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README.md

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+# 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
+
+   Once you've generated the network configuration and state dictionary files, you will need to **convert these files into an ONNX format**.