Update tokenizer README

Document MolmoAct2-FAST Tokenizer usage and open-data FAST reimplementation context.

README.md

@@ -1,199 +1,91 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- robotics
+- tokenizer
+- action-tokenizer
 ---
 
-# Model Card for Model ID
+# MolmoAct2-FAST Tokenizer
 
-<!-- Provide a quick summary of what the model is/does. -->
+MolmoAct2-FAST Tokenizer is an action tokenizer for autoregressive vision-language-action models. It is a reimplementation of [physical-intelligence/fast](https://huggingface.co/physical-intelligence/fast) using fully open-sourced data.
 
+The tokenizer turns robot action chunks into compact discrete action tokens and can decode those tokens back into continuous action chunks. This makes it useful for training policies that predict robot actions as token sequences.
 
+## Installation
 
-## Model Details
+Install the Hugging Face `transformers` package plus `scipy`, which is used for the DCT-based action transform.
 
-### Model Description
+```bash
+pip install transformers scipy numpy
+```
 
-<!-- Provide a longer summary of what this model is. -->
+## Load the Tokenizer
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This repository provides a custom `AutoProcessor`, so `trust_remote_code=True` is required.
 
-- **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]
+```python
+from transformers import AutoProcessor
 
-### Model Sources [optional]
+tokenizer = AutoProcessor.from_pretrained(
+    "allenai/MolmoAct2-FAST-Tokenizer",
+    trust_remote_code=True,
+)
+```
 
-<!-- Provide the basic links for the model. -->
+## Encode and Decode Actions
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Use the tokenizer on 1-second robot action chunks that have been normalized consistently, typically to approximately `[-1, 1]`. Inputs may be a single action chunk with shape `[time_horizon, action_dim]` or a batch with shape `[batch, time_horizon, action_dim]`.
 
-## Uses
+```python
+import numpy as np
+from transformers import AutoProcessor
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+tokenizer = AutoProcessor.from_pretrained(
+    "allenai/MolmoAct2-FAST-Tokenizer",
+    trust_remote_code=True,
+)
 
-### Direct Use
+# Example batch: 256 chunks, 50 timesteps per chunk, 14 action dimensions.
+action_data = np.random.uniform(-1, 1, size=(256, 50, 14)).astype(np.float32)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+tokens = tokenizer(action_data)
+decoded_actions = tokenizer.decode(tokens)
 
-[More Information Needed]
+print(len(tokens))
+print(decoded_actions.shape)
+```
 
-### Downstream Use [optional]
+During decoding, the processor needs to know the original time horizon and action dimension. If `decode()` is called after tokenizing a chunk, those dimensions are cached automatically. If you decode tokens in a separate process or before an encode call, pass the dimensions explicitly.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```python
+decoded_actions = tokenizer.decode(tokens, time_horizon=50, action_dim=14)
+```
 
-[More Information Needed]
+## Train a Custom Action Tokenizer
 
-### Out-of-Scope Use
+You can train a new action tokenizer from your own action chunks with `.fit()`. Each chunk should be an array shaped `[time_horizon, action_dim]`; chunks may be passed as a list or as a batch array.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```python
+import numpy as np
+from transformers import AutoProcessor
 
-[More Information Needed]
+base_tokenizer = AutoProcessor.from_pretrained(
+    "allenai/MolmoAct2-FAST-Tokenizer",
+    trust_remote_code=True,
+)
 
-## Bias, Risks, and Limitations
+training_chunks = np.random.uniform(-1, 1, size=(4000, 50, 14)).astype(np.float32)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+custom_tokenizer = base_tokenizer.fit(
+    training_chunks,
+    vocab_size=2048,
+    time_horizon=50,
+    action_dim=14,
+)
 
-[More Information Needed]
+custom_tokenizer.save_pretrained("./my-fast-tokenizer")
+# custom_tokenizer.push_to_hub("your-org/my-fast-tokenizer")
+```
 
-### 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]
+For best results, use the same action normalization when training, encoding, decoding, and evaluating decoded actions.