Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1 @@
+__pycache__

libero_utils.py

@@ -0,0 +1,104 @@
+"""Utils for evaluating policies in LIBERO simulation environments."""
+
+import math
+import os
+
+import imageio
+import numpy as np
+import tensorflow as tf
+from libero.libero import get_libero_path
+from libero.libero.envs import OffScreenRenderEnv
+
+from robot_utils import (
+    DATE,
+    DATE_TIME,
+)
+
+
+def get_libero_env(task, model_family, resolution=256):
+    """Initializes and returns the LIBERO environment, along with the task description."""
+    task_description = task.language
+    task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file)
+    env_args = {"bddl_file_name": task_bddl_file, "camera_heights": resolution, "camera_widths": resolution}
+    env = OffScreenRenderEnv(**env_args)
+    env.seed(0)  # IMPORTANT: seed seems to affect object positions even when using fixed initial state
+    return env, task_description
+
+
+def get_libero_dummy_action(model_family: str):
+    """Get dummy/no-op action, used to roll out the simulation while the robot does nothing."""
+    return [0, 0, 0, 0, 0, 0, -1]
+
+
+def resize_image(img, resize_size):
+    """
+    Takes numpy array corresponding to a single image and returns resized image as numpy array.
+
+    NOTE (Moo Jin): To make input images in distribution with respect to the inputs seen at training time, we follow
+                    the same resizing scheme used in the Octo dataloader, which OpenVLA uses for training.
+    """
+    assert isinstance(resize_size, tuple)
+    # Resize to image size expected by model
+    img = tf.image.encode_jpeg(img)  # Encode as JPEG, as done in RLDS dataset builder
+    img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)  # Immediately decode back
+    img = tf.image.resize(img, resize_size, method="lanczos3", antialias=True)
+    img = tf.cast(tf.clip_by_value(tf.round(img), 0, 255), tf.uint8)
+    img = img.numpy()
+    return img
+
+
+def get_libero_image(obs, resize_size):
+    """Extracts image from observations and preprocesses it."""
+    assert isinstance(resize_size, int) or isinstance(resize_size, tuple)
+    if isinstance(resize_size, int):
+        resize_size = (resize_size, resize_size)
+    img = obs["agentview_image"]
+    img = img[::-1, ::-1]  # IMPORTANT: rotate 180 degrees to match train preprocessing
+    img = resize_image(img, resize_size)
+    return img
+
+
+def save_rollout_video(rollout_images, idx, success, task_description, log_file=None, saved_dir=None):
+    """Saves an MP4 replay of an episode."""
+    if saved_dir is None:
+        rollout_dir = f"./rollouts/{DATE}"
+    else:
+        rollout_dir = f"./rollouts/{saved_dir}/{DATE}"
+    os.makedirs(rollout_dir, exist_ok=True)
+    processed_task_description = task_description.lower().replace(" ", "_").replace("\n", "_").replace(".", "_")[:50]
+    mp4_path = f"{rollout_dir}/{DATE_TIME}--episode={idx}--success={success}--task={processed_task_description}.mp4"
+    video_writer = imageio.get_writer(mp4_path, fps=30)
+    for img in rollout_images:
+        video_writer.append_data(img)
+    video_writer.close()
+    print(f"Saved rollout MP4 at path {mp4_path}")
+    if log_file is not None:
+        log_file.write(f"Saved rollout MP4 at path {mp4_path}\n")
+    return mp4_path
+
+
+def quat2axisangle(quat):
+    """
+    Copied from robosuite: https://github.com/ARISE-Initiative/robosuite/blob/eafb81f54ffc104f905ee48a16bb15f059176ad3/robosuite/utils/transform_utils.py#L490C1-L512C55
+
+    Converts quaternion to axis-angle format.
+    Returns a unit vector direction scaled by its angle in radians.
+
+    Args:
+        quat (np.array): (x,y,z,w) vec4 float angles
+
+    Returns:
+        np.array: (ax,ay,az) axis-angle exponential coordinates
+    """
+    # clip quaternion
+    if quat[3] > 1.0:
+        quat[3] = 1.0
+    elif quat[3] < -1.0:
+        quat[3] = -1.0
+
+    den = np.sqrt(1.0 - quat[3] * quat[3])
+    if math.isclose(den, 0.0):
+        # This is (close to) a zero degree rotation, immediately return
+        return np.zeros(3)
+
+    return (quat[:3] * 2.0 * math.acos(quat[3])) / den

oc_model/README.md

@@ -0,0 +1,202 @@
+---
+base_model: output_hf_model_openx
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## 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]
+### Framework versions
+
+- PEFT 0.11.1

oc_model/adapter_config.json

@@ -0,0 +1,258 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "OpenVLAForActionPrediction",
+    "parent_library": "prismatic.extern.hf.modeling_prismatic"
+  },
+  "base_model_name_or_path": "output_hf_model_openx",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "language_model.model.layers.8.mlp.down_proj",
+    "language_model.model.layers.7.self_attn.k_proj",
+    "language_model.model.layers.16.mlp.gate_proj",
+    "language_model.model.layers.25.self_attn.v_proj",
+    "language_model.model.layers.15.self_attn.v_proj",
+    "language_model.model.layers.3.self_attn.o_proj",
+    "language_model.model.layers.17.self_attn.q_proj",
+    "language_model.model.layers.30.mlp.gate_proj",
+    "language_model.model.layers.16.self_attn.k_proj",
+    "language_model.model.layers.5.self_attn.k_proj",
+    "language_model.model.layers.3.self_attn.v_proj",
+    "language_model.model.layers.9.mlp.up_proj",
+    "language_model.model.layers.28.self_attn.v_proj",
+    "language_model.model.layers.10.self_attn.k_proj",
+    "language_model.model.layers.3.mlp.up_proj",
+    "language_model.model.layers.25.mlp.gate_proj",
+    "language_model.model.layers.20.self_attn.v_proj",
+    "language_model.model.layers.14.self_attn.o_proj",
+    "language_model.model.layers.2.mlp.gate_proj",
+    "language_model.model.layers.17.self_attn.v_proj",
+    "language_model.model.layers.30.self_attn.o_proj",
+    "language_model.lm_head",
+    "language_model.model.layers.1.mlp.up_proj",
+    "language_model.model.layers.28.mlp.up_proj",
+    "language_model.model.layers.4.self_attn.o_proj",
+    "language_model.model.layers.29.mlp.down_proj",
+    "language_model.model.layers.21.mlp.up_proj",
+    "language_model.model.layers.10.self_attn.v_proj",
+    "language_model.model.layers.18.self_attn.o_proj",
+    "language_model.model.layers.19.mlp.down_proj",
+    "language_model.model.layers.2.self_attn.o_proj",
+    "language_model.model.layers.22.self_attn.q_proj",
+    "language_model.model.layers.26.mlp.up_proj",
+    "language_model.model.layers.17.mlp.gate_proj",
+    "language_model.model.layers.11.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.q_proj",
+    "language_model.model.layers.30.self_attn.k_proj",
+    "language_model.model.layers.27.mlp.gate_proj",
+    "language_model.model.layers.6.mlp.gate_proj",
+    "language_model.model.layers.10.mlp.gate_proj",
+    "language_model.model.layers.12.mlp.down_proj",
+    "language_model.model.layers.13.mlp.down_proj",
+    "language_model.model.layers.6.self_attn.q_proj",
+    "language_model.model.layers.0.mlp.down_proj",
+    "language_model.model.layers.20.self_attn.o_proj",
+    "language_model.model.layers.23.self_attn.k_proj",
+    "language_model.model.layers.23.mlp.gate_proj",
+    "language_model.model.layers.3.self_attn.q_proj",
+    "language_model.model.layers.18.self_attn.k_proj",
+    "language_model.model.layers.19.mlp.gate_proj",
+    "language_model.model.layers.20.mlp.down_proj",
+    "language_model.model.layers.21.self_attn.v_proj",
+    "language_model.model.layers.30.mlp.down_proj",
+    "language_model.model.layers.18.self_attn.q_proj",
+    "language_model.model.layers.26.self_attn.o_proj",
+    "language_model.model.layers.19.self_attn.o_proj",
+    "language_model.model.layers.14.self_attn.v_proj",
+    "language_model.model.layers.31.mlp.gate_proj",
+    "language_model.model.layers.11.mlp.up_proj",
+    "language_model.model.layers.24.self_attn.k_proj",
+    "language_model.model.layers.9.mlp.down_proj",
+    "language_model.model.layers.16.self_attn.o_proj",
+    "language_model.model.layers.28.mlp.gate_proj",
+    "language_model.model.layers.4.mlp.up_proj",
+    "language_model.model.layers.31.self_attn.o_proj",
+    "language_model.model.layers.9.self_attn.v_proj",
+    "language_model.model.layers.20.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.q_proj",
+    "language_model.model.layers.19.mlp.up_proj",
+    "language_model.model.layers.28.mlp.down_proj",
+    "language_model.model.layers.26.self_attn.v_proj",
+    "language_model.model.layers.16.self_attn.q_proj",
+    "language_model.model.layers.21.mlp.gate_proj",
+    "language_model.model.layers.6.self_attn.v_proj",
+    "language_model.model.layers.15.self_attn.q_proj",
+    "language_model.model.layers.5.self_attn.o_proj",
+    "language_model.model.layers.14.mlp.gate_proj",
+    "language_model.model.layers.11.self_attn.k_proj",
+    "language_model.model.layers.5.mlp.gate_proj",
+    "language_model.model.layers.10.self_attn.o_proj",
+    "language_model.model.layers.11.mlp.down_proj",
+    "language_model.model.layers.8.mlp.gate_proj",
+    "projector.fc1",
+    "language_model.model.layers.9.self_attn.k_proj",
+    "language_model.model.layers.21.self_attn.o_proj",
+    "language_model.model.layers.5.mlp.down_proj",
+    "language_model.model.layers.8.self_attn.k_proj",
+    "language_model.model.layers.22.self_attn.o_proj",
+    "language_model.model.layers.3.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.o_proj",
+    "language_model.model.layers.30.self_attn.q_proj",
+    "language_model.model.layers.0.self_attn.q_proj",
+    "language_model.model.layers.11.self_attn.o_proj",
+    "language_model.model.layers.17.self_attn.k_proj",
+    "language_model.model.layers.14.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.v_proj",
+    "language_model.model.layers.21.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.v_proj",
+    "language_model.model.layers.0.self_attn.v_proj",
+    "language_model.model.layers.0.mlp.up_proj",
+    "language_model.model.layers.20.self_attn.q_proj",
+    "language_model.model.layers.7.self_attn.v_proj",
+    "language_model.model.layers.23.self_attn.v_proj",
+    "language_model.model.layers.29.self_attn.k_proj",
+    "language_model.model.layers.10.mlp.up_proj",
+    "language_model.model.layers.17.self_attn.o_proj",
+    "language_model.model.layers.16.mlp.up_proj",
+    "language_model.model.layers.6.mlp.down_proj",
+    "language_model.model.layers.13.mlp.gate_proj",
+    "language_model.model.layers.24.mlp.down_proj",
+    "language_model.model.layers.26.self_attn.k_proj",
+    "language_model.model.layers.29.mlp.up_proj",
+    "language_model.model.layers.25.mlp.up_proj",
+    "language_model.model.layers.29.mlp.gate_proj",
+    "language_model.model.layers.2.self_attn.v_proj",
+    "language_model.model.layers.1.self_attn.o_proj",
+    "language_model.model.layers.27.mlp.down_proj",
+    "language_model.model.layers.21.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.q_proj",
+    "language_model.model.layers.9.self_attn.o_proj",
+    "language_model.model.layers.10.self_attn.q_proj",
+    "language_model.model.layers.22.self_attn.k_proj",
+    "language_model.model.layers.12.self_attn.q_proj",
+    "language_model.model.layers.31.self_attn.k_proj",
+    "language_model.model.layers.22.mlp.up_proj",
+    "language_model.model.layers.14.mlp.up_proj",
+    "language_model.model.layers.17.mlp.down_proj",
+    "language_model.model.layers.23.self_attn.o_proj",
+    "language_model.model.layers.1.self_attn.v_proj",
+    "language_model.model.layers.12.self_attn.k_proj",
+    "language_model.model.layers.7.mlp.gate_proj",
+    "language_model.model.layers.20.mlp.up_proj",
+    "language_model.model.layers.31.self_attn.q_proj",
+    "language_model.model.layers.15.self_attn.k_proj",
+    "language_model.model.layers.26.mlp.down_proj",
+    "language_model.model.layers.19.self_attn.k_proj",
+    "language_model.model.layers.6.self_attn.k_proj",
+    "language_model.model.layers.2.self_attn.k_proj",
+    "language_model.model.layers.12.mlp.gate_proj",
+    "language_model.model.layers.13.self_attn.q_proj",
+    "language_model.model.layers.13.mlp.up_proj",
+    "language_model.model.layers.23.mlp.up_proj",
+    "language_model.model.layers.13.self_attn.o_proj",
+    "language_model.model.layers.11.self_attn.v_proj",
+    "language_model.model.layers.23.mlp.down_proj",
+    "language_model.model.layers.19.self_attn.q_proj",
+    "language_model.model.layers.9.mlp.gate_proj",
+    "language_model.model.layers.4.mlp.down_proj",
+    "language_model.model.layers.21.mlp.down_proj",
+    "language_model.model.layers.20.mlp.gate_proj",
+    "language_model.model.layers.8.self_attn.o_proj",
+    "language_model.model.layers.23.self_attn.q_proj",
+    "language_model.model.layers.24.self_attn.q_proj",
+    "language_model.model.layers.6.self_attn.o_proj",
+    "language_model.model.layers.28.self_attn.q_proj",
+    "language_model.model.layers.29.self_attn.o_proj",
+    "language_model.model.layers.24.self_attn.o_proj",
+    "language_model.model.layers.25.self_attn.o_proj",
+    "language_model.model.layers.27.mlp.up_proj",
+    "language_model.model.layers.12.self_attn.o_proj",
+    "language_model.model.layers.22.mlp.gate_proj",
+    "language_model.model.layers.1.mlp.gate_proj",
+    "language_model.model.layers.0.self_attn.k_proj",
+    "language_model.model.layers.1.mlp.down_proj",
+    "language_model.model.layers.12.mlp.up_proj",
+    "language_model.model.layers.28.self_attn.k_proj",
+    "language_model.model.layers.13.self_attn.v_proj",
+    "language_model.model.layers.22.mlp.down_proj",
+    "language_model.model.layers.3.mlp.down_proj",
+    "language_model.model.layers.17.mlp.up_proj",
+    "language_model.model.layers.31.self_attn.v_proj",
+    "language_model.model.layers.18.mlp.gate_proj",
+    "language_model.model.layers.24.mlp.up_proj",
+    "language_model.model.layers.14.mlp.down_proj",
+    "language_model.model.layers.8.self_attn.v_proj",
+    "language_model.model.layers.1.self_attn.q_proj",
+    "language_model.model.layers.2.mlp.up_proj",
+    "language_model.model.layers.26.self_attn.q_proj",
+    "language_model.model.layers.27.self_attn.o_proj",
+    "language_model.model.layers.28.self_attn.o_proj",
+    "language_model.model.layers.15.mlp.gate_proj",
+    "language_model.model.layers.25.mlp.down_proj",
+    "language_model.model.layers.24.self_attn.v_proj",
+    "language_model.model.layers.11.self_attn.q_proj",
+    "language_model.model.layers.15.mlp.down_proj",
+    "projector.fc2",
+    "language_model.model.layers.29.self_attn.q_proj",
+    "language_model.model.layers.15.mlp.up_proj",
+    "language_model.model.layers.27.self_attn.k_proj",
+    "language_model.model.layers.29.self_attn.v_proj",
+    "language_model.model.layers.26.mlp.gate_proj",
+    "language_model.model.layers.24.mlp.gate_proj",
+    "language_model.model.layers.30.self_attn.v_proj",
+    "language_model.model.layers.18.mlp.down_proj",
+    "language_model.model.layers.14.self_attn.q_proj",
+    "language_model.model.layers.18.self_attn.v_proj",
+    "language_model.model.layers.4.self_attn.k_proj",
+    "language_model.model.layers.3.self_attn.k_proj",
+    "language_model.model.layers.22.self_attn.v_proj",
+    "language_model.model.layers.7.mlp.up_proj",
+    "language_model.model.layers.10.mlp.down_proj",
+    "language_model.model.layers.16.self_attn.v_proj",
+    "language_model.model.layers.12.self_attn.v_proj",
+    "language_model.model.layers.2.self_attn.q_proj",
+    "language_model.model.layers.15.self_attn.o_proj",
+    "language_model.model.layers.1.self_attn.k_proj",
+    "language_model.model.layers.30.mlp.up_proj",
+    "language_model.model.layers.19.self_attn.v_proj",
+    "language_model.model.layers.0.mlp.gate_proj",
+    "language_model.model.layers.8.self_attn.q_proj",
+    "language_model.model.layers.18.mlp.up_proj",
+    "language_model.model.layers.31.mlp.down_proj",
+    "language_model.model.layers.31.mlp.up_proj",
+    "language_model.model.layers.25.self_attn.k_proj",
+    "projector.fc3",
+    "language_model.model.layers.7.mlp.down_proj",
+    "language_model.model.layers.16.mlp.down_proj",
+    "language_model.model.layers.13.self_attn.k_proj",
+    "language_model.model.layers.0.self_attn.o_proj",
+    "language_model.model.layers.5.self_attn.v_proj",
+    "language_model.model.layers.2.mlp.down_proj",
+    "language_model.model.layers.5.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.q_proj",
+    "language_model.model.layers.4.mlp.gate_proj",
+    "language_model.model.layers.6.mlp.up_proj",
+    "language_model.model.layers.8.mlp.up_proj",
+    "language_model.model.layers.5.mlp.up_proj",
+    "language_model.model.layers.25.self_attn.q_proj"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

