Improve Model Card

This PR improves the model card by:
- Adding a more detailed model description.
- Including information about the intended uses, limitations, and training details.
- Adding a clear citation.
- Adding the `question-answering` pipeline tag.
- Specifying the license.

README.md

@@ -1,200 +1,68 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+pipeline_tag: question-answering
+tags:
+- lora
+- knowledge-editing
+- question-answering
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Knowledge-Packed LoRA Adapters
 
+This model card describes LoRA adapters fine-tuned to incorporate new knowledge into Large Language Models (LLMs), while preserving previously learned information. The approach and potential pitfalls of LoRA-based LLM updates are discussed in the paper: [How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?](https://arxiv.org/abs/2502.14502).
 
 ## 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Sergey Pletenev, Maria Marina, Daniil Moskovskiy, Vasily Konovalov, Pavel Braslavski, Alexander Panchenko, and Mikhail Salnikov
+- **Model type:** LoRA adapter for causal language modeling
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
 ## 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 -->
-
-[More Information Needed]
+The model can be used to answer questions based on newly injected knowledge, for example, using facts from a specific domain. However, be mindful of the potential biases and knowledge spillover effects described in the paper.
 
 ### 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's performance may degrade when applied to tasks significantly different from the training data or when the training data is imbalanced. The model may exhibit biases learned from the training data and should not be used in high-stakes applications without careful evaluation and mitigation strategies.
 
 ## Bias, Risks, and Limitations
+The model may regress to overrepresented answers when the training data is biased towards certain entities. Fine-tuning can negatively impact the model's performance on external question-answering benchmarks. The model may also become more confident and refuse to provide an answer in only a few cases.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+See the Github repository for instructions on generating the dataset and training LoRA adapters: [https://github.com/memyprokotow/lora_vs_persisted/tree/master](https://github.com/memyprokotow/lora_vs_persisted/tree/master)
 
-[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]
+The training data consists of a mixture of known and new facts, created using the head-to-tail pipeline with Dbpedia. The authors experimented with varying amounts of new knowledge. More details about the training data generation process can be found in the paper and the Github repo.  Datasets used for the paper can be downloaded from:
+- [Dataset with precollected triples and questions](https://drive.google.com/file/d/1pCtfRlvBW769384AgmfNBpIU8OmftfKd/view?usp=sharing)
+- [Questions with labelled knowledge categories](https://drive.google.com/file/d/1-NDeTa8TMRNY9UIsIqtI-Iw4vq-rda35/view?usp=sharing).
 
 ### 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]
+The model is fine-tuned using LoRA. 
 
 ## 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]
-
-arxiv.org/abs/2502.14502
-<!-- 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]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The model's performance was evaluated on external question-answering benchmarks and by analyzing knowledge spillover effects.  See the paper and Github repo for more details.
+
+## Citation
+
+```
+@misc{pletenev2025knowledgepackloraadapter,
+      title={How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?}, 
+      author={Sergey Pletenev and Maria Marina and Daniil Moskovskiy and Vasily Konovalov and Pavel Braslavski and Alexander Panchenko and Mikhail Salnikov},
+      year={2025},
+      eprint={2502.14502},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.14502}, 
+}
+```