Launch a custom model

This page shows how to prepare your model repository on Hugging Face so it can be deployed on SynapsAI Cloud. If the model is already on the Hub in Safetensors format with a valid pipeline_tag, you can skip to Deploy a model. We support model architectures available in the Hugging Face transformers and diffusers libraries. Custom architectures and remote code are not supported yet.

Prerequisites

Hugging Face account: huggingface.co
Personal Access Token with read access: huggingface.co/settings/tokens
huggingface_hub, transformers, and safetensors installed

pip install --upgrade huggingface_hub transformers safetensors

Create a model repository

Create a new model repo on Hugging Face (UI or CLI):

UI: huggingface.co/new
CLI:

huggingface-cli login
huggingface-cli repo create <namespace>/<model-name> --type model

Required files

Your repository should include:

Model weights in .safetensors format
Model configuration files (for example, config.json)
Tokenizer files (for example, tokenizer.json, tokenizer_config.json, vocab.json, merges.txt)
Any processors (for example, preprocessor_config.json, image processor files)
A README.md with a pipeline_tag

Refer to the official docs for file layouts:

If your weights are not in Safetensors format, see Convert models to Safetensors.

Push model weights and artifacts

Below is a Python example that saves a model and tokenizer, then pushes them to the Hub.

from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer

login(token="<HF_TOKEN>")

model_id = "<namespace>/<model-name>"

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

model.save_pretrained("./model", safe_serialization=True)
tokenizer.save_pretrained("./model")

model.push_to_hub(model_id, private=True)
tokenizer.push_to_hub(model_id, private=True)

CLI alternative with Git:

cp -r ./model/* .
git add .
git commit -m "Add model weights, config, and tokenizer"
git push

For more details, see the Hugging Face upload guide.

Verify repository readiness

Confirm all required files are present (weights, config, tokenizer, processors).
Confirm weights are in .safetensors format.
Ensure README.md includes a pipeline tag:

pipeline_tag: text-generation

Next, proceed to Deploy a model.

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Prerequisites

Create a model repository

Required files

Push model weights and artifacts

Verify repository readiness

​Prerequisites

​Create a model repository

​Required files

​Push model weights and artifacts

​Verify repository readiness

Prerequisites

Create a model repository

Required files

Push model weights and artifacts

Verify repository readiness