oc_model/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:494a46a1aa21689c4cf9b6b591b75e78de82e187dfa3e31655fe7b97efe44cfa
+size 328592296

oc_model/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<PAD>": 32000
+}

oc_model/dataset_statistics.json

@@ -0,0 +1,127 @@
+{
+  "real_data": {
+    "action": {
+      "mean": [
+        -0.00021713033493142575,
+        3.951489816245157e-06,
+        -6.244335236260667e-05,
+        0.024315927177667618,
+        -0.024200621992349625,
+        0.0001426006929250434,
+        -0.1762954145669937
+      ],
+      "std": [
+        0.005905783269554377,
+        0.010097788646817207,
+        0.005760197062045336,
+        0.9473578929901123,
+        0.9439426064491272,
+        0.028618143871426582,
+        0.9843310117721558
+      ],
+      "max": [
+        0.0263775996863842,
+        0.02954130433499813,
+        0.02553696744143963,
+        4.978769779205322,
+        5.3642754554748535,
+        0.4048313796520233,
+        1.0
+      ],
+      "min": [
+        -0.029638897627592087,
+        -0.029703686013817787,
+        -0.024002285674214363,
+        -4.92792272567749,
+        -5.274268627166748,
+        -0.44714170694351196,
+        -1.0
+      ],
+      "q01": [
+        -0.018638468496501446,
+        -0.0258210021071136,
+        -0.012519038049504161,
+        -4.446948285102844,
+        -4.433915729522705,
+        -0.08356364756822586,
+        -1.0
+      ],
+      "q99": [
+        0.017138871438801287,
+        0.025166765898466083,
+        0.02048220705240963,
+        4.4373928689956665,
+        4.42347291469574,
+        0.08423277527093823,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        false
+      ]
+    },
+    "proprio": {
+      "mean": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "std": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "max": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "min": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ]
+    },
+    "num_transitions": 20148,
+    "num_trajectories": 150
+  }
+}

oc_model/preprocessor_config.json

@@ -0,0 +1,114 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "image_processor_type": "PrismaticImageProcessor",
+  "image_resize_strategy": "resize-naive",
+  "input_sizes": [
+    [
+      3,
+      224,
+      224
+    ],
+    [
+      3,
+      224,
+      224
+    ]
+  ],
+  "interpolations": [
+    "bicubic",
+    "bicubic"
+  ],
+  "means": [
+    [
+      0.485,
+      0.456,
+      0.406
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "processor_class": "PrismaticProcessor",
+  "stds": [
+    [
+      0.229,
+      0.224,
+      0.225
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "tvf_crop_params": [
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    },
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "tvf_do_letterbox": false,
+  "tvf_letterbox_fill": null,
+  "tvf_normalize_params": [
+    {
+      "inplace": false,
+      "mean": [
+        0.484375,
+        0.455078125,
+        0.40625
+      ],
+      "std": [
+        0.228515625,
+        0.2236328125,
+        0.224609375
+      ]
+    },
+    {
+      "inplace": false,
+      "mean": [
+        0.5,
+        0.5,
+        0.5
+      ],
+      "std": [
+        0.5,
+        0.5,
+        0.5
+      ]
+    }
+  ],
+  "tvf_resize_params": [
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    },
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "use_fused_vision_backbone": true
+}

oc_model/pretrained_slots-w-filters_s16.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fb915260b6c164b68b2abb642558980f5eaa6b8178613d089f88067b4234a5b
+size 146434388

oc_model/relate_object_bboxes_w_mask_actionable_s16h1.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:875099ecf58478b785669c75f5537fa9c841cdf695daa67af7043f4296d3a072
+size 901969424

oc_model/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

oc_model/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

oc_model/tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

openvla_utils.py

@@ -0,0 +1,244 @@
+"""Utils for evaluating the OpenVLA policy."""
+
+import json
+import os
+import time
+
+import numpy as np
+import tensorflow as tf
+import torch
+from PIL import Image
+from transformers import AutoConfig, AutoImageProcessor, AutoModelForVision2Seq, AutoProcessor
+
+from prismatic.extern.hf.configuration_prismatic import OpenVLAConfig
+from prismatic.extern.hf.modeling_prismatic import OpenVLAForActionPrediction
+from prismatic.extern.hf.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor
+
+# Initialize important constants and pretty-printing mode in NumPy.
+ACTION_DIM = 7
+DATE = time.strftime("%Y_%m_%d")
+DATE_TIME = time.strftime("%Y_%m_%d-%H_%M_%S")
+DEVICE = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+np.set_printoptions(formatter={"float": lambda x: "{0:0.3f}".format(x)})
+
+# Initialize system prompt for OpenVLA v0.1.
+OPENVLA_V01_SYSTEM_PROMPT = (
+    "A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions."
+)
+
+from peft import PeftModel
+from safetensors.torch import load_file
+
+
+def crop_and_resize(image, crop_scale, batch_size):
+    """
+    Center-crops an image to have area `crop_scale` * (original image area), and then resizes back
+    to original size. We use the same logic seen in the `dlimp` RLDS datasets wrapper to avoid
+    distribution shift at test time.
+
+    Args:
+        image: TF Tensor of shape (batch_size, H, W, C) or (H, W, C) and datatype tf.float32 with
+               values between [0,1].
+        crop_scale: The area of the center crop with respect to the original image.
+        batch_size: Batch size.
+    """
+    # Convert from 3D Tensor (H, W, C) to 4D Tensor (batch_size, H, W, C)
+    assert image.shape.ndims == 3 or image.shape.ndims == 4
+    expanded_dims = False
+    if image.shape.ndims == 3:
+        image = tf.expand_dims(image, axis=0)
+        expanded_dims = True
+
+    # Get height and width of crop
+    new_heights = tf.reshape(tf.clip_by_value(tf.sqrt(crop_scale), 0, 1), shape=(batch_size,))
+    new_widths = tf.reshape(tf.clip_by_value(tf.sqrt(crop_scale), 0, 1), shape=(batch_size,))
+
+    # Get bounding box representing crop
+    height_offsets = (1 - new_heights) / 2
+    width_offsets = (1 - new_widths) / 2
+    bounding_boxes = tf.stack(
+        [
+            height_offsets,
+            width_offsets,
+            height_offsets + new_heights,
+            width_offsets + new_widths,
+        ],
+        axis=1,
+    )
+
+    # Crop and then resize back up
+    image = tf.image.crop_and_resize(image, bounding_boxes, tf.range(batch_size), (224, 224))
+
+    # Convert back to 3D Tensor (H, W, C)
+    if expanded_dims:
+        image = image[0]
+
+    return image
+
+
+def get_vla_action(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    image = Image.fromarray(obs["full_image"])
+    image = image.convert("RGB")
+
+    # (If trained with image augmentations) Center crop image and then resize back up to original size.
+    # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+    #            the original height and width by sqrt(0.9) -- not 0.9!
+    if center_crop:
+        batch_size = 1
+        crop_scale = 0.9
+
+        # Convert to TF Tensor and record original data type (should be tf.uint8)
+        image = tf.convert_to_tensor(np.array(image))
+        orig_dtype = image.dtype
+
+        # Convert to data type tf.float32 and values between [0,1]
+        image = tf.image.convert_image_dtype(image, tf.float32)
+
+        # Crop and then resize back to original size
+        image = crop_and_resize(image, crop_scale, batch_size)
+
+        # Convert back to original data type
+        image = tf.clip_by_value(image, 0, 1)
+        image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+        # Convert back to PIL Image
+        image = Image.fromarray(image.numpy())
+        image = image.convert("RGB")
+
+    # Build VLA prompt
+    if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+        prompt = (
+            f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+        )
+    else:  # OpenVLA
+        prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut: _ _ _ _ _ _ _ _</s>"
+
+    # Process inputs.
+    inputs = processor(prompt, image).to(DEVICE, dtype=torch.float16)
+    inputs['input_ids'][0][-9] = 29871
+    # print(inputs['input_ids']); 1/0
+
+    # Get action.
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action
+
+
+def get_vla_action_v2(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    images = [obs["full_image"], obs["wrist_image"]]
+    input_data = []
+    for image in images:
+        image = Image.fromarray(image)
+        image = image.convert("RGB")
+
+        # (If trained with image augmentations) Center crop image and then resize back up to original size.
+        # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+        #            the original height and width by sqrt(0.9) -- not 0.9!
+        if center_crop:
+            batch_size = 1
+            crop_scale = 0.9
+
+            # Convert to TF Tensor and record original data type (should be tf.uint8)
+            image = tf.convert_to_tensor(np.array(image))
+            orig_dtype = image.dtype
+
+            # Convert to data type tf.float32 and values between [0,1]
+            image = tf.image.convert_image_dtype(image, tf.float32)
+
+            # Crop and then resize back to original size
+            image = crop_and_resize(image, crop_scale, batch_size)
+
+            # Convert back to original data type
+            image = tf.clip_by_value(image, 0, 1)
+            image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+            # Convert back to PIL Image
+            image = Image.fromarray(image.numpy())
+            image = image.convert("RGB")
+        
+        # Build VLA prompt
+        if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+            prompt = (
+                f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+            )
+        else:  # OpenVLA
+            prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut: _ _ _ _ _ _ _ _</s>"
+
+        # Process inputs.
+        inputs = processor(prompt, image).to(DEVICE, dtype=torch.float16)
+        inputs['input_ids'][0][-9] = 29871
+        input_data.append(inputs)
+
+    # Get action.
+    inputs = {}
+    inputs['pixel_values'] = []
+    for datum in input_data:
+        inputs['input_ids'] = datum['input_ids']
+        inputs['attention_mask'] = datum['attention_mask']
+        bz, cc, h, w = datum['pixel_values'].shape
+        datum['pixel_values'] = datum['pixel_values'].reshape(bz, 1, cc, h, w)
+        inputs['pixel_values'].append(datum['pixel_values'])
+    inputs['pixel_values'] = torch.stack(inputs['pixel_values'], dim=1)
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action
+
+
+def get_vla_action_v3(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    images = [obs["full_image"], obs["wrist_image"], obs["depth_full_image"], obs["depth_wrist_image"]]
+    input_data = []
+    for image in images:
+        image = Image.fromarray(image)
+        image = image.convert("RGB")
+
+        # (If trained with image augmentations) Center crop image and then resize back up to original size.
+        # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+        #            the original height and width by sqrt(0.9) -- not 0.9!
+        if center_crop:
+            batch_size = 1
+            crop_scale = 0.9
+
+            # Convert to TF Tensor and record original data type (should be tf.uint8)
+            image = tf.convert_to_tensor(np.array(image))
+            orig_dtype = image.dtype
+
+            # Convert to data type tf.float32 and values between [0,1]
+            image = tf.image.convert_image_dtype(image, tf.float32)
+
+            # Crop and then resize back to original size
+            image = crop_and_resize(image, crop_scale, batch_size)
+
+            # Convert back to original data type
+            image = tf.clip_by_value(image, 0, 1)
+            image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+            # Convert back to PIL Image
+            image = Image.fromarray(image.numpy())
+            image = image.convert("RGB")
+        
+        # Build VLA prompt
+        if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+            prompt = (
+                f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+            )
+        else:  # OpenVLA
+            prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut:"
+
+        # Process inputs.
+        inputs = processor(prompt, image).to(DEVICE, dtype=torch.bfloat16)
+        input_data.append(inputs)
+
+    # Get action.
+    inputs = {}
+    inputs['pixel_values'] = []
+    for datum in input_data:
+        inputs['input_ids'] = datum['input_ids']
+        inputs['attention_mask'] = datum['attention_mask']
+        bz, cc, h, w = datum['pixel_values'].shape
+        datum['pixel_values'] = datum['pixel_values'].reshape(bz, 1, cc, h, w)
+        inputs['pixel_values'].append(datum['pixel_values'])
+    inputs['pixel_values'] = torch.stack(inputs['pixel_values'], dim=1)
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action

