. For
     example, when a client-specified test-and-set fails, indicating the
     client should restart a read-modify-write sequence.
 (c) Use `FAILED_PRECONDITION` if the client should not retry until
     the system state has been explicitly fixed. For example, if an "rmdir"
     fails because the directory is non-empty, `FAILED_PRECONDITION`
     should be returned since the client should not retry unless
     the files are deleted from the directory.

HTTP Mapping: 400 Bad Request
 - ABORTED: The operation was aborted, typically due to a concurrency issue such as
a sequencer check failure or transaction abort.

See the guidelines above for deciding between `FAILED_PRECONDITION`,
`ABORTED`, and `UNAVAILABLE`.

HTTP Mapping: 409 Conflict
 - OUT_OF_RANGE: The operation was attempted past the valid range.  E.g., seeking or
reading past end-of-file.

Unlike `INVALID_ARGUMENT`, this error indicates a problem that may
be fixed if the system state changes. For example, a 32-bit file
system will generate `INVALID_ARGUMENT` if asked to read at an
offset that is not in the range [0,2^32-1], but it will generate
`OUT_OF_RANGE` if asked to read from an offset past the current
file size.

There is a fair bit of overlap between `FAILED_PRECONDITION` and
`OUT_OF_RANGE`.  We recommend using `OUT_OF_RANGE` (the more specific
error) when it applies so that callers who are iterating through
a space can easily look for an `OUT_OF_RANGE` error to detect when
they are done.

HTTP Mapping: 400 Bad Request
 - UNIMPLEMENTED: The operation is not implemented or is not supported/enabled in this
service.

HTTP Mapping: 501 Not Implemented
 - INTERNAL: Internal errors.  This means that some invariants expected by the
underlying system have been broken.  This error code is reserved
for serious errors.

HTTP Mapping: 500 Internal Server Error
 - UNAVAILABLE: The service is currently unavailable.  This is most likely a
transient condition, which can be corrected by retrying with
a backoff. Note that it is not always safe to retry
non-idempotent operations.

See the guidelines above for deciding between `FAILED_PRECONDITION`,
`ABORTED`, and `UNAVAILABLE`.

HTTP Mapping: 503 Service Unavailable
 - DATA_LOSS: Unrecoverable data loss or corruption.

HTTP Mapping: 500 Internal Server Error

Mimics [https://github.com/googleapis/googleapis/blob/master/google/rpc/code.proto]

A developer-facing error message in English.

Mimics [https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto]

BearerAuth

Next ID: 8

Fireworks AI is a generative AI inference platform to run and customize models with industry-leading speed and production-readiness.

Introduction

Quickstart

This document outlines basic Fireworks AI concepts.

Concepts

Querying text models

Querying vision-language models

Querying embedding models

Using function-calling

Using grammar mode

Using JSON mode

Prompt caching

Use Predicted Outputs to boost output generation speeds for editing / rewriting use cases

Using Predicted Outputs

Overview

Uploading a custom model

Fine-tuning models

Fine-tuning models via API

A guide for downloading base models, merging them with LoRA adapters, and deploying the result using Fireworks.

Merging LoRA adapters with base models

Understand the performance impact of LoRA fine-tuning, optimization strategies, and deployment considerations.

Understanding LoRA performance

Deploying models

Quantization

On-demand deployments

Fireworks runs a global fleet of hardware on which you can deploy your models.

Regions

Reserved capacity

Direct routing enables enterprise users reduce latency to their deployments.

Direct routing

This page describes how to define and upload custom Server Apps to the Fireworks platform by using the Flumina toolkit.

Custom Server Apps with Flumina

Reference documentation for Flumina Server Apps

Flumina Server Apps Reference

Rate limits, spend limits and quotas for serverless inference and on-demand deployments

Rate limits, spend limits and quotas

Add and delete additional users in your Fireworks account

Managing users

Set up custom Single Sign-On (SSO) authentication for Fireworks AI

Custom SSO

Data privacy & security

This page lists common error codes encountered during inference requests using the Fireworks API, their meanings, and potential resolutions.

Inference errors

Creates a model response for the given chat conversation.

Create Chat Completion

Creates a completion for the provided prompt and parameters.

Create Completion

Create embeddings

Generate an image

Transcribe audio

Translate audio

Align transcription

Transcribe audio (realtime)

Start here

Step-by-step guides for hands-on exploration, ideal for interactive learning of AI techniques.

Build with Fireworks

Creative user-contributed projects that showcase innovative applications of Fireworks in diverse contexts.

Community showcase

Learn to create, deploy, and manage resources using Firectl

Getting Started

Authentication for access to your account

Authentication

List various resources in an Fireworks AI account

List Resources

Create a Deployment on Fireworks AI platform

Create a deployment

Create a fine-tuning job with a base model

Create a fine-tuning job

Create a Dataset on Fireworks AI platform

Create a Dataset

Create Model

Deletes resource(s) in a Fireworks AI account

Delete Resources

Deploy Model

Download a model from third-party locations

Download a model

Retrieves model information from Fireworks AI platform

Get Resources

Imports specified model from Fireworks AI Platform

Import Model

Undeploy a model on Fireworks AI platform

Undeploy Model

Updates Resources on Fireworks AI platform

Update Resources

OpenAI compatibility

Get Account

List Deployments

Get Deployment

Create Deployment

Update Deployment

Delete Deployment

CRUD APIs for deployed models.

List Models

Get Model

Update Model

Delete Model

Get Model Upload Endpoint

Get Model Download Endpoint

Validate Model Upload

List Users

Create User

Update User

List Datasets

Get Dataset

Create Dataset

Provides a streamlined way to upload a dataset file in a single API request. This path can handle file sizes up to 150Mb. For larger file sizes use [Get Dataset Upload Endpoint](get-dataset-upload-endpoint).


Upload Dataset Files

Get Dataset Upload Endpoint

Validate Dataset Upload

Update Dataset

Delete Dataset

List Fine-tuning Jobs

Get Fine-tuning Job

Create Fine-tuning Job

Update Fine-tuning Job

Delete Fine-tuning Job

List Batch Jobs

Get Batch Job

Create Batch Job

Update Batch Job

Cancels an existing batch job if it is queued, pending, or running.

Cancel Batch Job

Get Batch Job Logs

Delete Batch Job

Batch Delete Batch Jobs

List Clusters

Get Cluster

Create Cluster

Update Cluster

Delete Cluster

Retrieve connection settings for the cluster to be put in kubeconfig

Get Cluster Connection Info

List Environments

Create Environment

Get Environment

Update Environment

Connects the environment to a node pool.
Returns an error if there is an existing pending connection.

Connect Environment

Disconnects the environment from the node pool. Returns an error
if the environment is not connected to a node pool.

API Documentation

LLM API

Embeddings API

Image API

Audio API

Accounts

Deployments

Deployed Models

Models

Users

Datasets

Fine-tuning Jobs

Get User

Authorizations

Path Parameters

Response