# Fast Flux fine tuning API This documentation outlines the API endpoints for initiating and managing Fast Flux fine-tuning requests in Segmind. ### Base URL ``` https://api.segmind.com ``` ### Authentication All requests require an API key for authentication. Include the API key in the headers as follows: ``` --header 'x-api-key: YOUR_API_KEY' ``` ### 1. Initiate Fine-Tune Request #### Description Initiate a new fine-tuning request. Request **Headers** * `x-api-key`: Your API key. * `Content-Type`: Should be `application/json`. **Dataset (data\_source\_path)** * `data_source_path`: A URL pointing to a ZIP file containing your training dataset.\ `Purpose`: Specifies the dataset source for fine-tuning. Must be a valid public URL or a Segmind public URL.\ `Options`: "Public ZIP URL (must support GET & HEAD requests)", "Segmind Public URL using Presigned (public)"\ `Descriptions`:\ \- `Public ZIP URL`: Must be directly accessible and respond properly to both HEAD and GET requests with headers like Content-Length.\ \- `Segmind Presigned URL`: Use Segmind Presigned URL endpoint with public as true to upload as public zip file.\ \- `File Size Limit`: The ZIP file must be under 100 MB in size. Larger files will be rejected or failed by the system. **Body** The request body must be in JSON format. **Request** ``` curl --location 'https://api.segmind.com/finetune/request/submit' \ --header 'x-api-key: YOUR_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "name": "fastfluxtest1", "data_source_path": "Segmind-hosted Public ZIP URL (uploaded via presigned link with public access)" or "any public zip url", "instance_prompt": "1MAN, running in brown suit", "trigger_word": "1MAN", "base_model": "FAST_FLUX", "theme": "subject", "segmind_public": false, "advance_parameters": { "steps": 2000, "face_crop": false, "finetune_type": "LORA" } }' ``` #### Sample Response ``` { "status": "REQUESTED", "finetune_id": "uuid", "name": "fine-tune-job-name" } ``` ### 2. Get the Details of Individual Fine-Tune Request #### Description Retrieve a fine-tuning request along with details. **Request** ``` curl --location --request GET 'https://api.segmind.com/finetune/request/details' \ --header 'x-api-key: YOUR_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "request_id": "FINETUNE_ID" }' ``` #### Sample Response ``` { "finetune": { "id": "uuid", "finetune_id": "uuid", "name": "fine-tune-job-name", "data_source_path": "https://your-bucket.s3.amazonaws.com/path/to/dataset.zip", "instance_prompt": "sample instance prompt", "status": "AVAILABLE", "source_type": "AWS_S3", "base_model": "BASE_MODEL_NAME", "slug": "model-slug", "public_model": false, "error_message": null, "cloud_storage_url": "https://your-bucket.s3.amazonaws.com/path/to/model.safetensors", "created_ts": "2025-01-01T00:00:00Z", "updated_ts": "2025-01-01T00:00:00Z" } } ``` ### 3. Get the List of Fine-Tune Requests #### Description Retrieve a list of fine-tuning requests along with their details. **Request** ``` curl --location 'https://api.segmind.com/finetune/request/list' \ --header 'x-api-key: YOUR_API_KEY' ``` #### Sample Response ``` [ { "id": "uuid", "data_source_path": "https://your-bucket.s3.amazonaws.com/path/to/dataset.zip", "name": "fine-tune-job-name", "status": "AVAILABLE", "error_message": null, "segmind_model_path": null, "advance_parameters": { "steps": 10, "learning_rate": 0.0001, "prompt": "sample prompt", "theme": "sample-theme" }, "segmind_public_model": false, "train_type": "LORA", "source_type": "AWS_S3", "base_model": "BASE_MODEL_NAME", "theme": "sample-theme", "cloud_storage_url": null, "finetune_id": "uuid", "model_information": {} } ] ``` ### 4. Get Fine-Tune Data Upload Pre-Signed URL #### Description Obtain a public pre-signed URL to securely upload fine-tuning data to cloud storage. This URL allows you to upload data directly from your local system or application without needing AWS credentials. #### Usage Call this endpoint to generate a temporary public pre-signed URL. Use the returned URL to upload your dataset file to the specified location via a PUT request. #### Request ``` curl --location --request GET 'https://api.segmind.com/finetune/request/upload/pre-signed-url' \ --header 'x-api-key: YOUR_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "name": "NAME_OF_THE_FILE", "extension": ".zip", "public": true }' ``` #### Sample Response ``` { "presigned_url": "https://finetune-pipeline.s3.amazonaws.com/uploads/{user_id}/{file_id}-{filename}.zip?X-Amz-Algorithm=...&X-Amz-Signature=...", "s3_url": "https://finetune-pipeline.s3.amazonaws.com/uploads/{user_id}/{file_id}-{filename}.zip" } ``` #### Upload File Using the Pre-Signed URL Once you have the presigned\_url, upload your dataset file using the following curl command: #### Request ``` curl -X PUT \ -T your_dataset.zip \ -H "Content-Type: application/zip" \ -H "x-amz-acl: public-read" \ "PRESIGNED_URL" ``` #### Note * Replace your\_dataset.zip with your actual local file name. * Replace PRESIGNED\_URL with the exact presigned\_url value received in the previous step. * The x-amz-acl: public-read header is required if the pre-signed URL is generated with public read access. ### 5. Update Fine-Tuned Model Access #### Description Update the access settings of a fine-tuned model (public/private). #### Request ``` curl --location --request PUT 'https://api.segmind.com/finetune/request/access-update' \ --header 'x-api-key: YOUR_API_KEY' \ --form 'request_id="FINETUNE_REQUEST_ID"' \ --form 'segmind_public="True"' ``` #### Sample Response ``` 200 ``` ### 6. Download the Fine-Tuned Safetensor File #### Description Fast Fine-tuned model weight files (.safetensors) are now publicly accessible via the cloud\_storage\_url. You can download them directly without generating a pre-signed URL or using AWS credentials. #### Request ``` curl -L "https://storage.googleapis.com//.safetensors" -o model.safetensors ``` #### Sample Response ``` https://storage.googleapis.com/my-finetune-bucket/abc12345/model.safetensors ``` ## Webhooks Webhooks provide a way to get real-time updates about finetuning jobs programmatically. You can register a webhook for finetuning jobs in the Developer tab on console. Once a webhook is created, test it, by having it send a sample payload to verify delivery. Once its up, create a finetuning job to receive status updates.\ \ Events are sent to webhooks on 3 status changes:\\ 1. `TRAINING_COMPLETED` : The training is completed on model, and finetuned model is available for download on the `trained_model_url`. 2. `INFERENCE_QUEUED`: Model is being deployed on Segmind inference engine. 3. `AVAILABLE`: Model is deployed, and ready for inferences on `inference_api_url` Note: You can create only 1 webhook at a time for finetune jobs. If you want to change the webhook, please delete the old webhook before creating a new one. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.segmind.com/readme/flux-fine-tuning/fast-flux-fine-tuning-api.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.