orc_model/README.md

@@ -0,0 +1,202 @@
+---
+base_model: output_hf_model_openx
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## 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]
+### Framework versions
+
+- PEFT 0.11.1

orc_model/adapter_config.json

@@ -0,0 +1,258 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "OpenVLAForActionPrediction",
+    "parent_library": "prismatic.extern.hf.modeling_prismatic"
+  },
+  "base_model_name_or_path": "output_hf_model_openx",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "language_model.model.layers.22.mlp.up_proj",
+    "language_model.model.layers.11.self_attn.k_proj",
+    "language_model.model.layers.23.self_attn.q_proj",
+    "language_model.model.layers.15.mlp.up_proj",
+    "language_model.model.layers.20.mlp.up_proj",
+    "language_model.model.layers.29.mlp.gate_proj",
+    "language_model.model.layers.18.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.v_proj",
+    "language_model.model.layers.18.mlp.up_proj",
+    "language_model.model.layers.19.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.o_proj",
+    "language_model.model.layers.20.self_attn.q_proj",
+    "language_model.model.layers.26.self_attn.k_proj",
+    "projector.fc1",
+    "language_model.model.layers.2.self_attn.q_proj",
+    "language_model.model.layers.0.mlp.gate_proj",
+    "language_model.model.layers.17.self_attn.q_proj",
+    "language_model.model.layers.23.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.k_proj",
+    "language_model.model.layers.16.self_attn.q_proj",
+    "language_model.model.layers.11.self_attn.o_proj",
+    "language_model.model.layers.18.self_attn.v_proj",
+    "language_model.model.layers.14.mlp.gate_proj",
+    "language_model.model.layers.4.mlp.down_proj",
+    "language_model.model.layers.12.mlp.gate_proj",
+    "language_model.model.layers.23.self_attn.v_proj",
+    "language_model.model.layers.7.mlp.gate_proj",
+    "language_model.model.layers.17.mlp.down_proj",
+    "language_model.model.layers.21.mlp.down_proj",
+    "language_model.model.layers.22.mlp.down_proj",
+    "language_model.model.layers.13.self_attn.k_proj",
+    "language_model.model.layers.17.mlp.gate_proj",
+    "language_model.model.layers.10.mlp.down_proj",
+    "language_model.model.layers.11.mlp.down_proj",
+    "language_model.model.layers.2.self_attn.k_proj",
+    "language_model.model.layers.2.mlp.gate_proj",
+    "language_model.model.layers.7.mlp.down_proj",
+    "language_model.model.layers.19.mlp.gate_proj",
+    "language_model.lm_head",
+    "language_model.model.layers.1.self_attn.k_proj",
+    "language_model.model.layers.16.mlp.down_proj",
+    "language_model.model.layers.13.mlp.down_proj",
+    "language_model.model.layers.19.self_attn.k_proj",
+    "language_model.model.layers.25.self_attn.v_proj",
+    "language_model.model.layers.4.self_attn.v_proj",
+    "language_model.model.layers.9.mlp.gate_proj",
+    "language_model.model.layers.3.self_attn.q_proj",
+    "language_model.model.layers.7.self_attn.o_proj",
+    "language_model.model.layers.30.self_attn.k_proj",
+    "language_model.model.layers.31.self_attn.k_proj",
+    "language_model.model.layers.0.self_attn.q_proj",
+    "language_model.model.layers.25.mlp.down_proj",
+    "language_model.model.layers.31.mlp.gate_proj",
+    "language_model.model.layers.26.self_attn.v_proj",
+    "language_model.model.layers.27.self_attn.v_proj",
+    "language_model.model.layers.27.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.o_proj",
+    "language_model.model.layers.0.self_attn.k_proj",
+    "language_model.model.layers.28.self_attn.v_proj",
+    "language_model.model.layers.25.mlp.gate_proj",
+    "language_model.model.layers.30.mlp.down_proj",
+    "language_model.model.layers.29.self_attn.k_proj",
+    "language_model.model.layers.17.self_attn.k_proj",
+    "language_model.model.layers.11.self_attn.q_proj",
+    "language_model.model.layers.5.mlp.gate_proj",
+    "language_model.model.layers.24.self_attn.v_proj",
+    "language_model.model.layers.31.mlp.up_proj",
+    "language_model.model.layers.1.mlp.gate_proj",
+    "language_model.model.layers.13.mlp.up_proj",
+    "language_model.model.layers.23.mlp.down_proj",
+    "language_model.model.layers.5.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.q_proj",
+    "language_model.model.layers.14.self_attn.v_proj",
+    "language_model.model.layers.24.mlp.gate_proj",
+    "language_model.model.layers.8.self_attn.o_proj",
+    "language_model.model.layers.31.self_attn.o_proj",
+    "language_model.model.layers.5.self_attn.q_proj",
+    "language_model.model.layers.12.self_attn.q_proj",
+    "language_model.model.layers.26.mlp.down_proj",
+    "language_model.model.layers.16.self_attn.k_proj",
+    "language_model.model.layers.12.self_attn.k_proj",
+    "language_model.model.layers.24.self_attn.k_proj",
+    "language_model.model.layers.26.mlp.gate_proj",
+    "language_model.model.layers.29.mlp.up_proj",
+    "projector.fc2",
+    "language_model.model.layers.5.self_attn.o_proj",
+    "language_model.model.layers.5.mlp.down_proj",
+    "language_model.model.layers.31.mlp.down_proj",
+    "language_model.model.layers.0.mlp.up_proj",
+    "language_model.model.layers.12.mlp.up_proj",
+    "language_model.model.layers.9.mlp.up_proj",
+    "language_model.model.layers.9.self_attn.o_proj",
+    "language_model.model.layers.22.self_attn.q_proj",
+    "language_model.model.layers.20.self_attn.k_proj",
+    "language_model.model.layers.25.self_attn.q_proj",
+    "language_model.model.layers.24.self_attn.o_proj",
+    "language_model.model.layers.26.self_attn.o_proj",
+    "language_model.model.layers.3.self_attn.k_proj",
+    "language_model.model.layers.30.mlp.gate_proj",
+    "language_model.model.layers.10.self_attn.k_proj",
+    "language_model.model.layers.30.self_attn.q_proj",
+    "language_model.model.layers.14.mlp.up_proj",
+    "language_model.model.layers.19.self_attn.v_proj",
+    "language_model.model.layers.21.mlp.gate_proj",
+    "language_model.model.layers.28.mlp.gate_proj",
+    "language_model.model.layers.1.self_attn.q_proj",
+    "language_model.model.layers.11.mlp.gate_proj",
+    "language_model.model.layers.18.mlp.down_proj",
+    "language_model.model.layers.2.mlp.up_proj",
+    "language_model.model.layers.12.self_attn.o_proj",
+    "language_model.model.layers.0.self_attn.v_proj",
+    "language_model.model.layers.10.self_attn.o_proj",
+    "language_model.model.layers.17.self_attn.v_proj",
+    "language_model.model.layers.3.self_attn.o_proj",
+    "language_model.model.layers.20.self_attn.o_proj",
+    "language_model.model.layers.4.mlp.up_proj",
+    "language_model.model.layers.5.self_attn.v_proj",
+    "language_model.model.layers.10.self_attn.q_proj",
+    "language_model.model.layers.13.self_attn.o_proj",
+    "language_model.model.layers.31.self_attn.v_proj",
+    "language_model.model.layers.2.self_attn.o_proj",
+    "language_model.model.layers.21.self_attn.o_proj",
+    "language_model.model.layers.30.mlp.up_proj",
+    "language_model.model.layers.29.self_attn.o_proj",
+    "language_model.model.layers.14.self_attn.k_proj",
+    "language_model.model.layers.15.mlp.down_proj",
+    "language_model.model.layers.8.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.k_proj",
+    "language_model.model.layers.6.self_attn.v_proj",
+    "language_model.model.layers.8.mlp.up_proj",
+    "language_model.model.layers.20.mlp.gate_proj",
+    "language_model.model.layers.21.self_attn.q_proj",
+    "language_model.model.layers.7.mlp.up_proj",
+    "language_model.model.layers.13.self_attn.v_proj",
+    "language_model.model.layers.20.self_attn.v_proj",
+    "language_model.model.layers.8.mlp.down_proj",
+    "language_model.model.layers.11.self_attn.v_proj",
+    "language_model.model.layers.0.mlp.down_proj",
+    "language_model.model.layers.10.mlp.gate_proj",
+    "language_model.model.layers.16.self_attn.o_proj",
+    "language_model.model.layers.15.mlp.gate_proj",
+    "language_model.model.layers.24.mlp.up_proj",
+    "language_model.model.layers.11.mlp.up_proj",
+    "language_model.model.layers.15.self_attn.q_proj",
+    "language_model.model.layers.22.self_attn.k_proj",
+    "language_model.model.layers.19.mlp.up_proj",
+    "language_model.model.layers.18.mlp.gate_proj",
+    "language_model.model.layers.17.self_attn.o_proj",
+    "language_model.model.layers.29.self_attn.v_proj",
+    "language_model.model.layers.16.self_attn.v_proj",
+    "language_model.model.layers.14.self_attn.q_proj",
+    "projector.fc3",
+    "language_model.model.layers.26.self_attn.q_proj",
+    "language_model.model.layers.27.self_attn.q_proj",
+    "language_model.model.layers.28.self_attn.q_proj",
+    "language_model.model.layers.1.mlp.up_proj",
+    "language_model.model.layers.8.self_attn.q_proj",
+    "language_model.model.layers.15.self_attn.k_proj",
+    "language_model.model.layers.30.self_attn.o_proj",
+    "language_model.model.layers.22.self_attn.o_proj",
+    "language_model.model.layers.25.mlp.up_proj",
+    "language_model.model.layers.16.mlp.up_proj",
+    "language_model.model.layers.23.mlp.up_proj",
+    "language_model.model.layers.7.self_attn.q_proj",
+    "language_model.model.layers.19.mlp.down_proj",
+    "language_model.model.layers.28.self_attn.o_proj",
+    "language_model.model.layers.13.self_attn.q_proj",
+    "language_model.model.layers.10.mlp.up_proj",
+    "language_model.model.layers.26.mlp.up_proj",
+    "language_model.model.layers.7.self_attn.v_proj",
+    "language_model.model.layers.6.self_attn.q_proj",
+    "language_model.model.layers.15.self_attn.v_proj",
+    "language_model.model.layers.18.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.q_proj",
+    "language_model.model.layers.1.self_attn.o_proj",
+    "language_model.model.layers.27.mlp.down_proj",
+    "language_model.model.layers.6.self_attn.k_proj",
+    "language_model.model.layers.28.mlp.up_proj",
+    "language_model.model.layers.29.mlp.down_proj",
+    "language_model.model.layers.6.mlp.gate_proj",
+    "language_model.model.layers.1.self_attn.v_proj",
+    "language_model.model.layers.3.mlp.up_proj",
+    "language_model.model.layers.23.mlp.gate_proj",
+    "language_model.model.layers.12.mlp.down_proj",
+    "language_model.model.layers.3.mlp.gate_proj",
+    "language_model.model.layers.28.self_attn.k_proj",
+    "language_model.model.layers.3.mlp.down_proj",
+    "language_model.model.layers.3.self_attn.v_proj",
+    "language_model.model.layers.27.mlp.up_proj",
+    "language_model.model.layers.18.self_attn.o_proj",
+    "language_model.model.layers.4.self_attn.k_proj",
+    "language_model.model.layers.14.mlp.down_proj",
+    "language_model.model.layers.12.self_attn.v_proj",
+    "language_model.model.layers.31.self_attn.q_proj",
+    "language_model.model.layers.30.self_attn.v_proj",
+    "language_model.model.layers.6.mlp.up_proj",
+    "language_model.model.layers.22.mlp.gate_proj",
+    "language_model.model.layers.22.self_attn.v_proj",
+    "language_model.model.layers.25.self_attn.o_proj",
+    "language_model.model.layers.4.mlp.gate_proj",
+    "language_model.model.layers.15.self_attn.o_proj",
+    "language_model.model.layers.1.mlp.down_proj",
+    "language_model.model.layers.8.mlp.gate_proj",
+    "language_model.model.layers.14.self_attn.o_proj",
+    "language_model.model.layers.21.self_attn.k_proj",
+    "language_model.model.layers.9.mlp.down_proj",
+    "language_model.model.layers.10.self_attn.v_proj",
+    "language_model.model.layers.0.self_attn.o_proj",
+    "language_model.model.layers.24.self_attn.q_proj",
+    "language_model.model.layers.29.self_attn.q_proj",
+    "language_model.model.layers.8.self_attn.v_proj",
+    "language_model.model.layers.2.self_attn.v_proj",
+    "language_model.model.layers.6.mlp.down_proj",
+    "language_model.model.layers.13.mlp.gate_proj",
+    "language_model.model.layers.20.mlp.down_proj",
+    "language_model.model.layers.21.mlp.up_proj",
+    "language_model.model.layers.23.self_attn.o_proj",
+    "language_model.model.layers.21.self_attn.v_proj",
+    "language_model.model.layers.24.mlp.down_proj",
+    "language_model.model.layers.16.mlp.gate_proj",
+    "language_model.model.layers.6.self_attn.o_proj",
+    "language_model.model.layers.5.mlp.up_proj",
+    "language_model.model.layers.28.mlp.down_proj",
+    "language_model.model.layers.25.self_attn.k_proj",
+    "language_model.model.layers.2.mlp.down_proj",
+    "language_model.model.layers.17.mlp.up_proj",
+    "language_model.model.layers.19.self_attn.o_proj"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

