Add model card for TokenSwift

This PR adds a model card for the TokenSwift model. It includes the paper link, the GitHub repository, specifies the library name, and sets the pipeline tag for text generation. It also populates sections of the model card with descriptions and usage information derived from the paper and the repository README.

README.md

@@ -1,199 +1,89 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+license: apache-2.0 # Please verify license
+tags: [long-sequence-generation, lossless-acceleration]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for TokenSwift
 
+**TokenSwift** is a novel framework that achieves **lossless acceleration** for ultra-long sequence generation (up to 100K tokens), reducing computation time from hours to minutes.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
+TokenSwift is a framework designed to accelerate the generation of long sequences in large language models without sacrificing the quality of the output. It works as a plug-and-play solution with most Hugging Face models, providing a 3x speedup.
 
-### Model Sources [optional]
+- **Developed by:** BigAI-NLCO
+- **Model type:** Model Adapter Framework
+- **Language(s) (NLP):** Multiple, depending on the underlying LLM
+- **License:** Apache-2.0 # Please verify license
+- **Finetuned from model [optional]:**  Various Hugging Face LLMs (see Inference section)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/bigai-nlco/TokenSwift](https://github.com/bigai-nlco/TokenSwift)
+- **Paper:** [https://arxiv.org/abs/2502.18890](https://arxiv.org/abs/2502.18890)
 
 ## 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]
+TokenSwift is used as a framework to accelerate the inference of existing Hugging Face LLMs, particularly for long sequence generation.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The accelerated LLMs can be used for any downstream task supported by the underlying base model.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+TokenSwift is not designed for tasks that do not involve text generation or where short sequence lengths are sufficient.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+TokenSwift inherits the biases and limitations of the underlying language model it is used with.
 
 ### 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.
+Users should be aware of the potential biases and limitations of the base language model used with TokenSwift.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+See the [Inference](#inference) section of the GitHub README for usage instructions. Pre-trained TokenSwift adapters are available on the Hugging Face Hub.
 
 ## 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. -->
+The training data is derived from the PG-19 dataset. Data longer than 8K tokens are filtered out. Processed training datasets are available at:
 
-[More Information Needed]
+-   llama2-pg19: [https://huggingface.co/datasets/TokenSwift/llama2\_pg19\_train\_data](https://huggingface.co/datasets/TokenSwift/llama2_pg19_train_data)
+-   llama3.1-pg19: [https://huggingface.co/datasets/TokenSwift/llama3.1\_pg19\_train\_data](https://huggingface.co/datasets/TokenSwift/llama3.1_pg19_train_data)
+-   qwen2.5-pg19: [https://huggingface.co/datasets/TokenSwift/qwen2.5\_pg19\_train\_data](https://huggingface.co/datasets/TokenSwift/qwen2.5_pg19_train_data)
 
 ### 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]
+See the [Training Guide](#training-guide-option) section of the GitHub README for details.
 
 ## 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]
+See the GitHub README for benchmark results.
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{wu2025hoursminuteslosslessacceleration,
+      title={From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation up to 100K Tokens},
+      author={Tong Wu and Junzhe Shen and Zixia Jia and Yuxuan Wang and Zilong Zheng},
+      year={2025},
+      eprint={2502.18890},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.18890},
+}
+```
 
-## Model Card Contact
+## Acknowledgment
 
-[More Information Needed]
+This codebase is influenced by remarkable projects from the LLM community, including [Medusa](https://github.com/FasterDecoding/Medusa/tree/main) and [TriForce](https://github.com/Infini-AI-Lab/TriForce).