Update README.md

README.md

@@ -3,11 +3,65 @@ license: apache-2.0
 tags:
 - trl
 - sft
+library_name: transformers
 ---
+
+# CatMemo: Fine-Tuning Large Language Models for Financial Applications
+
+## Model Overview
+This model, **CatMemo**, is fine-tuned using **Data Fusion** techniques for financial applications. It was developed as part of the FinLLM Challenge Task and focuses on enhancing the performance of large language models in finance-specific tasks such as question answering, document summarization, and sentiment analysis.
+
+### Key Features
+- Fine-tuned on financial datasets using **Supervised Fine-Tuning (SFT)** techniques.
+- Optimized for **Transfer Reinforcement Learning (TRL)** workflows.
+- Specialized for tasks requiring domain-specific context in financial applications.
+
+## Usage
+You can use this model with the [Hugging Face Transformers library](https://huggingface.co/docs/transformers/) to perform financial text analysis. Below is a quick example:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model_name = "zeeshanali01/cryptotunned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Tokenize input
+inputs = tokenizer("What are the key takeaways from the latest earnings report?", return_tensors="pt")
+
+# Generate output
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+This model was fine-tuned using **Data Fusion** methods on domain-specific financial datasets. The training pipeline includes:
+- Preprocessing financial documents and datasets to enhance model understanding.
+- Applying **Supervised Fine-Tuning (SFT)** to optimize the model for financial NLP tasks.
+- Testing and evaluation on FinLLM benchmark tasks.
+
+## Citation
+If you use this model, please cite our work:
+
+```
 @inproceedings{cao2024catmemo,
   title={CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications},
   author={Cao, Yupeng and Yao, Zhiyuan and Chen, Zhi and Deng, Zhiyang},
   booktitle={Joint Workshop of the 8th Financial Technology and Natural Language Processing (FinNLP) and the 1st Agent AI for Scenario Planning (AgentScen) in conjunction with IJCAI 2023},
   pages={174},
   year={2024}
-}
+}
+```
+
+## License
+This model is licensed under the Apache 2.0 License. See the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
+
+## Acknowledgments
+We thank the organizers of the FinLLM Challenge Task for providing the benchmark datasets and tasks used to develop this model.
+
+---
+### Model Card Metadata
+- **License:** Apache 2.0
+- **Tags:** TRL, SFT
+- **Library Used:** Transformers