orc_model/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bc9ade7a25cee73354c4e6f2973c1b2e548b04094280428f3f58d1fb2a9eb4f
+size 328592296

orc_model/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<PAD>": 32000
+}

orc_model/dataset_statistics.json

@@ -0,0 +1,127 @@
+{
+  "real_data": {
+    "action": {
+      "mean": [
+        -0.00021713033493142575,
+        3.951489816245157e-06,
+        -6.244335236260667e-05,
+        0.024315927177667618,
+        -0.024200621992349625,
+        0.0001426006929250434,
+        -0.1762954145669937
+      ],
+      "std": [
+        0.005905783269554377,
+        0.010097788646817207,
+        0.005760197062045336,
+        0.9473578929901123,
+        0.9439426064491272,
+        0.028618143871426582,
+        0.9843310117721558
+      ],
+      "max": [
+        0.0263775996863842,
+        0.02954130433499813,
+        0.02553696744143963,
+        4.978769779205322,
+        5.3642754554748535,
+        0.4048313796520233,
+        1.0
+      ],
+      "min": [
+        -0.029638897627592087,
+        -0.029703686013817787,
+        -0.024002285674214363,
+        -4.92792272567749,
+        -5.274268627166748,
+        -0.44714170694351196,
+        -1.0
+      ],
+      "q01": [
+        -0.018638468496501446,
+        -0.0258210021071136,
+        -0.012519038049504161,
+        -4.446948285102844,
+        -4.433915729522705,
+        -0.08356364756822586,
+        -1.0
+      ],
+      "q99": [
+        0.017138871438801287,
+        0.025166765898466083,
+        0.02048220705240963,
+        4.4373928689956665,
+        4.42347291469574,
+        0.08423277527093823,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        false
+      ]
+    },
+    "proprio": {
+      "mean": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "std": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "max": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "min": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ]
+    },
+    "num_transitions": 20148,
+    "num_trajectories": 150
+  }
+}

orc_model/preprocessor_config.json

@@ -0,0 +1,114 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "image_processor_type": "PrismaticImageProcessor",
+  "image_resize_strategy": "resize-naive",
+  "input_sizes": [
+    [
+      3,
+      224,
+      224
+    ],
+    [
+      3,
+      224,
+      224
+    ]
+  ],
+  "interpolations": [
+    "bicubic",
+    "bicubic"
+  ],
+  "means": [
+    [
+      0.485,
+      0.456,
+      0.406
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "processor_class": "PrismaticProcessor",
+  "stds": [
+    [
+      0.229,
+      0.224,
+      0.225
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "tvf_crop_params": [
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    },
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "tvf_do_letterbox": false,
+  "tvf_letterbox_fill": null,
+  "tvf_normalize_params": [
+    {
+      "inplace": false,
+      "mean": [
+        0.484375,
+        0.455078125,
+        0.40625
+      ],
+      "std": [
+        0.228515625,
+        0.2236328125,
+        0.224609375
+      ]
+    },
+    {
+      "inplace": false,
+      "mean": [
+        0.5,
+        0.5,
+        0.5
+      ],
+      "std": [
+        0.5,
+        0.5,
+        0.5
+      ]
+    }
+  ],
+  "tvf_resize_params": [
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    },
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "use_fused_vision_backbone": true
+}

orc_model/pretrained_slots-w-filters_s16.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fb915260b6c164b68b2abb642558980f5eaa6b8178613d089f88067b4234a5b
+size 146434388

orc_model/relate_object_bboxes_w_mask_actionable_s16h1.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3a25bf18137f9021d5244628d54464dabc08e44d278ceb8cc26f26e415d9107
+size 1002856432

orc_model/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

orc_model/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

orc_model/tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

output_hf_model_openx/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

output_hf_model_openx/README.md

@@ -0,0 +1,99 @@
+---
+library_name: transformers
+tags:
+- robotics
+- vla
+- image-text-to-text
+- multimodal
+- pretraining
+license: mit
+language:
+- en
+pipeline_tag: image-text-to-text
+---
+
+# OpenVLA 7B
+
+OpenVLA 7B (`openvla-7b`) is an open vision-language-action model trained on 970K robot manipulation episodes from the [Open X-Embodiment](https://robotics-transformer-x.github.io/) dataset. 
+The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning.
+
+All OpenVLA checkpoints, as well as our [training codebase](https://github.com/openvla/openvla) are released under an MIT License.
+
+For full details, please read [our paper](https://arxiv.org/abs/2406.09246) and see [our project page](https://openvla.github.io/).
+
+## Model Summary
+
+- **Developed by:** The OpenVLA team consisting of researchers from Stanford, UC Berkeley, Google Deepmind, and the Toyota Research Institute.
+- **Model type:** Vision-language-action (language, image => robot actions)
+- **Language(s) (NLP):** en
+- **License:** MIT
+- **Finetuned from:** [`prism-dinosiglip-224px`](https://github.com/TRI-ML/prismatic-vlms), a VLM trained from:
+  + **Vision Backbone**: DINOv2 ViT-L/14 and SigLIP ViT-So400M/14
+  + **Language Model**: Llama-2
+- **Pretraining Dataset:** [Open X-Embodiment](https://robotics-transformer-x.github.io/) -- specific component datasets can be found [here](https://github.com/openvla/openvla).
+- **Repository:** [https://github.com/openvla/openvla](https://github.com/openvla/openvla)
+- **Paper:** [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/abs/2406.09246)
+- **Project Page & Videos:** [https://openvla.github.io/](https://openvla.github.io/)
+
+## Uses
+
+OpenVLA models take a language instruction and a camera image of a robot workspace as input, and predict (normalized) robot actions consisting of 7-DoF end-effector deltas
+of the form (x, y, z, roll, pitch, yaw, gripper). To execute on an actual robot platform, actions need to be *un-normalized* subject to statistics computed on a per-robot,
+per-dataset basis. See [our repository](https://github.com/openvla/openvla) for more information.
+
+OpenVLA models can be used zero-shot to control robots for specific combinations of embodiments and domains seen in the Open-X pretraining mixture (e.g., for 
+[BridgeV2 environments with a Widow-X robot](https://rail-berkeley.github.io/bridgedata/)). They can also be efficiently *fine-tuned* for new tasks and robot setups
+given minimal demonstration data; [see here](https://github.com/openvla/openvla/blob/main/scripts/finetune.py).
+
+**Out-of-Scope:** OpenVLA models do not zero-shot generalize to new (unseen) robot embodiments, or setups that are not represented in the pretraining mix; in these cases,
+we suggest collecting a dataset of demonstrations on the desired setup, and fine-tuning OpenVLA models instead.
+
+## Getting Started
+
+OpenVLA 7B can be used to control multiple robots for domains represented in the pretraining mixture out-of-the-box. For example,
+here is an example for loading `openvla-7b` for zero-shot instruction following in the [BridgeV2 environments] with a Widow-X robot:
+
+```python
+# Install minimal dependencies (`torch`, `transformers`, `timm`, `tokenizers`, ...)
+# > pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
+from transformers import AutoModelForVision2Seq, AutoProcessor
+from PIL import Image
+
+import torch
+
+# Load Processor & VLA
+processor = AutoProcessor.from_pretrained("openvla/openvla-7b", trust_remote_code=True)
+vla = AutoModelForVision2Seq.from_pretrained(
+    "openvla/openvla-7b",
+    attn_implementation="flash_attention_2",  # [Optional] Requires `flash_attn`
+    torch_dtype=torch.bfloat16, 
+    low_cpu_mem_usage=True, 
+    trust_remote_code=True
+).to("cuda:0")
+
+# Grab image input & format prompt
+image: Image.Image = get_from_camera(...)
+prompt = "In: What action should the robot take to {<INSTRUCTION>}?\nOut:"
+
+# Predict Action (7-DoF; un-normalize for BridgeV2)
+inputs = processor(prompt, image).to("cuda:0", dtype=torch.bfloat16)
+action = vla.predict_action(**inputs, unnorm_key="bridge_orig", do_sample=False)
+
+# Execute...
+robot.act(action, ...)
+```
+
+For more examples, including scripts for fine-tuning OpenVLA models on your own robot demonstration datasets, see [our training repository](https://github.com/openvla/openvla).
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{kim24openvla,
+    title={OpenVLA: An Open-Source Vision-Language-Action Model},
+    author={{Moo Jin} Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
+    journal = {arXiv preprint arXiv:2406.09246},
+    year={2024}
+} 
+```

output_hf_model_openx/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<PAD>": 32000
+}

