Upload custom model weights to the namespace.
Requirements:
Supported Model Formats:
safetensors: SafeTensors format (recommended for transformers)onnx: ONNX Runtime formatpytorch: PyTorch state_dict or TorchScripthuggingface: HuggingFace model directoryBase Images (auto-selected based on format):
mixpeek/serve-gpu:latest: For safetensors, pytorch, huggingface (includes torch, transformers)mixpeek/serve-minimal:latest: For ONNX (includes onnxruntime only)Important: Models run in fixed base images. You cannot install additional pip packages. All required frameworks (torch, transformers, onnxruntime) are pre-installed.
REQUIRED: Bearer token authentication using your API key. Format: 'Bearer sk_xxxxxxxxxxxxx'. You can create API keys in the Mixpeek dashboard under Organization Settings.
"Bearer YOUR_API_KEY"
"Bearer YOUR_STRIPE_API_KEY"
Model archive (.tar.gz)
Model name
Model version
Model format
safetensors, onnx, pytorch, huggingface ML framework (e.g., sentence-transformers)
Task type (e.g., embedding, classification)
CPU requirements
GPU requirements
Memory in GB
Successful Response
Response model for model upload.
Whether upload succeeded
Unique model identifier
Deployment status
deployed, pending, failed, not_deployed Model inference endpoint
S3 URL where archive is stored