Improve model card (#1)

- Improve model card (43c3f17245d2e65be57a2a2315812373187bfc54)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,202 +1,91 @@
 ---
-library_name: transformers
 language:
 - en
+library_name: transformers
+license: cc-by-4.0
+pipeline_tag: question-answering
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Llama-3.1-8B-Instruct LoRA for Knowledge Incorporation
 
+This model is a Low-Rank Adaptation (LoRA) of Llama-3.1-8B-Instruct, designed to enhance its question-answering capabilities by incorporating new knowledge, as described 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
+- **Developed by:** Sergey Pletenev et al.
+- **Model type:** `LlamaForCausalLM` with LoRA
+- **Language(s) (NLP):** English
+- **License:** CC-BY-4.0
+- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
-<!-- Provide a longer summary of what this model is. -->
+### Model Sources
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Repository:** [https://github.com/memyprokotow/knowledge_lora](https://github.com/memyprokotow/knowledge_lora)
+- **Paper:** [How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?](https://arxiv.org/abs/2502.14502)
+- **Datasets:**
+    - [Dbpedia dump](https://databus.dbpedia.org/dbpedia/mappings/mappingbased-objects)
+    - [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)
 
-- **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]
 
 ## 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. -->
+This model can be used for question-answering tasks, particularly those involving the new knowledge incorporated during fine-tuning. It is designed to be used with the base model `meta-llama/Meta-Llama-3.1-8B-Instruct`.
 
-[More Information Needed]
+### Downstream Use
 
-### 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]
+This model can be further fine-tuned or used as a starting point for research on knowledge incorporation into LLMs.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model should not be used for generating harmful, biased, or misleading content.  Its performance on general question-answering benchmarks might be impacted after fine-tuning with specific knowledge.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model inherits the biases present in the base Llama-3.1-8B-Instruct model.  Furthermore, the focused fine-tuning may introduce biases related to the new knowledge incorporated. The paper highlights potential performance decline on external question-answering benchmarks and a tendency to over-represent answers related to prominent entities in the 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. Careful attention should be paid to the composition and balance of the training data to mitigate biases and preserve general question-answering capabilities.
 
-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]
+See the Github repository for detailed instructions on training and using the LoRA adapter with the base Llama model.
 
 ## 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 model is fine-tuned on a dataset generated using the head-to-tail pipeline with DBpedia as the knowledge source. The data includes known facts, potentially known facts, and unknown facts categorized based on the base model's pre-training knowledge. See the "Data" section of the Github README for 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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+The model is trained using the LoRA technique.  Refer to the `lora_train_llama.py` script in the Github repository for training parameters and instructions.
 
 
-#### 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]
 
 ## 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]
+The paper evaluates the model's performance using a reliability score and investigates different knowledge integration scenarios. See the paper for detailed results and analysis.
 
-#### 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 environmental impact information is not available in the original README. Users can estimate the carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+## 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}, 
+}
+```