output_hf_model_openx/config.json

@@ -0,0 +1,3168 @@
+{
+  "arch_specifier": "no-align+fused-gelu-mlp",
+  "architectures": [
+    "OpenVLAForActionPrediction"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_prismatic.OpenVLAConfig",
+    "AutoModelForVision2Seq": "modeling_prismatic.OpenVLAForActionPrediction"
+  },
+  "hf_llm_id": "meta-llama/Llama-2-7b-hf",
+  "image_resize_strategy": "resize-naive",
+  "image_sizes": [
+    224,
+    224
+  ],
+  "llm_backbone_id": "llama2-7b-pure",
+  "llm_max_length": 2048,
+  "model_type": "openvla",
+  "n_action_bins": 256,
+  "norm_stats": {
+    "austin_buds_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.0,
+          1.0,
+          1.0,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "mean": [
+          -0.07678354531526566,
+          0.0036849044263362885,
+          0.05644911900162697,
+          0.0,
+          0.0,
+          0.0,
+          0.3510494828224182
+        ],
+        "min": [
+          -1.0,
+          -1.0,
+          -1.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          -1.0,
+          -0.9599999785423279,
+          -0.8714285492897034,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          1.0,
+          0.8600000143051147,
+          1.0,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "std": [
+          0.6367740631103516,
+          0.37889179587364197,
+          0.47796326875686646,
+          0.0,
+          0.0,
+          0.0,
+          0.47721168398857117
+        ]
+      },
+      "num_trajectories": 50,
+      "num_transitions": 34112,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "austin_sailor_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.0,
+          1.0,
+          1.0,
+          0.0,
+          0.0,
+          0.375,
+          1.0
+        ],
+        "mean": [
+          0.011825348250567913,
+          0.006461074110120535,
+          0.06023626774549484,
+          0.0,
+          0.0,
+          0.0016465914668515325,
+          0.5260950326919556
+        ],
+        "min": [
+          -1.0,
+          -1.0,
+          -1.0,
+          0.0,
+          0.0,
+          -0.375,
+          0.0
+        ],
+        "q01": [
+          -1.0,
+          -0.9828571677207947,
+          -0.6000000238418579,
+          0.0,
+          0.0,
+          -0.17249999940395355,
+          0.0
+        ],
+        "q99": [
+          1.0,
+          0.9457142949104309,
+          1.0,
+          0.0,
+          0.0,
+          0.17892856895923615,
+          1.0
+        ],
+        "std": [
+          0.46348899602890015,
+          0.41240179538726807,
+          0.411862850189209,
+          0.0,
+          0.0,
+          0.0578610822558403,
+          0.49894046783447266
+        ]
+      },
+      "num_trajectories": 240,
+      "num_transitions": 353094,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "austin_sirius_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.0002285242080688,
+          0.960608720779419,
+          1.105179786682129,
+          0.0,
+          0.0,
+          0.341785728931427,
+          1.0
+        ],
+        "mean": [
+          0.07747682929039001,
+          0.03195561468601227,
+          0.04244732856750488,
+          0.0,
+          0.0,
+          -0.01603456400334835,
+          0.43260177969932556
+        ],
+        "min": [
+          -1.0183025598526,
+          -0.9800000190734863,
+          -0.9774575233459473,
+          0.0,
+          0.0,
+          -0.34607142210006714,
+          0.0
+        ],
+        "q01": [
+          -0.780905865430832,
+          -0.5667179036140442,
+          -0.5254343223571777,
+          0.0,
+          0.0,
+          -0.28495091378688814,
+          0.0
+        ],
+        "q99": [
+          0.9569637751579284,
+          0.6971374487876891,
+          0.8124888157844541,
+          0.0,
+          0.0,
+          0.1971428543329239,
+          1.0
+        ],
+        "std": [
+          0.3906329572200775,
+          0.2998155355453491,
+          0.2782271206378937,
+          0.0,
+          0.0,
+          0.08120622485876083,
+          0.49528297781944275
+        ]
+      },
+      "num_trajectories": 559,
+      "num_transitions": 279939,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "bc_z": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.2165454924106598,
+          0.1251407265663147,
+          0.10772687941789627,
+          0.33544227480888367,
+          0.28117990493774414,
+          0.40614867210388184,
+          1.0
+        ],
+        "mean": [
+          -0.009958467446267605,
+          0.0008958321413956583,
+          0.004995597992092371,
+          0.00029755113064311445,
+          -0.008735382929444313,
+          -0.030693737789988518,
+          0.8344562649726868
+        ],
+        "min": [
+          -0.1677047461271286,
+          -0.14630407094955444,
+          -0.10066790133714676,
+          -0.29421567916870117,
+          -0.32101404666900635,
+          -0.4635624885559082,
+          0.0
+        ],
+        "q01": [
+          -0.09220654994249344,
+          -0.06456145539879798,
+          -0.049121275544166565,
+          -0.11594625547528267,
+          -0.14152548640966414,
+          -0.2251061636209488,
+          0.0
+        ],
+        "q99": [
+          0.07628866866230968,
+          0.058019736707210584,
+          0.052540797740221024,
+          0.11740604028105736,
+          0.11703975558280955,
+          0.16729306846857078,
+          1.0
+        ],
+        "std": [
+          0.03053455986082554,
+          0.0231423731893301,
+          0.020641816779971123,
+          0.04155943542718887,
+          0.046427831053733826,
+          0.0769818127155304,
+          0.3610210120677948
+        ]
+      },
+      "num_trajectories": 43264,
+      "num_transitions": 6015535,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "berkeley_autolab_ur5": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.019999999552965164,
+          0.019999999552965164,
+          0.019999999552965164,
+          0.06666667014360428,
+          0.06666667014360428,
+          0.06666667014360428,
+          1.0
+        ],
+        "mean": [
+          0.0005683620693162084,
+          0.001217700308188796,
+          -0.0005296372692100704,
+          0.00021029810886830091,
+          6.0695128922816366e-05,
+          0.001204986940138042,
+          0.6298308372497559
+        ],
+        "min": [
+          -0.019999999552965164,
+          -0.019999999552965164,
+          -0.019999999552965164,
+          -0.06666667014360428,
+          -0.06666667014360428,
+          -0.06666667014360428,
+          0.0
+        ],
+        "q01": [
+          -0.019999999552965164,
+          -0.019999999552965164,
+          -0.019999999552965164,
+          -0.02628571353852749,
+          -0.06666667014360428,
+          -0.03847619146108627,
+          0.0
+        ],
+        "q99": [
+          0.019999999552965164,
+          0.019999999552965164,
+          0.019999999552965164,
+          0.031809523701667786,
+          0.06666667014360428,
+          0.036571428179740906,
+          1.0
+        ],
+        "std": [
+          0.0115329809486866,
+          0.007990492507815361,
+          0.009577835910022259,
+          0.009432995691895485,
+          0.016427582129836082,
+          0.011053967289626598,
+          0.48267969489097595
+        ]
+      },
+      "num_trajectories": 1000,
+      "num_transitions": 97939,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "berkeley_cable_routing": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.9633283019065857,
+          1.0,
+          1.0,
+          0.0,
+          0.0,
+          1.0,
+          0.0
+        ],
+        "mean": [
+          -0.07139874249696732,
+          0.023609008640050888,
+          0.10241943597793579,
+          0.0,
+          0.0,
+          0.049671024084091187,
+          0.0
+        ],
+        "min": [
+          -0.9809081554412842,
+          -0.9554349184036255,
+          -0.9994775056838989,
+          0.0,
+          0.0,
+          -1.0,
+          0.0
+        ],
+        "q01": [
+          -0.5534318816661835,
+          -0.4797285574674606,
+          -0.5314934802055359,
+          0.0,
+          0.0,
+          -0.8855219376087189,
+          0.0
+        ],
+        "q99": [
+          0.42652835428714786,
+          0.5000944086909298,
+          0.639823433756829,
+          0.0,
+          0.0,
+          0.984243879914284,
+          0.0
+        ],
+        "std": [
+          0.1815500408411026,
+          0.1810990273952484,
+          0.21220779418945312,
+          0.0,
+          0.0,
+          0.3475511968135834,
+          0.0
+        ]
+      },
+      "num_trajectories": 1647,
+      "num_transitions": 42328,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "berkeley_fanuc_manipulation": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.009999999776482582,
+          0.009999999776482582,
+          0.009999999776482582,
+          0.03490658476948738,
+          0.03490658476948738,
+          0.03490658476948738,
+          1.0
+        ],
+        "mean": [
+          0.0007744057802483439,
+          -0.00031240080716088414,
+          -0.0015001941937953234,
+          -0.0007515158504247665,
+          -0.00015832878125365824,
+          0.00014327642566058785,
+          0.699295699596405
+        ],
+        "min": [
+          -0.009999999776482582,
+          -0.009999999776482582,
+          -0.009999999776482582,
+          -0.03490658476948738,
+          -0.03490658476948738,
+          -0.03490658476948738,
+          0.0
+        ],
+        "q01": [
+          -0.009999999776482582,
+          -0.009999999776482582,
+          -0.009999999776482582,
+          -0.03490658476948738,
+          0.0,
+          -0.03490658476948738,
+          0.0
+        ],
+        "q99": [
+          0.009999999776482582,
+          0.009999999776482582,
+          0.009999999776482582,
+          0.03490658476948738,
+          0.0,
+          0.03490658476948738,
+          1.0
+        ],
+        "std": [
+          0.0034070091787725687,
+          0.0049921851605176926,
+          0.005344334989786148,
+          0.00759894959628582,
+          0.004081866703927517,
+          0.008568956516683102,
+          0.4586937427520752
+        ]
+      },
+      "num_trajectories": 415,
+      "num_transitions": 62613,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "bridge_orig": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.41691166162490845,
+          0.25864794850349426,
+          0.21218234300613403,
+          3.122201919555664,
+          1.8618112802505493,
+          6.280478477478027,
+          1.0
+        ],
+        "mean": [
+          0.0002334194869035855,
+          0.00013004911306779832,
+          -0.00012762474943883717,
+          -0.0001556558854645118,
+          -0.0004039328487124294,
+          0.00023557482927571982,
+          0.5764579176902771
+        ],
+        "min": [
+          -0.4007510244846344,
+          -0.13874775171279907,
+          -0.22553899884223938,
+          -3.2010786533355713,
+          -1.8618112802505493,
+          -6.279075622558594,
+          0.0
+        ],
+        "q01": [
+          -0.02872725307941437,
+          -0.04170349963009357,
+          -0.026093858778476715,
+          -0.08092105075716972,
+          -0.09288699507713317,
+          -0.20718276381492615,
+          0.0
+        ],
+        "q99": [
+          0.028309678435325586,
+          0.040855254605412394,
+          0.040161586627364146,
+          0.08192047759890528,
+          0.07792850524187081,
+          0.20382574498653397,
+          1.0
+        ],
+        "std": [
+          0.009765930473804474,
+          0.013689135201275349,
+          0.012667362578213215,
+          0.028534092009067535,
+          0.030637972056865692,
+          0.07691419124603271,
+          0.4973701536655426
+        ]
+      },
+      "num_trajectories": 60064,
+      "num_transitions": 2135463,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "cmu_stretch": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.02338407188653946,
+          0.0,
+          0.023404927924275398,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "mean": [
+          0.00036304505192674696,
+          0.0,
+          0.0016466958913952112,
+          0.0,
+          0.0,
+          0.0,
+          0.3987048268318176
+        ],
+        "min": [
+          -0.019353797659277916,
+          0.0,
+          -0.02019215188920498,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          -0.011175686959177256,
+          0.0,
+          -0.0032206363626755773,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.014501785952597848,
+          0.0,
+          0.015056106168776728,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "std": [
+          0.004081828519701958,
+          0.0,
+          0.0037743328139185905,
+          0.0,
+          0.0,
+          0.0,
+          0.48963725566864014
+        ]
+      },
+      "num_trajectories": 135,
+      "num_transitions": 25016,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "dlr_edan_shared_control_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.18991442024707794,
+          0.0739002525806427,
+          0.18064819276332855,
+          0.0866486132144928,
+          0.13464981317520142,
+          0.16910280287265778,
+          1.0
+        ],
+        "mean": [
+          0.006647810339927673,
+          -0.0007657372043468058,
+          0.006522852927446365,
+          0.0011679717572405934,
+          -0.006395625416189432,
+          -0.011902998201549053,
+          0.6985887289047241
+        ],
+        "min": [
+          -0.10054297000169754,
+          -0.08427435159683228,
+          -0.13533438742160797,
+          -0.17556548118591309,
+          -0.18485672771930695,
+          -0.2680685818195343,
+          0.0
+        ],
+        "q01": [
+          -0.02987122368067503,
+          -0.06013262912631035,
+          -0.08286409199237824,
+          -0.05924444157630205,
+          -0.15986866518855095,
+          -0.15636983573436739,
+          0.0
+        ],
+        "q99": [
+          0.08832092039287087,
+          0.042126184627413736,
+          0.11311905644834042,
+          0.0643695573508739,
+          0.03941855944693088,
+          0.156646853685379,
+          1.0
+        ],
+        "std": [
+          0.021393608301877975,
+          0.01814231649041176,
+          0.03374375030398369,
+          0.01743541844189167,
+          0.03394376486539841,
+          0.04641875624656677,
+          0.4588589072227478
+        ]
+      },
+      "num_trajectories": 104,
+      "num_transitions": 8928,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "dobbe": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          38.590423583984375,
+          17.932697296142578,
+          4.843764305114746,
+          1.4372116327285767,
+          0.4340403974056244,
+          1.2057193517684937,
+          0.9998947381973267
+        ],
+        "mean": [
+          -0.0001120665911003016,
+          0.0011229600058868527,
+          -0.00010194431524723768,
+          -7.371398532995954e-05,
+          -0.00067531579406932,
+          -5.6643435527803376e-05,
+          0.6318281888961792
+        ],
+        "min": [
+          -5.700923442840576,
+          -21.605947494506836,
+          -123.72489929199219,
+          -1.7229845523834229,
+          -0.4998578727245331,
+          -0.8867913484573364,
+          1.4196479014572105e-06
+        ],
+        "q01": [
+          -0.01119564864784479,
+          -0.014266146533191203,
+          -0.0071747214533388615,
+          -0.009444301575422287,
+          -0.03990109823644161,
+          -0.017422311007976532,
+          4.003279136668425e-05
+        ],
+        "q99": [
+          0.01015154086053368,
+          0.017181577533483497,
+          0.007216989761218411,
+          0.010380979906767595,
+          0.03556173853576176,
+          0.018032474815845446,
+          0.9982578039169312
+        ],
+        "std": [
+          0.04264938458800316,
+          0.04428559169173241,
+          0.12224084138870239,
+          0.005388413090258837,
+          0.011246449314057827,
+          0.006287882570177317,
+          0.39732322096824646
+        ]
+      },
+      "num_trajectories": 5208,
+      "num_transitions": 1139911,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "fmb_dataset": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.399999976158142,
+          1.0,
+          1.399999976158142,
+          1.0,
+          1.0,
+          1.0,
+          1.0
+        ],
+        "mean": [
+          0.059029702097177505,
+          -0.06476633995771408,
+          -0.09787475317716599,
+          0.004325388930737972,
+          0.00028963794466108084,
+          -0.04457257315516472,
+          0.7336440086364746
+        ],
+        "min": [
+          -1.399999976158142,
+          -1.399999976158142,
+          -1.0,
+          -1.0,
+          -1.0,
+          -1.0,
+          0.0
+        ],
+        "q01": [
+          -0.8257142901420593,
+          -1.399999976158142,
+          -1.0,
+          -1.0,
+          -0.3028571307659149,
+          -1.0,
+          0.0
+        ],
+        "q99": [
+          1.0,
+          0.5257142782211304,
+          1.0,
+          1.0,
+          0.3400000035762787,
+          1.0,
+          1.0
+        ],
+        "std": [
+          0.28809213638305664,
+          0.2820415794849396,
+          0.4626740515232086,
+          0.3266514539718628,
+          0.10842999070882797,
+          0.3440099358558655,
+          0.4435282051563263
+        ]
+      },
+      "num_trajectories": 8612,
+      "num_transitions": 1137459,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "fractal20220817_data": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          2.9984593391418457,
+          22.09052848815918,
+          2.7507524490356445,
+          1.570636510848999,
+          1.5321086645126343,
+          1.5691522359848022,
+          1.0
+        ],
+        "mean": [
+          0.006987582892179489,
+          0.006265917327255011,
+          -0.01262515690177679,
+          0.04333311319351196,
+          -0.005756212864071131,
+          0.0009130256366916001,
+          0.5354204773902893
+        ],
+        "min": [
+          -2.0204520225524902,
+          -5.497899532318115,
+          -2.031663417816162,
+          -1.569917917251587,
+          -1.569892168045044,
+          -1.570419430732727,
+          0.0
+        ],
+        "q01": [
+          -0.22453527510166169,
+          -0.14820013284683228,
+          -0.231589707583189,
+          -0.3517994859814644,
+          -0.4193011274933815,
+          -0.43643461108207704,
+          0.0
+        ],
+        "q99": [
+          0.17824687153100965,
+          0.14938379630446405,
+          0.21842354819178575,
+          0.5892666035890578,
+          0.35272657424211445,
+          0.44796681255102094,
+          1.0
+        ],
+        "std": [
+          0.0692116990685463,
+          0.05970962345600128,
+          0.07353084534406662,
+          0.15610496699810028,
