| --- |
| license: bsd-2-clause |
| --- |
| # Utilizing Custom ONNX Models Stored in Hugging Face within HSSM |
|
|
| This guide will walk you through the process of using custom ONNX models stored in Hugging Face within HSSM (Hierarchical State Space Model) framework. |
|
|
| ## Prerequisites |
|
|
| 1. Python 3.8 or later. |
| 2. HSSM library installed in your Python environment. |
| 3. A pre-trained ONNX model stored on Hugging Face model hub. |
|
|
| ## Step-by-step guide |
|
|
| ### Step 1: Import necessary libraries |
| ``` |
| import pandas as pd |
| import hssm |
| import ssms.basic_simulators |
| |
| pytensor.config.floatX = "float32" |
| ``` |
| ### Step 2: Define HSSM Configuration |
|
|
| You will have to define the configuration of your model. Make sure you are defining the log-likelihood kind as "approx_differentiable" and providing the Hugging Face model name in the loglik field. |
| |
| ``` |
| my_hssm = hssm.HSSM( |
| data=dataset_lan, |
| loglik_kind = "approx_differentiable", |
| loglik = "levy.onnx", |
| model="custom", |
| model_config= { |
| "backend": "jax", |
| "list_params": ["v", "a", "z", "alpha", "t"], |
| "bounds": { |
| "v": (-3.0, 3.0), |
| "a": (0.3, 3.0), |
| "z": (0.1, 0.9), |
| "alpha": (1.0, 2.0), |
| "t": (1e-3, 2.0), |
| }, |
| } |
| ) |
| ``` |
| This creates an HSSM object my_hssm using the custom ONNX model levy.onnx from the Hugging Face repository. |
| |
| ``` |
| my_hssm.sample(cores=2, draws=500, tune=500, mp_ctx="forkserver") |
| ``` |
|
|
| # Uploading ONNX Files to a Hugging Face Repository |
|
|
| If your ONNX file is not currently housed in your Hugging Face repository, you can include it by adhering to the steps delineated below: |
|
|
| 1. Import the HfApi module from huggingface_hub: |
| |
| ``` |
| from huggingface_hub import HfApi |
| ``` |
| |
| 2. Upload the ONNX file using the upload_file method: |
| |
| ``` |
| api = HfApi() |
| api.upload_file( |
| path_or_fileobj="test.onnx", |
| path_in_repo="test.onnx", |
| repo_id="franklab/HSSM", |
| repo_type="model", |
| create_pr=True, |
| ) |
| ``` |
| The execution of these steps will generate a Pull Request (PR) on Hugging Face, which will subsequently be evaluated by a member of our team. |
| |
| ## Creating a Pull Request and a New ONNX Model |
|
|
| 1. **Creating a Pull Request on Hugging Face** |
|
|
| Navigate to the following link: [Hugging Face PR](https://huggingface.co/franklab/HSSM/blob/refs%2Fpr%2F1/test.onnx) |
|
|
| By doing so, you will **generate a Pull Request on Hugging Face**, which will be reviewed by our team members. |
|
|
| 2. **Creating a Custom ONNX Model** |
|
|
| ### Establish a Network Config and State Dictionary Files in PyTorch |
|
|
| To construct a custom model and save it as an ONNX file, you must create a network configuration file and a state dictionary file in PyTorch. Refer to the instructions outlined in the README of the [LANFactory package](LINK_TO_LANFACTORY_PACKAGE). |
|
|
| ### Convert Network Config and State Dictionary Files to ONNX |
|
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| Once you've generated the network configuration and state dictionary files, you will need to **convert these files into an ONNX format**. |
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|
