Upload folder using huggingface_hub

README.md

@@ -0,0 +1,237 @@
+---
+library_name: transformers
+tags: []
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+ActionCodec model trained on 3 embodiments:
+- franka_libero_20hz_1s
+- widowx_bridge_5hz_3s
+- franka_droid_15hz_1s
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+TODO
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+```python
+import numpy as np
+from transformers import AutoModel
+np.set_printoptions(suppress=True)
+if __name__ == "__main__":
+    tokenizer = AutoModel.from_pretrained("ZibinDong/ActionCodec-Base", trust_remote_code=True)
+    q99 = np.array([0.9375, 0.91071427, 0.9375, 0.20357142, 0.26357144, 0.375, 1.0])
+    q01 = np.array([-0.87857145, -0.87589288, -0.9375, -0.15107143, -0.20678571, -0.27964285, 0.0])
+    # an example action from physical-intelligence/libero
+    action = np.array(
+        [
+            [0.3268, 0.2089, -0.3295, 0.0000, -0.0868, -0.0611, 1.0000],
+            [0.3696, 0.1955, -0.2866, 0.0000, -0.0793, -0.0643, 1.0000],
+            [0.3857, 0.1929, -0.2759, 0.0000, -0.0782, -0.0654, 1.0000],
+            [0.3964, 0.2089, -0.2786, 0.0000, -0.0761, -0.0654, 1.0000],
+            [0.3321, 0.1741, -0.3268, 0.0000, -0.0793, -0.0686, 1.0000],
+            [0.2250, 0.0964, -0.4232, 0.0000, -0.0932, -0.0761, 1.0000],
+            [0.0723, 0.0000, -0.5625, 0.0000, -0.1339, -0.0879, 1.0000],
+            [0.0536, 0.0000, -0.5652, 0.0000, -0.1521, -0.0921, 1.0000],
+            [0.0750, 0.0000, -0.5464, 0.0000, -0.1511, -0.0964, 1.0000],
+            [0.0723, 0.0000, -0.5411, 0.0000, -0.1414, -0.0986, 1.0000],
+            [0.0402, 0.0000, -0.5196, 0.0000, -0.1350, -0.1007, 1.0000],
+            [0.0080, 0.0000, -0.4795, 0.0000, -0.1189, -0.1018, 1.0000],
+            [0.0000, 0.0000, -0.4527, 0.0000, -0.0986, -0.1018, 1.0000],
+            [0.0000, 0.0000, -0.4313, 0.0000, -0.0846, -0.1018, 1.0000],
+            [-0.0455, -0.0268, -0.3509, 0.0000, -0.0568, -0.1018, 1.0000],
+            [-0.0964, -0.0482, -0.3321, 0.0000, -0.0439, -0.1039, 1.0000],
+            [-0.1768, -0.0562, -0.3402, 0.0000, -0.0300, -0.1050, 1.0000],
+            [-0.2438, -0.0429, -0.3187, 0.0000, -0.0193, -0.0996, 1.0000],
+            [-0.3054, -0.0054, -0.2893, 0.0000, -0.0139, -0.0932, 1.0000],
+            [-0.3509, 0.0000, -0.2598, 0.0000, -0.0054, -0.0879, 1.0000],
+        ],
+    )[None]
+    # normalization
+    normalized_action = np.copy(action)
+    normalized_action[..., :-1] = normalized_action[..., :-1] / np.maximum(np.abs(q99), np.abs(q01))[..., :-1]
+    normalized_action[..., -1] = normalized_action[..., -1] * 2.0 - 1.0  # scale to [-1, 1]
+    normalized_action = normalized_action.clip(-1.0, 1.0)
+    # tokenization
+    tokens = tokenizer.encode(normalized_action)  # numpy (b, n, d) -> list of ints
+    print(tokens)
+    # decoding
+    decoded_action, padding_mask = tokenizer.decode(tokens)  # list of ints -> numpy (b, n, d)
+    # calculate reconstruction error
+    mse_error = np.mean((normalized_action - decoded_action) ** 2)
+    l1_error = np.mean(np.abs(normalized_action - decoded_action))
+    print(f"Reconstruction MSE error: {mse_error:.6f}")
+    print(f"Reconstruction L1 error: {l1_error:.6f}")
+```
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+TODO
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+TODO
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+TODO
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+TODO
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+TODO
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]