Improve model card with metadata and details

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]