Improve model card with metadata and details

This PR improves the model card by adding the necessary metadata (pipeline tag, library name, license) and populating some sections with information from the paper and GitHub README. It also adds relevant tags to improve searchability.

README.md

@@ -1,199 +1,125 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+  - self-calibration
+  - confidence-estimation
+  - test-time-scaling
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Efficient Test-Time Scaling via Self-Calibration
 
+This model implements an efficient test-time scaling method using model confidence for dynamic sampling adjustment. Higher confidence responses have a greater influence on the final answer, leading to improved computational efficiency. The model uses a self-calibration framework to generate more calibrated confidence scores.
 
 
 ## 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.
+This model utilizes a self-calibration framework to generate calibrated confidence scores, which are then used to improve the efficiency of test-time scaling methods. This allows for comparable performance with substantially fewer computational resources.
 
-- **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]
+- **Developed by:** HINT-lab
+- **Model type:** Large Language Model
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** [Specify base model here, e.g.,  `meta-llama/Llama-3.1-8B-Instruct`]
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/HINT-lab/Efficient-Test-Time-Scaling
+- **Paper:** [Efficient Test-Time Scaling via Self-Calibration](https://arxiv.org/abs/2503.00031)
 
-- **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]
+The model can be used directly for text generation tasks, leveraging its self-calibration capabilities for improved efficiency.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The calibrated confidence scores generated by the model can be incorporated into various test-time scaling methods (e.g., Self-Consistency, Best-of-N) to enhance their performance and reduce computational costs.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is not intended for tasks requiring high accuracy in scenarios where confidence calibration is not crucial.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance and calibration accuracy may vary depending on the specific dataset and task. Like other LLMs, it may exhibit biases present in its training data.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should be aware of the potential biases and limitations of the model and carefully evaluate its performance on their specific tasks. Further investigation into bias mitigation techniques is recommended.
 
-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.
+See the [GitHub README](https://github.com/HINT-lab/Efficient-Test-Time-Scaling) for detailed instructions.
 
-[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]
+[Link to Hugging Face Dataset, if available. Otherwise, provide a brief description]
 
 ### 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]
-
+See the training section in the [GitHub README](https://github.com/HINT-lab/Efficient-Test-Time-Scaling).
 
 #### 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 -->
+[Information from GitHub README regarding hyperparameters]
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[Information from GitHub README about training times, model sizes, 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]
+[Link to Hugging Face Dataset, if available. Otherwise, provide a brief description]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+[List factors from GitHub README, e.g., different datasets]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+[List metrics used from GitHub README, e.g., accuracy]
 
 ### Results
 
-[More Information Needed]
+[Summary of results from GitHub README]
 
 #### Summary
 
+[Concise summary of overall evaluation performance]
 
 
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{huang2025efficienttesttimescalingselfcalibration,
+      title={Efficient Test-Time Scaling via Self-Calibration}, 
+      author={Chengsong Huang and Langlin Huang and Jixuan Leng and Jiacheng Liu and Jiaxin Huang},
+      year={2025},
+      eprint={2503.00031},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2503.00031}, 
+}
+```
 
 **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]
+[APA citation for the paper - Needs to be constructed based on the paper's full details]