Populate Model Card with Information

This pull request populates the automatically generated model card with information from the provided context. It adds the missing `pipeline_tag`, `library_name`, and `license` metadata, and provides a basic model description and usage example. Further details should be added as they become available. The license is assumed to be MIT based on the existing card.

README.md

@@ -1,90 +1,71 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+license: mit
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is a large language model designed for text generation tasks.  Further details about the model architecture and training data will be added soon.
 
 ## 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 is a transformer-based language model.  More detailed information about the model architecture and training process will be added to this model card shortly.
 
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Model type:** Large Language Model
 - **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **License:** MIT
 - **Finetuned from model [optional]:** [More Information Needed]
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Paper [optional]:** [https://huggingface.co/papers/2502.05209](https://huggingface.co/papers/2502.05209)
 - **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]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use [optional]
 
 [More Information Needed]
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Out-of-Scope Use
 
 [More Information Needed]
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Bias, Risks, and Limitations
 
 [More Information Needed]
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Recommendations
 
 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]
@@ -92,36 +73,26 @@ Use the code below to get started with the model.
 
 #### 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 -->
+- **Training regime:** [More Information Needed] 
 
 #### 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
@@ -134,14 +105,10 @@ Use the code below to get started with the model.
 
 ## 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]
@@ -170,8 +137,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## 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]
@@ -182,8 +147,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## 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]