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Documentation Index

Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt

Use this file to discover all available pages before exploring further.

Use the Fireworks REST API to upload custom models programmatically — ideal for automation, CI/CD pipelines, and notebooks. If you just want the quickest path from a terminal, use firectl instead.

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The upload pipeline

Uploading a model via the REST API is a four-step pipeline:
  1. Create the model – Register a model object that references the files you’re about to upload.
  2. Get upload URLs – Request a signed URL for each file.
  3. Upload the files – Upload each file directly to its signed URL.
  4. Validate the upload – Finalize the upload and move the model to READY.
Step 4 is required. Until you call validateUpload, your model stays in the UPLOADING state and cannot be deployed — even after every file has finished uploading.

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Why validation is a separate step

Transfer and validation are decoupled because uploads can arrive several ways (signed-URL upload, S3 import, Azure import) and the transfer runs asynchronously on Fireworks infrastructure. The validateUpload call is what:
  • Confirms every expected file has landed and is intact.
  • Parses your model config to detect architecture, precision, and context length.
  • Transitions the model from UPLOADING to READY so it can be deployed.
Because the transfer is asynchronous, validateUpload returns FAILED_PRECONDITION while files are still landing. Poll it until it returns 200 — at that point the model is READY.

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Full example

This script runs the entire pipeline end to end. Set your API key, account ID, model ID, and the local folder containing your Hugging Face model files. 
import os
import time
import requests

# --- Configuration ---
API_KEY = os.environ["FIREWORKS_API_KEY"]
ACCOUNT_ID = "your-account-id"
MODEL_ID = "my-custom-model"
MODEL_PATH = "/path/to/model/files/"  # folder of Hugging Face files: config.json, *.safetensors, tokenizer, ...

BASE_URL = "https://api.fireworks.ai/v1"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

files = [f for f in os.listdir(MODEL_PATH) if os.path.isfile(os.path.join(MODEL_PATH, f))]
file_sizes = {f: os.path.getsize(os.path.join(MODEL_PATH, f)) for f in files}

# --- Step 1: Create the model ---
requests.post(
    f"{BASE_URL}/accounts/{ACCOUNT_ID}/models",
    headers=HEADERS,
    json={
        "modelId": MODEL_ID,
        "model": {
            "kind": "HF_BASE_MODEL",
            "baseModelDetails": {
                "checkpointFormat": "HUGGINGFACE",
                "worldSize": 1,
                "huggingfaceFiles": files,
            },
        },
    },
).raise_for_status()
print(f"Created model {MODEL_ID}")

# --- Step 2: Get a signed upload URL for each file ---
resp = requests.post(
    f"{BASE_URL}/accounts/{ACCOUNT_ID}/models/{MODEL_ID}:getUploadEndpoint",
    headers=HEADERS,
    json={"filenameToSize": file_sizes, "enableResumableUpload": False},
)
resp.raise_for_status()
upload_urls = resp.json()["filenameToSignedUrls"]

# --- Step 3: Upload each file to its signed URL ---
for filename, url in upload_urls.items():
    size = file_sizes[filename]
    with open(os.path.join(MODEL_PATH, filename), "rb") as f:
        requests.put(
            url,
            data=f,
            headers={
                "Content-Type": "application/octet-stream",
                "x-goog-content-length-range": f"{size},{size}",
            },
        ).raise_for_status()
    print(f"Uploaded {filename}")

# --- Step 4: Validate — poll until the model is READY ---
validate_url = f"{BASE_URL}/accounts/{ACCOUNT_ID}/models/{MODEL_ID}:validateUpload"
while True:
    resp = requests.get(validate_url, headers=HEADERS)
    if resp.status_code == 200:
        print("Model validated and ready to deploy!")
        break
    if "FAILED_PRECONDITION" in resp.text:  # files still landing
        print("Files still finalizing, retrying in 30s...")
        time.sleep(30)
        continue
    resp.raise_for_status()  # any other error is fatal
Every step is safe to retry. create and getUploadEndpoint no-op if the model already exists (returning ALREADY_EXISTS), so an automated uploader that gets interrupted can simply re-run from the top and keep polling validateUpload until it succeeds.

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Troubleshooting: model stuck in UPLOADING

The most common cause is that validateUpload was never called — files transferring successfully does not move the model to READY on its own. Call the endpoint (and poll it): 
curl -X GET "https://api.fireworks.ai/v1/accounts/<ACCOUNT_ID>/models/<MODEL_ID>:validateUpload" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY"
If it returns FAILED_PRECONDITION, files are still landing — wait and retry. If it keeps failing after the transfer should be complete, confirm that every required file was included.

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Next steps

  1. Verify: firectl model get accounts/<ACCOUNT_ID>/models/<MODEL_ID> — look for State: READY.
  2. Deploy: see the Custom Models guide for deployment instructions.