+          0.13164450228214264,
+          0.14593800902366638,
+          0.497110515832901
+        ]
+      },
+      "num_trajectories": 87212,
+      "num_transitions": 3786400,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "furniture_bench_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.10000000149011612,
+          0.10000000149011612,
+          0.10000000149011612,
+          0.8651833534240723,
+          1.0909736156463623,
+          2.863185405731201,
+          1.0
+        ],
+        "mean": [
+          0.00014610752987209707,
+          0.0010830952087417245,
+          0.0006224989192560315,
+          -0.003303206292912364,
+          -0.0026880695950239897,
+          0.018242603167891502,
+          0.48854944109916687
+        ],
+        "min": [
+          -0.10495579987764359,
+          -0.10939455777406693,
+          -0.10000000149011612,
+          -0.971906840801239,
+          -1.0475432872772217,
+          -3.06000018119812,
+          0.0
+        ],
+        "q01": [
+          -0.053988199681043625,
+          -0.05049169331789017,
+          -0.032499241530895236,
+          -0.1953887003660202,
+          -0.41674559473991396,
+          -0.8886768388748169,
+          0.0
+        ],
+        "q99": [
+          0.05414841488003723,
+          0.04965164884924884,
+          0.060055799782276154,
+          0.18231668293476103,
+          0.39867786407470646,
+          0.8772023963928218,
+          1.0
+        ],
+        "std": [
+          0.01610708422958851,
+          0.014891477301716805,
+          0.014014219865202904,
+          0.058274295181035995,
+          0.11417088657617569,
+          0.33479776978492737,
+          0.49991825222969055
+        ]
+      },
+      "num_trajectories": 5100,
+      "num_transitions": 3948057,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "iamlab_cmu_pickup_insert_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.6634981632232666,
+          0.23428471386432648,
+          0.4308285415172577,
+          3.1415927410125732,
+          0.13647015392780304,
+          3.141592502593994,
+          1.0
+        ],
+        "mean": [
+          0.5274372696876526,
+          0.02858201041817665,
+          0.18712575733661652,
+          1.2339589595794678,
+          0.03226623684167862,
+          -1.4199490547180176,
+          0.5550631880760193
+        ],
+        "min": [
+          0.3071657121181488,
+          -0.29754969477653503,
+          0.06578229367733002,
+          -3.1415927410125732,
+          -0.04584203287959099,
+          -3.141592502593994,
+          0.0
+        ],
+        "q01": [
+          0.3148897051811218,
+          -0.20317550599575043,
+          0.06785467118024827,
+          -3.140952730178833,
+          -0.029743434861302376,
+          -3.141091251373291,
+          0.0
+        ],
+        "q99": [
+          0.6472805738449097,
+          0.20846802592277527,
+          0.36855655312538155,
+          3.1409926891326903,
+          0.11424950212240226,
+          3.1410969257354737,
+          1.0
+        ],
+        "std": [
+          0.08108345419168472,
+          0.1116757020354271,
+          0.07747554779052734,
+          2.8737246990203857,
+          0.02774704433977604,
+          2.7678682804107666,
+          0.49695101380348206
+        ]
+      },
+      "num_trajectories": 631,
+      "num_transitions": 146241,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "jaco_play": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.20000000298023224,
+          0.20000000298023224,
+          0.20000000298023224,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "mean": [
+          0.0009658430935814977,
+          -0.00580078037455678,
+          -0.00395062193274498,
+          0.0,
+          0.0,
+          0.0,
+          0.34934908151626587
+        ],
+        "min": [
+          -0.20000000298023224,
+          -0.20000000298023224,
+          -0.20000000298023224,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          -0.20000000298023224,
+          -0.20000000298023224,
+          -0.20000000298023224,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.20000000298023224,
+          0.20000000298023224,
+          0.20000000298023224,
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ],
+        "std": [
+          0.12235074490308762,
+          0.09678777307271957,
+          0.11155334860086441,
+          0.0,
+          0.0,
+          0.0,
+          0.4768252968788147
+        ]
+      },
+      "num_trajectories": 1085,
+      "num_transitions": 77965,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "kuka": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.1697135865688324,
+          0.2777623236179352,
+          0.43710532784461975,
+          0.0,
+          0.0,
+          1.9684287309646606,
+          1.0
+        ],
+        "mean": [
+          -0.0004668905457947403,
+          0.00040138536132872105,
+          -0.001280792523175478,
+          0.0,
+          0.0,
+          -0.03722453489899635,
+          0.4131543040275574
+        ],
+        "min": [
+          -0.159867063164711,
+          -0.2892282009124756,
+          -0.2795473635196686,
+          0.0,
+          0.0,
+          -1.9875637292861938,
+          0.0
+        ],
+        "q01": [
+          -0.06619441494345665,
+          -0.08713878810405731,
+          -0.15083016991615295,
+          0.0,
+          0.0,
+          -0.5415697038173676,
+          0.0
+        ],
+        "q99": [
+          0.06601839080452929,
+          0.08732476785779003,
+          0.18168179214000715,
+          0.0,
+          0.0,
+          0.2923380345106127,
+          1.0
+        ],
+        "std": [
+          0.02083250693976879,
+          0.02915887162089348,
+          0.06422865390777588,
+          0.0,
+          0.0,
+          0.14224295318126678,
+          0.49086448550224304
+        ]
+      },
+      "num_trajectories": 209880,
+      "num_transitions": 2455879,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "nyu_franka_play_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.06424188613891602,
+          0.07027634978294373,
+          0.06129661202430725,
+          6.281067848205566,
+          0.1967729926109314,
+          0.26377415657043457,
+          1.0
+        ],
+        "mean": [
+          0.001021989737637341,
+          -0.00012002651783404872,
+          0.00032894269679673016,
+          0.0015034361276775599,
+          -0.002198522910475731,
+          -0.001663230243138969,
+          0.7230083346366882
+        ],
+        "min": [
+          -0.05952230095863342,
+          -0.07232445478439331,
+          -0.06730806827545166,
+          -6.278434753417969,
+          -0.21479034423828125,
+          -0.3627619743347168,
+          0.0
+        ],
+        "q01": [
+          -0.03199600875377655,
+          -0.032861671447753905,
+          -0.03368805110454559,
+          -0.12080862045288086,
+          -0.12175218224525451,
+          -0.11370223641395569,
+          0.0
+        ],
+        "q99": [
+          0.03101520001888276,
+          0.0373908892273903,
+          0.03646374464035038,
+          0.11764093399047852,
+          0.1258920183777809,
+          0.09366151213645942,
+          1.0
+        ],
+        "std": [
+          0.01327415369451046,
+          0.013215910643339157,
+          0.012822109274566174,
+          0.2732451558113098,
+          0.057022541761398315,
+          0.039172880351543427,
+          0.44752755761146545
+        ]
+      },
+      "num_trajectories": 456,
+      "num_transitions": 44875,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "roboturk": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.39124172925949097,
+          0.4601028263568878,
+          0.4870833456516266,
+          1.816888689994812,
+          1.8240282535552979,
+          1.4824820756912231,
+          1.0
+        ],
+        "mean": [
+          0.0014448732836171985,
+          -0.0015945249469950795,
+          -0.0011753785656765103,
+          0.0023012510500848293,
+          -0.0009382463176734746,
+          -0.00011485807772260159,
+          0.5746025443077087
+        ],
+        "min": [
+          -0.6546999216079712,
+          -0.6365841031074524,
+          -0.4217723608016968,
+          -1.6695482730865479,
+          -1.8023357391357422,
+          -1.4630827903747559,
+          0.0
+        ],
+        "q01": [
+          -0.1342635464668274,
+          -0.19996687173843383,
+          -0.1482972100377083,
+          -0.20720748245716095,
+          -0.09676413893699647,
+          -0.18075634717941286,
+          0.0
+        ],
+        "q99": [
+          0.14956976801157001,
+          0.1805950567126275,
+          0.18841815620660796,
+          0.21615413755178453,
+          0.09457383215427405,
+          0.18543301910162005,
+          1.0
+        ],
+        "std": [
+          0.04935386776924133,
+          0.0635455846786499,
+          0.061164740473032,
+          0.09553450345993042,
+          0.08420111238956451,
+          0.06517903506755829,
+          0.49452081322669983
+        ]
+      },
+      "num_trajectories": 1995,
+      "num_transitions": 187507,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "stanford_hydra_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.02499854564666748,
+          0.02499903365969658,
+          0.024999922141432762,
+          0.24974457919597626,
+          0.24997030198574066,
+          0.24999946355819702,
+          1.0
+        ],
+        "mean": [
+          0.0007790001109242439,
+          0.00013707754260394722,
+          -0.0002548607881180942,
+          0.0012903271708637476,
+          -0.004751681815832853,
+          0.002692886395379901,
+          0.48855218291282654
+        ],
+        "min": [
+          -0.024999044835567474,
+          -0.024999700486660004,
+          -0.02499929815530777,
+          -0.24993225932121277,
+          -0.2499666064977646,
+          -0.2499932497739792,
+          0.0
+        ],
+        "q01": [
+          -0.019992006458342076,
+          -0.02415412735193968,
+          -0.022941758055239916,
+          -0.11085530579090118,
+          -0.12024572037160397,
+          -0.13314770206809043,
+          0.0
+        ],
+        "q99": [
+          0.022886231057345868,
+          0.022358838934451335,
+          0.02410089675337076,
+          0.12370114490389822,
+          0.11323311634361738,
+          0.18474749639630164,
+          1.0
+        ],
+        "std": [
+          0.008022161200642586,
+          0.009131459519267082,
+          0.009574338793754578,
+          0.04122216999530792,
+          0.0384303517639637,
+          0.04606688767671585,
+          0.49976691603660583
+        ]
+      },
+      "num_trajectories": 570,
+      "num_transitions": 358234,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "taco_play": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.4915844202041626,
+          2.1842432022094727,
+          2.6836395263671875,
+          5.035226821899414,
+          2.665864944458008,
+          4.250768661499023,
+          1.0
+        ],
+        "mean": [
+          -0.003845922416076064,
+          0.009671456180512905,
+          0.012780580669641495,
+          -0.005403771996498108,
+          -0.009606587700545788,
+          -0.002480733208358288,
+          0.4263913035392761
+        ],
+        "min": [
+          -4.242457866668701,
+          -3.192805051803589,
+          -1.3371467590332031,
+          -4.202683448791504,
+          -2.6722638607025146,
+          -3.3467135429382324,
+          0.0
+        ],
+        "q01": [
+          -0.7106140398979186,
+          -1.056944659948349,
+          -0.5878450274467468,
+          -0.7682853937149048,
+          -0.7180147767066956,
+          -1.5527938604354858,
+          0.0
+        ],
+        "q99": [
+          0.6482916426658629,
+          1.0051310062408447,
+          0.9480248689651489,
+          0.6926478147506714,
+          0.6351067513227462,
+          1.628010264635086,
+          1.0
+        ],
+        "std": [
+          0.23254038393497467,
+          0.36298269033432007,
+          0.28692901134490967,
+          0.2617705166339874,
+          0.2438892275094986,
+          0.5216503143310547,
+          0.4946896731853485
+        ]
+      },
+      "num_trajectories": 3603,
+      "num_transitions": 237798,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "toto": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          0.6839867234230042,
+          0.4454185664653778,
+          0.7984078526496887,
+          2.120781660079956,
+          1.371164321899414,
+          1.4118704795837402,
+          0.0
+        ],
+        "mean": [
+          0.38542115688323975,
+          0.007769413758069277,
+          0.3632740378379822,
+          -0.6652036905288696,
+          0.1890396922826767,
+          0.03298724442720413,
+          0.0
+        ],
+        "min": [
+          0.09922284632921219,
+          -0.5180193781852722,
+          0.13791072368621826,
+          -2.635117530822754,
+          -1.0734480619430542,
+          -1.9282547235488892,
+          0.0
+        ],
+        "q01": [
+          0.1756722891330719,
+          -0.3077590811252594,
+          0.235383919775486,
+          -2.0908505964279174,
+          -0.6191593289375306,
+          -0.7488683319091797,
+          0.0
+        ],
+        "q99": [
+          0.6136963081359863,
+          0.33704194784164443,
+          0.6681221985816956,
+          0.7422861719131538,
+          0.7955395007133507,
+          0.740464625358582,
+          0.0
+        ],
+        "std": [
+          0.12211652100086212,
+          0.19378550350666046,
+          0.10178236663341522,
+          0.5725259184837341,
+          0.29884573817253113,
+          0.3259911835193634,
+          0.0
+        ]
+      },
+      "num_trajectories": 1003,
+      "num_transitions": 325699,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "ucsd_kitchen_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          678.0,
+          400.0,
+          507.0,
+          180.00001525878906,
+          6.000013828277588,
+          116.99998474121094,
+          1.0
+        ],
+        "mean": [
+          410.37567138671875,
+          116.9518814086914,
+          192.35032653808594,
+          -121.22441864013672,
+          -33.84893035888672,
+          50.016136169433594,
+          0.741813600063324
+        ],
+        "min": [
+          172.0,
+          -166.0,
+          -99.99999237060547,
+          -180.00001525878906,
+          -89.0,
+          -96.00010681152344,
+          0.0
+        ],
+        "q01": [
+          200.00001052856445,
+          -102.31004211425781,
+          -94.99993370056153,
+          -180.00001525878906,
+          -88.00001525878906,
+          -38.999977111816406,
+          0.0
+        ],
+        "q99": [
+          637.0,
+          368.30999999999995,
+          493.0,
+          180.00001525878906,
+          0.999983012676239,
+          105.00001525878906,
+          1.0
+        ],
+        "std": [
+          122.81494903564453,
+          108.8009033203125,
+          130.303466796875,
+          116.28205108642578,
+          27.621843338012695,
+          41.02094650268555,
+          0.43763357400894165
+        ]
+      },
+      "num_trajectories": 150,
+      "num_transitions": 3970,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "utaustin_mutex": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.0,
+          1.0,
+          1.0,
+          0.375,
+          0.375,
+          0.375,
+          1.0
+        ],
+        "mean": [
+          0.06176406890153885,
+          -0.005005486309528351,
+          0.10216785222291946,
+          -0.03314131125807762,
+          0.013895004987716675,
+          -0.011317633092403412,
+          0.5038976669311523
+        ],
+        "min": [
+          -1.0,
+          -1.0,
+          -1.0,
+          -0.375,
+          -0.375,
+          -0.375,
+          0.0
+        ],
+        "q01": [
+          -0.4285714328289032,
+          -0.9800000190734863,
+          -0.5571428537368774,
+          -0.375,
+          -0.15642857551574707,
+          -0.335357129573822,
+          0.0
+        ],
+        "q99": [
+          0.5914285778999329,
+          0.9714285731315613,
+          1.0,
+          0.3278571367263794,
+          0.207857146859169,
+          0.25607141852378845,
+          1.0
+        ],
+        "std": [
+          0.1875014752149582,
+          0.4468473494052887,
+          0.3792876601219177,
+          0.14097853004932404,
+          0.06453701853752136,
+          0.11765272170305252,
+          0.501045286655426
+        ]
+      },
+      "num_trajectories": 1500,
+      "num_transitions": 361883,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    },
+    "viola": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
+        "max": [
+          1.0,
+          1.0,
+          1.0,
+          0.375,
+          0.36321428418159485,
+          0.375,
+          1.0
+        ],
+        "mean": [
+          0.04761844128370285,
+          -0.029204415157437325,
+          0.05586736649274826,
+          -0.002618510741740465,
+          0.006867344491183758,
+          -0.01682133786380291,
+          0.7323777675628662
+        ],
+        "min": [
+          -1.0,
+          -1.0,
+          -1.0,
+          -0.375,
+          -0.375,
+          -0.375,
+          0.0
+        ],
+        "q01": [
+          -0.9628571271896362,
+          -1.0,
+          -1.0,
+          -0.26249998807907104,
+          -0.21321429312229156,
+          -0.3385714292526245,
+          0.0
+        ],
+        "q99": [
+          0.9114285707473755,
+          0.868571400642395,
+          1.0,
+          0.2817857265472412,
+          0.2239285707473755,
+          0.3557142913341522,
+          1.0
+        ],
+        "std": [
+          0.39157867431640625,
+          0.4076525568962097,
+          0.40077948570251465,
+          0.10023996233940125,
+          0.0844319611787796,
+          0.10375042259693146,
+          0.44260647892951965
+        ]
+      },
+      "num_trajectories": 150,
+      "num_transitions": 76324,
+      "proprio": {
+        "max": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "mean": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "min": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q01": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "q99": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "std": [
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0,
+          0.0
+        ]
+      }
+    }
+  },
+  "output_projector_states": false,
+  "pad_to_multiple_of": 64,
+  "pad_token_id": 32000,
+  "text_config": {
+    "model_type": "llama",
+    "pad_token_id": 32000,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 32064
+  },
+  "timm_model_ids": [
+    "vit_large_patch14_reg4_dinov2.lvd142m",
+    "vit_so400m_patch14_siglip_224"
+  ],
+  "timm_override_act_layers": [
+    null,
+    null
+  ],
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_fused_vision_backbone": true,
+  "vision_backbone_id": "dinosiglip-vit-so-224px"
+}

