Update README.md

README.md

@@ -4,9 +4,183 @@ language:
 - en
 base_model:
 - ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1
-new_version: ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1
 pipeline_tag: text-generation
 tags:
 - biology
 - medical
----
+---
+
+# Model Card for Bio-Medical-Llama-3-8B-V1
+
+This model is a fine-tuned version of **Bio-Medical-Llama-3-8B** for generating text related to biomedical knowledge. It is designed to assist in answering health and medical queries, serving as a robust tool for both healthcare professionals and general users.
+
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** ContactDoctor
+- **Funded by:** ContactDoctor Research Lab
+- **Model type:** Text Generation
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Bio-Medical-MultiModal-Llama-3-8B
+
+This model was created to address the need for accurate, conversational assistance in healthcare, biology, and medical science.
+
+---
+
+## Uses
+
+### Direct Use
+
+Users can employ the model to generate responses to biomedical questions, explanations of medical concepts, and general healthcare advice.
+
+### Downstream Use
+
+This model can be further fine-tuned for specific tasks, such as diagnosis support, clinical decision-making, and patient education.
+
+### Out-of-Scope Use
+
+The model should not be used as a substitute for professional medical advice, emergency assistance, or detailed medical diagnoses.
+
+---
+
+## Bias, Risks, and Limitations
+
+While the model is trained on extensive biomedical data, it might not cover every condition or the latest advancements. Users are advised to treat responses as informational rather than authoritative.
+
+### Recommendations
+
+- Use this model for general guidance, not as a substitute for professional advice.
+- Regularly review updates and improvements for the latest accuracy enhancements.
+
+---
+
+## How to Get Started with the Model
+
+You can use the model through the Hugging Face API or locally as shown in the example below.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ContactDoctor/Bio-Medical-Llama-3-8B-V1")
+model = AutoModelForCausalLM.from_pretrained("ContactDoctor/Bio-Medical-Llama-3-8B-V1")
+
+# Initialize the pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Generate a response
+response = generator("What is hypertension?", max_length=100)
+print(response[0]["generated_text"])
+---
+license: mit
+language:
+- en
+base_model: ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1
+pipeline_tag: text-generation
+tags:
+- biology
+- medical
+- fine-tuning
+---
+
+# Model Card for Fine-Tuned Bio-Medical-Llama-3-8B
+
+This model is a fine-tuned version of **Bio-Medical-Llama-3-8B-V1**, designed to enhance its performance for specialized biomedical and healthcare-related tasks. It provides responses to medical questions, explanations of health conditions, and insights into biology topics.
+
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** ContactDoctor Research Lab
+- **Fine-Tuned by:** Gokul Prasath M
+- **Model type:** Text Generation (Causal Language Modeling)
+- **Language(s):** English
+- **License:** MIT
+- **Fine-Tuned from Model:** Bio-Medical-Llama-3-8B-V1
+
+This fine-tuned model aims to improve accuracy and relevancy in generating biomedical-related responses, helping healthcare professionals and researchers with faster, more informed guidance.
+
+---
+
+## Uses
+
+### Direct Use
+
+- Biomedical question answering
+- Patient education and healthcare guidance
+- Biology and medical research support
+
+### Downstream Use
+
+- Can be further fine-tuned for specific domains within healthcare, such as oncology or pharmacology.
+- Integrates into larger medical chatbots or virtual assistants for clinical settings.
+
+### Out-of-Scope Use
+
+The model is not a substitute for professional medical advice, diagnosis, or treatment. It should not be used for emergency or diagnostic purposes.
+
+---
+
+## Fine-Tuning Details
+
+### Fine-Tuning Dataset
+
+The model was fine-tuned on a domain-specific dataset consisting of medical articles, clinical notes, and health information databases.
+
+### Fine-Tuning Procedure
+
+- **Precision:** Mixed-precision training using bf16 for optimal performance and memory efficiency.
+- **Quantization:** 4-bit LoRA for lightweight deployment.
+- **Hyperparameters**:
+  - **Learning Rate**: 2e-5
+  - **Batch Size**: 4
+  - **Epochs**: 3
+
+### Training Metrics
+
+During fine-tuning, the model achieved the following results:
+- **Training Loss:** 0.5396 at 1000 steps
+
+---
+
+## Evaluation
+
+### Evaluation Data
+
+The model was evaluated on a sample of medical and biological queries to assess its accuracy, relevance, and generalizability across health-related topics.
+
+### Metrics
+
+- **Accuracy:** Evaluated by response relevance to medical queries.
+- **Loss:** Final training loss of 0.5396
+
+---
+
+## Example Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Load the fine-tuned model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("path/to/your-finetuned-model/tokenizer")
+model = AutoModelForCausalLM.from_pretrained("path/to/your-finetuned-model")
+
+# Initialize the pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Generate a response
+response = generator("What are the symptoms of hypertension?", max_length=100)
+print(response[0]["generated_text"])
+```
+
+## Limitations and Recommendations
+The model may not cover the latest medical research or all conditions. It is recommended for general guidance rather than direct clinical application.
+
+## Bias, Risks, and Limitations
+Potential biases may exist due to dataset limitations. Responses should be verified by professionals for critical decisions.