Update README.md

README.md

@@ -16,6 +16,48 @@ tags:
 - OASIS
 ---
 
+# OASISCoder-Llama-3.2-3B
+
+### Model Description
+**OASISCoder-Llama-3.2-3B** is a fine-tuned version of the **LLaMA 3.2 3B** model, trained specifically on a **medical coding dataset** focusing on **CMS (Centers for Medicare & Medicaid Services)** and **OASIS (Outcome and Assessment Information Set)** standards. The model is designed to assist healthcare professionals and organizations in generating accurate medical codes and streamlining documentation tasks. It supports **medical question-answering**, **medical coding**, and **clinical decision support**, with an emphasis on regulatory compliance and documentation quality in the US healthcare system.
+
+### Intended Use Cases
+- **Medical Coding (CMS, OASIS)**: Supports automated or semi-automated coding tasks in clinical documentation, reducing administrative burden for healthcare providers.
+- **Clinical Decision Support**: Provides relevant, context-aware answers based on healthcare standards and medical queries.
+- **Healthcare QA Systems**: Useful for building medical chatbots and virtual assistants that handle queries related to CMS regulations, OASIS standards, and healthcare procedures.
+- **Medical Compliance**: Ensures accurate documentation for home healthcare assessments and improves regulatory compliance in clinical settings.
+
+### Training Data
+The model was fine-tuned on a **comprehensive medical coding dataset** integrating **CMS** and **OASIS** data, including real-world clinical documentation and coding tasks. The dataset includes examples of medical diagnoses, procedures, patient assessments, and coding annotations following CMS and OASIS regulations.
+
+### Architecture
+The model is based on **LLaMA 3.2 3B**, a powerful large language model architecture optimized for language understanding and generation tasks. Fine-tuning on the medical domain allows it to provide highly specialized and accurate outputs for healthcare tasks.
+
+### Performance
+- **Accuracy**: The model demonstrates a high accuracy rate in generating CMS and OASIS codes from clinical text and answering medical queries.
+- **Efficiency**: Fine-tuning on specific healthcare tasks has reduced the model's processing time for medical coding and decision support tasks.
+
+### Limitations
+- **Not a Diagnostic Tool**: This model is not intended for making medical diagnoses and should not be used as a replacement for professional medical judgment.
+- **Bias and Data Coverage**: The model's performance is best on US healthcare data (CMS, OASIS), and may not generalize well to other healthcare systems or international coding standards.
+
+### License
+The model is released under the **Apache License 2.0**, making it available for non-commercial research and development purposes.
+
+### How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("exafluence/OASISCoder-Llama-3.2-3B")
+model = AutoModelForCausalLM.from_pretrained("exafluence/OASISCoder-Llama-3.2-3B")
+
+input_text = "What is the CMS code for a patient with diabetes?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+```
+
+
 # Uploaded  model
 
 - **Developed by:** exafluence