output_hf_model_openx/configuration_prismatic.py

@@ -0,0 +1,140 @@
+"""
+configuration_prismatic.py
+
+HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
+Default configuration specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from transformers import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+
+# === Utilities for Mapping Prismatic names to HF names ===
+# fmt: off
+VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
+    "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
+
+    "clip-vit-l-336px": [336],
+    "siglip-vit-so400m-384px": [384],
+
+    "dinoclip-vit-l-336px": [336, 336],
+    "dinosiglip-vit-so-224px": [224, 224],
+    "dinosiglip-vit-so-384px": [384, 384],
+}
+VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
+    "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
+    "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
+
+    "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
+    "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
+
+    "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
+    "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
+
+    "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
+    "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
+    "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
+}
+TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
+    "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
+    "dinov2-vit-l": [None], "in1k-vit-l": [None],
+    "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
+    "dinoclip-vit-l-336px": [None, "quick_gelu"],
+    "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
+}
+
+LLM_BACKBONE_TO_HF_PATH = {
+    "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
+    "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
+
+    "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
+
+    "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
+    "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
+
+    "phi-2-3b": "microsoft/phi-2",
+}
+LLM_BACKBONE_TO_HF_METACLASS = {
+    "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
+    "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
+
+    "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
+
+    "phi-2-3b": "phi",
+}
+
+VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
+VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
+# fmt: on
+
+
+class PrismaticConfig(PretrainedConfig):
+    model_type: str = "prismatic"
+    is_composition: bool = False
+
+    def __init__(
+        self,
+        vision_backbone_id: str = "siglip-vit-so400m",
+        llm_backbone_id: str = "vicuna-v15-7b",
+        arch_specifier: str = "no-align+gelu-mlp",
+        use_fused_vision_backbone: Optional[bool] = None,
+        image_resize_strategy: str = "letterbox",
+        text_config: Optional[Dict[str, Any]] = None,
+        llm_max_length: int = 2048,
+        pad_token_id: int = 32000,
+        pad_to_multiple_of: int = 64,
+        output_projector_states: bool = False,
+        **kwargs: str,
+    ) -> None:
+        if vision_backbone_id not in VALID_VISION_BACKBONES:
+            raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
+
+        if llm_backbone_id not in VALID_LLM_BACKBONES:
+            raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
+
+        # Set Prismatic Configuration Fields
+        self.vision_backbone_id = vision_backbone_id
+        self.llm_backbone_id = llm_backbone_id
+        self.arch_specifier = arch_specifier
+        self.output_projector_states = output_projector_states
+
+        # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
+        self.use_fused_vision_backbone = (
+            use_fused_vision_backbone
+            if use_fused_vision_backbone is not None
+            else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
+        )
+
+        self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
+        self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
+        self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
+        self.image_resize_strategy = image_resize_strategy
+
+        self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
+        self.llm_max_length = llm_max_length
+        self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
+
+        # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
+        self.text_config = (
+            CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
+            if text_config is not None
+            else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
+        )
+
+        # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+
+class OpenVLAConfig(PrismaticConfig):
+    model_type: str = "openvla"
+
+    def __init__(
+        self,
+        norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
+        n_action_bins: int = 256,
+        **kwargs: str,
+    ) -> None:
+        self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
+
+        super().__init__(**kwargs)

output_hf_model_openx/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 32000,
+  "transformers_version": "4.40.1"
+}

output_hf_model_openx/model-00001-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10d8636256018712c5e5c823d12e22b5797f99bb721bd123bf6bf2379892be85
+size 6948961960

output_hf_model_openx/model-00002-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2050b14f21d48904d269f48d5a980fecea87cd7b36641d9b0f015e72d1fe216a
+size 6971232040

output_hf_model_openx/model-00003-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea65305a1577f36f721965bf84c8caec0a948ce7ce84d754701637376c531fef
+size 1162406824

output_hf_model_openx/model.safetensors.index.json

@@ -0,0 +1,989 @@
+{
+  "metadata": {
+    "total_size": 15082474368
+  },
+  "weight_map": {
+    "language_model.lm_head.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.embed_tokens.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.0.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.1.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.10.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.11.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.12.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.12.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.12.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.12.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.12.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.12.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.12.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.12.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.12.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.13.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.13.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.14.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.15.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.16.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.17.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.18.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.19.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.2.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.2.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.20.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.20.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.21.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.22.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.23.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.24.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.25.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.26.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.27.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.input_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.28.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.29.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.29.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.29.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.29.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
+    "language_model.model.layers.3.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.3.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.30.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.30.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.input_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.post_attention_layernorm.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.31.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
+    "language_model.model.layers.4.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.4.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.5.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.6.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.7.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "language_model.model.norm.weight": "model-00003-of-00003.safetensors",
+    "projector.fc1.bias": "model-00001-of-00003.safetensors",
+    "projector.fc1.weight": "model-00001-of-00003.safetensors",
+    "projector.fc2.bias": "model-00001-of-00003.safetensors",
+    "projector.fc2.weight": "model-00001-of-00003.safetensors",
+    "projector.fc3.bias": "model-00001-of-00003.safetensors",
+    "projector.fc3.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.0.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.1.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.10.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.11.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.12.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.13.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.14.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.15.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.16.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.17.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.18.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.19.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.2.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.20.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.21.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.22.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.23.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.3.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.4.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.5.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.6.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.7.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.8.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.ls1.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.ls2.scale_factor": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.blocks.9.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.cls_token": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.norm.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.norm.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.patch_embed.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.patch_embed.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.pos_embed": "model-00001-of-00003.safetensors",
+    "vision_backbone.featurizer.reg_token": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.kv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.kv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.latent": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.norm.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.norm.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.q.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.attn_pool.q.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.0.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.1.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.10.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.11.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.12.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.13.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.14.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.15.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.16.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.17.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.18.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.19.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.2.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.20.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.21.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.22.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.23.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.24.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.25.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.26.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.3.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.4.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.5.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.6.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.7.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.8.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.attn.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.attn.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.attn.qkv.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.attn.qkv.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.mlp.fc1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.mlp.fc1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.mlp.fc2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.norm1.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.norm1.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.norm2.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.blocks.9.norm2.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.norm.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.norm.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.patch_embed.proj.bias": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.patch_embed.proj.weight": "model-00001-of-00003.safetensors",
+    "vision_backbone.fused_featurizer.pos_embed": "model-00001-of-00003.safetensors"
+  }
+}

output_hf_model_openx/modeling_prismatic.py

@@ -0,0 +1,562 @@
+"""
+modeling_prismatic.py
+
+Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
+from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
+logic in `prismatic.models.vlms.prismatic.py`.
+
+Note =>> for the time being, not adding the custom HF "docstring" formatting.
+
+References [LLaVa, IDEFICS-2]:
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
+"""
+
+import logging
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import timm
+import tokenizers
+import torch
+import torch.nn as nn
+import transformers
+from timm.models.vision_transformer import LayerScale
+from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import ModelOutput
+
+from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
+
+# Get Logger
+logger = logging.getLogger(__name__)
+
+
+# === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
+IGNORE_INDEX = -100
+
+
+# === Utility Functions for Monkey-Patching ===
+def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        result = fn(*args, **kwargs)
+        return result[0] if isinstance(result, tuple) else result
+
+    return wrapper
+
+
+# HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
+#   =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
+#   =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
+def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
+    return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
+
+
+def ls_apply_patch(ls_module: LayerScale):
+    ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
+    ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
+    del ls_module.gamma
+
+
+# === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
+class PrismaticVisionBackbone(nn.Module):
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool,
+        image_sizes: List[int],
+        timm_model_ids: List[str],
+        timm_override_act_layers: List[Optional[str]],
+    ) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+
+        # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
+        #   =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
+        #               Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
+        assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
+        self.featurizer = timm.create_model(
+            timm_model_ids[0],
+            pretrained=False,
+            num_classes=0,
+            img_size=image_sizes[0],
+            act_layer=timm_override_act_layers[0],
+        )
+        self.featurizer.forward = unpack_tuple(
+            partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
+        )
+        self.embed_dim = self.featurizer.embed_dim
+
+        # If `use_fused_vision_backbone` =>> create "beta" featurizer
+        if self.use_fused_vision_backbone:
+            self.fused_featurizer = timm.create_model(
+                timm_model_ids[1],
+                pretrained=False,
+                num_classes=0,
+                img_size=image_sizes[1],
+                act_layer=timm_override_act_layers[1],
+            )
+            self.fused_featurizer.forward = unpack_tuple(
+                partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
+            )
+            self.embed_dim += self.fused_featurizer.embed_dim
+
+        # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
+        for module in self.featurizer.modules():
+            if isinstance(module, LayerScale):
+                ls_apply_patch(module)
+
+        if self.use_fused_vision_backbone:
+            for module in self.fused_featurizer.modules():
+                if isinstance(module, LayerScale):
+                    ls_apply_patch(module)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
+        if not self.use_fused_vision_backbone:
+            return self.featurizer(pixel_values)
+
+        # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
+        img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
+        patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
+
+        return torch.cat([patches, patches_fused], dim=2)
+
+
+# === Prismatic Projector (nn.Module) Definitions ===
+class PrismaticProjector(nn.Module):
+    def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.vision_dim, self.llm_dim = vision_dim, llm_dim
+
+        # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
+        if not self.use_fused_vision_backbone:
+            self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
+            self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+        else:
+            initial_projection_dim = 4 * vision_dim
+            self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
+            self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
+            self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+            self.act_fn2 = nn.GELU()
+
+    def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
+        if not self.use_fused_vision_backbone:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+        else:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+            projected_features = self.act_fn2(projected_features)
+            projected_features = self.fc3(projected_features)
+
+        return projected_features
+
+
+# === Main HF Class Definitions ===
+@dataclass
+class PrismaticCausalLMOutputWithPast(ModelOutput):
+    """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+    # Additions for VLMs
+    projector_features: Optional[torch.FloatTensor] = None
+
+
+class PrismaticPreTrainedModel(PreTrainedModel):
+    config_class: PretrainedConfig = PrismaticConfig
+    base_model_prefix: str = "model"
+    supports_gradient_checkpointing: bool = True
+
+    _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
+    _skip_keys_device_placement: str = "past_key_values"
+    _supports_flash_attn_2: bool = True
+
+    def _init_weights(self, module: nn.Module) -> None:
+        # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
+        #   => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
+        #      https://github.com/TRI-ML/prismatic-vlms
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @property
+    def _supports_sdpa(self) -> bool:
+        """Check LLM supports SDPA Attention"""
+        return self.language_model._supports_sdpa
+
+
+class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
+    def __init__(self, config: PrismaticConfig) -> None:
+        super().__init__(config)
+
+        # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
+        if config.use_fused_vision_backbone is None:
+            raise ValueError("Missing config field `use_fused_vision_backbone`")
+
+        if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
+            raise NotImplementedError(
+                "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
+                "if you urgently need support for latest TIMM versions."
+            )
+
+        if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
+            logger.warning(
+                f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
+                f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
+                f"there might be inference-time regressions due to dependency changes. If in doubt, please"
+                f"use the above versions."
+            )
+
+        # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
+        self.vision_backbone = PrismaticVisionBackbone(
+            config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
+        )
+
+        # Create Multimodal Projector
+        self.projector = PrismaticProjector(
+            config.use_fused_vision_backbone,
+            vision_dim=self.vision_backbone.embed_dim,
+            llm_dim=config.text_config.hidden_size,
+        )
+
+        # Instantiate LLM Backbone
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
+        self.vocab_size = config.text_config.vocab_size
+        self.pad_token_id = config.pad_token_id
+
+        # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
+        self.post_init()
+
+    # === `PreTrainedModel` Boilerplate ===
+    def get_input_embeddings(self) -> nn.Module:
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value: nn.Module) -> None:
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def get_decoder(self) -> nn.Module:
+        return self.language_model.get_decoder()
+
+    def set_decoder(self, decoder: nn.Module) -> None:
+        self.language_model.set_decoder(decoder)
+
+    def tie_weights(self) -> None:
+        self.language_model.tie_weights()  # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
+
+    def resize_token_embeddings(
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
+    ) -> nn.Embedding:
+        updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+
+        # Update config/instance variables
+        self.config.text_config.vocab_size = updated_embeddings.num_embeddings
+        self.vocab_size = updated_embeddings.num_embeddings
+
+        return updated_embeddings
+
+    # === Core Prismatic VLM `forward()` Logic ===
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_projector_features = output_projector_features if output_projector_features is not None else False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
+        use_cache = use_cache and not self.training
+
+        # Instantiate Placeholder for Projector Features
+        projected_patch_embeddings = None
+
+        # Note :: We only support forward passes with the following cases:
+        #   => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
+        #   => Unimodal Forward :: (pixel_values is None)
+        #   => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
+
+        # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
+        if input_ids.shape[1] == 1:
+            assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
+            assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
+            assert labels is None, "Unexpected key `labels` provided during cached generation!"
+
+            language_model_output = self.language_model(
+                input_ids=input_ids,
+                attention_mask=None,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Handle Unimodal Forward ===
+        elif pixel_values is None:
+            assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
+            assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
+
+            language_model_output = self.language_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=None,
+                past_key_values=None,
+                inputs_embeds=None,
+                labels=labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Handle Multimodal Forward ===
+        elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
+            assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
+
+            # Visual Feature Extraction
+            patch_features = self.vision_backbone(pixel_values)
+
+            # Projection Logic =>> Update Attention Mask
+            projected_patch_embeddings = self.projector(patch_features)
+            projected_patch_attention_mask = None
+            if attention_mask is not None:
+                projected_patch_attention_mask = torch.full(
+                    (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                    fill_value=True,
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+
+            # Get Input Embeddings (from Language Model Embeddings)
+            input_embeddings = self.get_input_embeddings()(input_ids)
+
+            # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+            multimodal_embeddings = torch.cat(
+                [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
+            )
+            multimodal_attention_mask = None
+            if attention_mask is not None:
+                multimodal_attention_mask = torch.cat(
+                    [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
+                )
+
+            # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
+            multimodal_labels = None
+            if labels is not None:
+                projected_patch_labels = torch.full(
+                    (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                    fill_value=IGNORE_INDEX,
+                    dtype=labels.dtype,
+                    device=labels.device,
+                )
+                multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
+
+            # Dispatch to Language Model
+            language_model_output = self.language_model(
+                input_ids=None,
+                attention_mask=multimodal_attention_mask,
+                position_ids=None,
+                past_key_values=None,
+                inputs_embeds=multimodal_embeddings,
+                labels=multimodal_labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Otherwise =>> Assume Invalid! ===
+        elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
+            raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
+
+        else:
+            raise ValueError(
+                "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
+                f"=> `input_ids` = {input_ids is not None}\n"
+                f"=> `attention_mask` = {attention_mask is not None}\n"
+                f"=> `pixel_values` = {pixel_values is not None}\n"
+                f"=> `labels` = {labels is not None}\n"
+                f"=> `input_embeds` = {inputs_embeds is not None}\n"
+                f"=> `past_key_values` = {past_key_values is not None}\n"
+                f"=> `use_cache` = {use_cache}"
+            )
+
+        # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
+        if not return_dict:
+            if output_projector_features and (projected_patch_embeddings is not None):
+                return *language_model_output, projected_patch_embeddings
+
+            return language_model_output
+
+        return PrismaticCausalLMOutputWithPast(
+            loss=language_model_output.loss,
+            logits=language_model_output.logits,
+            past_key_values=language_model_output.past_key_values,
+            hidden_states=language_model_output.hidden_states,
+            attentions=language_model_output.attentions,
+            projector_features=projected_patch_embeddings,
+        )
+
+    # === GenerationMixin Methods ===
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs: str,
+    ) -> Dict[str, torch.Tensor]:
+        """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
+        if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
+            (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
+        ):
+            raise ValueError("Generation with batch size > 1 is not currently supported!")
+
+        # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        # If `input_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"input_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        # Make sure `pixel_values` are preserved in `model_inputs`
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+            }
+        )
+
+        return model_inputs
+
+    # Defer to Language Model (all handle this differently, with different return types)
+    def _reorder_cache(self, *args, **kwargs) -> Any:
+        return self.language_model._reorder_cache(*args, **kwargs)
+
+
+class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
+    config_class: PretrainedConfig = OpenVLAConfig
+
+    def __init__(self, config: OpenVLAConfig) -> None:
+        super().__init__(config)
+        self.norm_stats = config.norm_stats
+
+        # Compute action bins
+        self.bins = np.linspace(-1, 1, config.n_action_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # Compute vocab size for de-tokenization -- revert added "multiple of"
+        self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
+
+    def predict_action(
+        self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """Thin wrapper around .generate() that decodes predicted actions and unnormalizes them."""
+        # If the special empty token ('') does not already appear after the colon (':') token in the prompt
+        # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
+        if not torch.all(input_ids[:, -1] == 29871):
+            input_ids = torch.cat(
+                (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
+            )
+
+        # Run VLA inference
+        generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
+
+        # Extract predicted action tokens and translate into (normalized) continuous actions
+        predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
+        discretized_actions = self.vocab_size - predicted_action_token_ids
+        discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+        normalized_actions = self.bin_centers[discretized_actions]
+
+        # Unnormalize actions
+        action_norm_stats = self.get_action_stats(unnorm_key)
+        mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+        action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
+        if unnorm_key is None:
+            assert len(norm_stats) == 1, (
+                f"Your model was trained on more than one dataset, "
+                f"please pass a `unnorm_key` from the following options to choose the statistics "
+                f"used for un-normalizing actions: {norm_stats.keys()}"
+            )
+            unnorm_key = next(iter(norm_stats.keys()))
+
+        assert unnorm_key in norm_stats, (
+            f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
+            f"please choose from: {norm_stats.keys()}"
+        )
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Get the dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return len(self.norm_stats[unnorm_key]["action"]["q01"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return self.norm_stats[unnorm_key]["action"]

