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README.md

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 library_name: transformers
+pipeline_tag: question-answering
+license: mit
+base_model: meta-llama/Llama-3.1-8B-Instruct
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
 
+This model card describes a LoRA model presented in [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. -->
+The performance of Large Language Models (LLMs) on many tasks is greatly limited by the knowledge learned during pre-training and stored in the model's parameters. Low-rank adaptation (LoRA) is a popular and efficient training technique for updating or domain-specific adaptation of LLMs. In this study, we investigate how new facts can be incorporated into the LLM using LoRA without compromising the previously learned knowledge. We fine-tuned Llama-3.1-8B-instruct using LoRA with varying amounts of new knowledge. Our experiments have shown that the best results are obtained when the training data contains a mixture of known and new facts. However, this approach is still potentially harmful because the model's performance on external question-answering benchmarks declines after such fine-tuning. When the training data is biased towards certain entities, the model tends to regress to few overrepresented answers. In addition, we found that the model becomes more confident and refuses to provide an answer in only few cases. These findings highlight the potential pitfalls of LoRA-based LLM updates and underscore the importance of training data composition and tuning parameters to balance new knowledge integration and general model capabilities.
 
-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]
+- **Developed by:** Sergey Pletenev, Maria Marina, Daniil Moskovskiy, Vasily Konovalov, Pavel Braslavski, Alexander Panchenko, Mikhail Salnikov
+- **Model type:** LLM
+- **Language(s) (NLP):** English
+- **License:** mit
+- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [https://github.com/memyprokotow/knowledge-lora-adapt](https://github.com/memyprokotow/knowledge-lora-adapt)
+- **Paper:** [How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?](https://arxiv.org/abs/2502.14502)
 
-- **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 for question answering.
 
-<!-- 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 model can be further fine-tuned for domain-specific question answering.
 
 ### 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 questions outside the knowledge it has been fine-tuned on, or if the training data was biased.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model may exhibit biases present in the training data. The model's performance may degrade on external question-answering benchmarks after fine-tuning, especially if the training data is biased towards certain entities.
 
 ### 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 in the model's responses and the limitations of its knowledge.
 
 ## 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]
+The training data consists of questions and answers generated using the head-to-tail pipeline with a Dbpedia script.  See the paper and Github repository for more details.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
+The model was fine-tuned using LoRA.
 
 #### 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 -->
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-#### 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
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- 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
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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 [More Information Needed]
 
-#### Software
 
-[More Information Needed]
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-## 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. -->
+## Citation
 
-**BibTeX:**
-
-[More Information Needed]
+```
+@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}, 
+}
+```
 
 **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. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
-
-[More Information Needed]
+Pletenev, S., Marina, M., Moskovskiy, D., Konovalov, V., Braslavski, P., Panchenko, A., & Salnikov, M. (2025). How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?.