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