output_hf_model_openx/preprocessor_config.json

@@ -0,0 +1,114 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "image_processor_type": "PrismaticImageProcessor",
+  "image_resize_strategy": "resize-naive",
+  "input_sizes": [
+    [
+      3,
+      224,
+      224
+    ],
+    [
+      3,
+      224,
+      224
+    ]
+  ],
+  "interpolations": [
+    "bicubic",
+    "bicubic"
+  ],
+  "means": [
+    [
+      0.485,
+      0.456,
+      0.406
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "processor_class": "PrismaticProcessor",
+  "stds": [
+    [
+      0.229,
+      0.224,
+      0.225
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "tvf_crop_params": [
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    },
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "tvf_do_letterbox": false,
+  "tvf_letterbox_fill": null,
+  "tvf_normalize_params": [
+    {
+      "inplace": false,
+      "mean": [
+        0.484375,
+        0.455078125,
+        0.40625
+      ],
+      "std": [
+        0.228515625,
+        0.2236328125,
+        0.224609375
+      ]
+    },
+    {
+      "inplace": false,
+      "mean": [
+        0.5,
+        0.5,
+        0.5
+      ],
+      "std": [
+        0.5,
+        0.5,
+        0.5
+      ]
+    }
+  ],
+  "tvf_resize_params": [
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    },
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "use_fused_vision_backbone": true
+}

output_hf_model_openx/processing_prismatic.py

@@ -0,0 +1,257 @@
+"""
+processing_prismatic.py
+
+HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
+specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
+import timm.data
+import torch
+import torchvision.transforms.functional as TVF
+from PIL import Image
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from transformers import PreTrainedTokenizerBase
+from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+# === Image Processing ===
+def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
+    """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+    (w, h), max_wh = image.size, max(image.size)
+    horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+    padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+
+    return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
+
+
+class PrismaticImageProcessor(ImageProcessingMixin):
+    model_input_names: ClassVar[List[str]] = ["pixel_values"]
+
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool = False,
+        image_resize_strategy: str = "letterbox",
+        input_sizes: Optional[List[Tuple[int, int, int]]] = None,
+        interpolations: Optional[List[str]] = None,
+        means: Optional[List[Tuple[float, float, float]]] = None,
+        stds: Optional[List[Tuple[float, float, float]]] = None,
+        **kwargs: str,
+    ) -> None:
+        """
+        Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
+        created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
+
+        @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
+        @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
+        @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
+        @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
+        @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
+        @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
+        """
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.image_resize_strategy = image_resize_strategy
+
+        # Handle `None` default values
+        input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
+        means = [(0.5, 0.5, 0.5)] if means is None else means
+        stds = [(0.5, 0.5, 0.5)] if stds is None else stds
+
+        # TIMM `data_cfg` Parameters
+        self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
+
+        # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
+        self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
+        self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+        for idx in range(len(input_sizes)):
+            transform = timm.data.create_transform(
+                input_size=self.input_sizes[idx],
+                interpolation=self.interpolations[idx],
+                mean=self.means[idx],
+                std=self.stds[idx],
+                crop_pct=1.0,  # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
+                crop_mode="center",  # Default crop mode -- no-op when `crop_pct == 1.0`
+                is_training=False,  # No image augmentations when loading the transform!
+            )
+
+            # [Validation] Ensure appropriate transform structure, expected sizes
+            if not (
+                isinstance(transform, Compose)
+                and (len(transform.transforms) == 4)
+                and isinstance(transform.transforms[0], Resize)
+                and isinstance(transform.transforms[1], CenterCrop)
+                and isinstance(transform.transforms[2], ToTensor)
+                and isinstance(transform.transforms[3], Normalize)
+                and (transform.transforms[0].size == self.input_sizes[idx][-1])
+                and (transform.transforms[1].size == self.input_sizes[idx][-2:])
+            ):
+                raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
+
+            # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
+            #   => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
+            resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
+            self.tvf_resize_params.append(
+                {
+                    "size": resize_t.size,
+                    "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
+                    "max_size": None,
+                    "antialias": True,
+                }
+            )
+            self.tvf_crop_params.append({"output_size": crop_t.size})
+            self.tvf_normalize_params.append(
+                {
+                    "mean": norm_t.mean.float().numpy().tolist(),
+                    "std": norm_t.std.float().numpy().tolist(),
+                    "inplace": False,
+                }
+            )
+            self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+            # Handle Prismatic `image_resize_strategy`
+            if self.image_resize_strategy == "resize-naive":
+                self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
+            elif self.image_resize_strategy == "letterbox":
+                self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
+            elif self.image_resize_strategy == "resize-crop":
+                pass
+            else:
+                raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
+
+        # Dispatch **kwargs to super()
+        super().__init__(**kwargs)
+
+    def apply_transform(self, img: Image.Image) -> torch.Tensor:
+        """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
+        if self.tvf_do_letterbox:
+            img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
+
+        # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
+        imgs_t = []
+        for idx in range(len(self.input_sizes)):
+            img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
+            img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
+            img_idx_t = TVF.to_tensor(img_idx)
+            img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
+            imgs_t.append(img_idx_t)
+
+        # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
+        img_t = torch.vstack(imgs_t)
+
+        return img_t
+
+    def preprocess(
+        self,
+        images: Union[Image.Image, List[Image.Image]],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **_: str,
+    ) -> BatchFeature:
+        """
+        Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
+        explicitly only handle PIL.Image.Image instances for simplicity.
+
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
+
+        @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
+        pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
+
+        # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
+        return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
+
+    def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
+        return self.preprocess(images, **kwargs)
+
+
+# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
+#   =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
+class PrismaticProcessor(ProcessorMixin):
+    attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
+    image_processor_class: str = "AutoImageProcessor"
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[ImageProcessingMixin] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ) -> None:
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: Union[Image.Image, List[Image.Image]],
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
+        forwards images to PrismaticImageProcessor.
+
+        @param text: The (batch) of text to encode; must be a string or list of strings.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
+        @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
+        @param max_length: Maximum length (in tokens) to truncate
+        @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
+
+        @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
+        """
+        pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+        text_inputs = self.tokenizer(
+            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+        )
+
+        # [Validate] Need same number of images and text inputs!
+        if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
+            raise ValueError("Batch is malformed; expected same number of images and text inputs!")
+
+        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+
+    # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
+    def batch_decode(
+        self,
+        sequences: Union[List[int], List[List[int]], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> List[str]:
+        return self.tokenizer.batch_decode(
+            sequences=sequences,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> str:
+        return self.tokenizer.decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self) -> List[str]:
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

output_hf_model_openx/processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "processor_class": "PrismaticProcessor"
+}

output_hf_model_openx/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

output_hf_model_openx/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

output_hf_model_openx/tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

prismatic/__init__.py

@@ -0,0 +1 @@
+from .models import available_model_names, available_models, get_model_description, load

prismatic/conf/__init__.py

@@ -0,0 +1,3 @@
+from .datasets import DatasetConfig, DatasetRegistry
+from .models import ModelConfig, ModelRegistry
+from .vla import VLAConfig, VLARegistry

prismatic/conf/datasets.py

@@ -0,0 +1,133 @@
+"""
+datasets.py
+
+Draccus Dataclass Definition for a DatasetConfig object, with various registered subclasses for each dataset variant
+and processing scheme. A given dataset variant (e.g., `llava-lightning`) configures the following attributes:
+    - Dataset Variant (Identifier) --> e.g., "llava-v15"
+    - Align Stage Dataset Components (annotations, images)
+    - Finetune Stage Dataset Components (annotations, images)
+    - Dataset Root Directory (Path)
+"""
+
+from dataclasses import dataclass
+from enum import Enum, unique
+from pathlib import Path
+from typing import Tuple
+
+from draccus import ChoiceRegistry
+
+
+@dataclass
+class DatasetConfig(ChoiceRegistry):
+    # fmt: off
+    dataset_id: str                                 # Unique ID that fully specifies a dataset variant
+
+    # Dataset Components for each Stage in < align | finetune >
+    align_stage_components: Tuple[Path, Path]       # Path to annotation file and images directory for `align` stage
+    finetune_stage_components: Tuple[Path, Path]    # Path to annotation file and images directory for `finetune` stage
+
+    dataset_root_dir: Path                          # Path to dataset root directory; others paths are relative to root
+    # fmt: on
+
+
+# [Reproduction] LLaVa-v15 (exact dataset used in all public LLaVa-v15 models)
+@dataclass
+class LLaVa_V15_Config(DatasetConfig):
+    dataset_id: str = "llava-v15"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_mix665k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# [Multimodal-Only] LLava-v15 WITHOUT the Language-Only ShareGPT Data (No Co-Training)
+@dataclass
+class LLaVa_Multimodal_Only_Config(DatasetConfig):
+    dataset_id: str = "llava-multimodal"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_stripped625k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LVIS-Instruct-4V
+@dataclass
+class LLaVa_LVIS4V_Config(DatasetConfig):
+    dataset_id: str = "llava-lvis4v"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lvis4v_mix888k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LRV-Instruct
+@dataclass
+class LLaVa_LRV_Config(DatasetConfig):
+    dataset_id: str = "llava-lrv"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lrv_mix1008k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LVIS-Instruct-4V + LRV-Instruct
+@dataclass
+class LLaVa_LVIS4V_LRV_Config(DatasetConfig):
+    dataset_id: str = "llava-lvis4v-lrv"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lvis4v_lrv_mix1231k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# === Define a Dataset Registry Enum for Reference & Validation =>> all *new* datasets must be added here! ===
+@unique
+class DatasetRegistry(Enum):
+    # === LLaVa v1.5 ===
+    LLAVA_V15 = LLaVa_V15_Config
+
+    LLAVA_MULTIMODAL_ONLY = LLaVa_Multimodal_Only_Config
+
+    LLAVA_LVIS4V = LLaVa_LVIS4V_Config
+    LLAVA_LRV = LLaVa_LRV_Config
+
+    LLAVA_LVIS4V_LRV = LLaVa_LVIS4V_LRV_Config
+
+    @property
+    def dataset_id(self) -> str:
+        return self.value.dataset_id
+
+
+# Register Datasets in Choice Registry
+for dataset_variant in DatasetRegistry:
+    DatasetConfig.register_subclass(dataset_variant.dataset_id, dataset_variant.value)