Populate Model Card with Details and Metadata for Self-Calibration Model

This PR populates the automatically generated model card with details from the research paper and the Github repository. It adds the `pipeline_tag`, `library_name`, and `license` metadata, as well as filling in the various sections of the model card with relevant information. Links to the Github repo and paper are also added.

README.md

@@ -1,199 +1,126 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+  - self-calibration
+  - efficient-sampling
+  - llm-uncertainty
 ---
 
-# 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. It addresses the challenge of overconfidence in LLMs by introducing a self-calibration framework that generates calibrated confidence scores, improving computational efficiency without sacrificing accuracy. This is based on the research paper [Efficient Test-Time Scaling via Self-Calibration](https://arxiv.org/abs/2503.00031).
 
 ## 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 uses model confidence to dynamically adjust sampling during inference, leading to significant improvements in computational efficiency. The self-calibration framework ensures calibrated confidence scores, making the method robust and reliable. The model is designed to work with various sampling methods, including early exit, ascending confidence, self-consistency, and best-of-N.
 
-- **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 (LLM)
+- **Language(s) (NLP):** English (supports other languages depending on the base model used during training)
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** (Specify base model used, e.g., `meta-llama/Llama-3.1-8B-Instruct`)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/HINT-lab/Self-Calibration](https://github.com/HINT-lab/Self-Calibration)
+- **Paper [optional]:** [https://arxiv.org/abs/2503.00031](https://arxiv.org/abs/2503.00031)
+- **Models:**
+  - [DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration](https://huggingface.co/HINT-lab/DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration)
+  - [Qwen2.5-7B-Instruct-Self-Calibration](https://huggingface.co/HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration)
+  - [Llama-3.1-8B-Instruct-Self-Calibration](https://huggingface.co/HINT-lab/Llama-3.1-8B-Instruct-Self-Calibration)
+- **Datasets:**
+  - [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/datasets/HINT-lab/DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration)
+  - [Qwen2.5-7B-Instruct](https://huggingface.co/datasets/HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration)
+  - [Llama_3.1-8B-Instruct](https://huggingface.co/datasets/HINT-lab/Llama_3.1-8B-Instruct-Self-Calibration)
 
 ## 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]
+The model can be used directly for text generation tasks with various sampling methods. The user can specify the desired sampling method, confidence threshold (if applicable), number of samples, and temperature.
 
 ### 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]
+The model can be fine-tuned for specific downstream tasks or integrated into larger applications requiring efficient text generation.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model may not perform well on tasks requiring high creativity or those outside the domains represented in the training data. The accuracy of the confidence scores depends heavily on the quality and calibration of the underlying base LLM.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model inherits biases from its base LLM. The accuracy of the confidence scores and the effectiveness of the sampling methods may vary depending on the task and the base model. Over-reliance on the model's confidence scores without considering other factors could lead to incorrect inferences.
 
 ### 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 potential biases and limitations. It's recommended to evaluate the model's performance on specific tasks before deploying it to critical applications. Users should also critically evaluate the confidence scores provided by the model.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+See the "Quickstart" section in the Github README for instructions on how to install the necessary packages and use the model for inference.
 
 ## 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]
+The training data consists of datasets created by generating multiple responses to prompts from various benchmark datasets (more detail can be found in the Github README).
 
 ### 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]
-
+The training procedure involves a self-calibration process to improve the model's ability to generate calibrated confidence scores. Details are in the Github README.
 
 #### 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 -->
+(To be added from Github README - `model_training/configs/{version}.json`)
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+(To be added from Github README - training times on various hardware)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+(To be added from Github README - evaluation protocols and 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]
+(To be added from Github README)
 
 ### Results
 
-[More Information Needed]
+(To be added from Github README)
 
 #### Summary
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+(To be added from Github README)
 
 ## 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]
+(To be added based on hardware usage reported in the Github README)
 
 ## Technical Specifications [optional]
 
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
+(To be added based on model architecture and training details in the Github README)
 
 ## 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]
+```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]
+(To be added based on the citation information in the Github README)