API Reference
Inference
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Fine-tuning
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curl --request GET \
--url https://api.fireworks.ai/v1/accounts/{account_id}/reinforcementFineTuningJobs/{reinforcement_fine_tuning_job_id} \
--header 'Authorization: Bearer <token>'{
"dataset": "<string>",
"evaluator": "<string>",
"name": "<string>",
"displayName": "<string>",
"createTime": "2023-11-07T05:31:56Z",
"completedTime": "2023-11-07T05:31:56Z",
"evaluationDataset": "<string>",
"evalAutoCarveout": true,
"state": "JOB_STATE_UNSPECIFIED",
"status": {
"code": "OK",
"message": "<string>"
},
"createdBy": "<string>",
"trainingConfig": {
"outputModel": "<string>",
"baseModel": "<string>",
"warmStartFrom": "<string>",
"jinjaTemplate": "<string>",
"learningRate": 123,
"maxContextLength": 123,
"loraRank": 123,
"region": "REGION_UNSPECIFIED",
"epochs": 123,
"batchSize": 123,
"gradientAccumulationSteps": 123,
"learningRateWarmupSteps": 123
},
"wandbConfig": {
"enabled": true,
"apiKey": "<string>",
"project": "<string>",
"entity": "<string>",
"runId": "<string>",
"url": "<string>"
},
"outputStats": "<string>",
"inferenceParameters": {
"maxTokens": 123,
"temperature": 123,
"topP": 123,
"n": 123,
"extraBody": "<string>",
"topK": 123
},
"chunkSize": 123,
"outputMetrics": "<string>",
"mcpServer": "<string>"
}Get Reinforcement Fine-tuning Job
curl --request GET \
--url https://api.fireworks.ai/v1/accounts/{account_id}/reinforcementFineTuningJobs/{reinforcement_fine_tuning_job_id} \
--header 'Authorization: Bearer <token>'{
"dataset": "<string>",
"evaluator": "<string>",
"name": "<string>",
"displayName": "<string>",
"createTime": "2023-11-07T05:31:56Z",
"completedTime": "2023-11-07T05:31:56Z",
"evaluationDataset": "<string>",
"evalAutoCarveout": true,
"state": "JOB_STATE_UNSPECIFIED",
"status": {
"code": "OK",
"message": "<string>"
},
"createdBy": "<string>",
"trainingConfig": {
"outputModel": "<string>",
"baseModel": "<string>",
"warmStartFrom": "<string>",
"jinjaTemplate": "<string>",
"learningRate": 123,
"maxContextLength": 123,
"loraRank": 123,
"region": "REGION_UNSPECIFIED",
"epochs": 123,
"batchSize": 123,
"gradientAccumulationSteps": 123,
"learningRateWarmupSteps": 123
},
"wandbConfig": {
"enabled": true,
"apiKey": "<string>",
"project": "<string>",
"entity": "<string>",
"runId": "<string>",
"url": "<string>"
},
"outputStats": "<string>",
"inferenceParameters": {
"maxTokens": 123,
"temperature": 123,
"topP": 123,
"n": 123,
"extraBody": "<string>",
"topK": 123
},
"chunkSize": 123,
"outputMetrics": "<string>",
"mcpServer": "<string>"
}Authorizations
Bearer authentication using your Fireworks API key. Format: Bearer <API_KEY>
Path Parameters
The Account Id
The Reinforcement Fine-tuning Job Id
Query Parameters
The fields to be returned in the response. If empty or "*", all fields will be returned.
Response
A successful response.
The name of the dataset used for training.
The evaluator resource name to use for RLOR fine-tuning job.
The completed time for the reinforcement fine-tuning job.
The name of a separate dataset to use for evaluation.
Whether to auto-carve the dataset for eval.
JobState represents the state an asynchronous job can be in.
- JOB_STATE_PAUSED: Job is paused, typically due to account suspension or manual intervention.
JOB_STATE_UNSPECIFIED, JOB_STATE_CREATING, JOB_STATE_RUNNING, JOB_STATE_COMPLETED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_DELETING, JOB_STATE_WRITING_RESULTS, JOB_STATE_VALIDATING, JOB_STATE_DELETING_CLEANING_UP, JOB_STATE_PENDING, JOB_STATE_EXPIRED, JOB_STATE_RE_QUEUEING, JOB_STATE_CREATING_INPUT_DATASET, JOB_STATE_IDLE, JOB_STATE_CANCELLING, JOB_STATE_EARLY_STOPPED, JOB_STATE_PAUSED Show child attributes
Show child attributes
The status code.
OK, CANCELLED, UNKNOWN, INVALID_ARGUMENT, DEADLINE_EXCEEDED, NOT_FOUND, ALREADY_EXISTS, PERMISSION_DENIED, UNAUTHENTICATED, RESOURCE_EXHAUSTED, FAILED_PRECONDITION, ABORTED, OUT_OF_RANGE, UNIMPLEMENTED, INTERNAL, UNAVAILABLE, DATA_LOSS A developer-facing error message in English.
The email address of the user who initiated this fine-tuning job.
Common training configurations.
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Show child attributes
The model ID to be assigned to the resulting fine-tuned model. If not specified, the job ID will be used.
The name of the base model to be fine-tuned Only one of 'base_model' or 'warm_start_from' should be specified.
The PEFT addon model in Fireworks format to be fine-tuned from Only one of 'base_model' or 'warm_start_from' should be specified.
The learning rate used for training.
The maximum context length to use with the model.
The rank of the LoRA layers.
The region where the fine-tuning job is located.
REGION_UNSPECIFIED, US_IOWA_1, US_VIRGINIA_1, US_ILLINOIS_1, AP_TOKYO_1, US_ARIZONA_1, US_TEXAS_1, US_ILLINOIS_2, EU_FRANKFURT_1, US_TEXAS_2, EU_ICELAND_1, EU_ICELAND_2, US_WASHINGTON_1, US_WASHINGTON_2, US_WASHINGTON_3, AP_TOKYO_2, US_CALIFORNIA_1, US_UTAH_1, US_TEXAS_3, US_GEORGIA_1, US_GEORGIA_2, US_WASHINGTON_4, US_GEORGIA_3 The number of epochs to train for.
The maximum packed number of tokens per batch for training in sequence packing.
The Weights & Biases team/user account for logging training progress.
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Show child attributes
Whether to enable wandb logging.
The API key for the wandb service.
The project name for the wandb service.
The entity name for the wandb service.
The run ID for the wandb service.
The URL for the wandb service.
The output dataset's aggregated stats for the evaluation job.
BIJ parameters.
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Maximum number of tokens to generate per response.
Sampling temperature, typically between 0 and 2.
Top-p sampling parameter, typically between 0 and 1.
Number of response candidates to generate per input.
Additional parameters for the inference request as a JSON string. For example: "{"stop": ["\n"]}".
Top-k sampling parameter, limits the token selection to the top k tokens.
Data chunking for rollout, default size 200, enabled when dataset > 300. Valid range is 1-10,000.
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