peft safetensors llama en dataset:huzaifa525/Medical_Intelligence_Dataset_40k_Rows_of_Disease_Info_Treatments_and_Medical_QA base_model:unsloth/Llama-3.2-1B-Instruct base_model:adapter:unsloth/Llama-3.2-1B-Instruct license:apache-2.0 4-bit bitsandbytes region:us
---
base_model: unsloth/Llama-3.2-1B-Instruct
datasets:
- huzaifa525/Medical_Intelligence_Dataset_40k_Rows_of_Disease_Info_Treatments_and_Medical_QA
language:
- en
library_name: peft
license: apache-2.0
---

# Model Card for PhysioMindAI-Llama3-Medical

## Model Details

### Model Description

PhysioMindAI-Llama3-Medical is a fine-tuned version of the **Llama-3.2-1B-Instruct** model, specifically designed for medical applications. The model is trained to understand and generate medical content, assisting in tasks like symptom analysis, treatment suggestions, and patient query responses.

- **Developed by:** Satish Soni  
- **Organization:** Globalspace Technologies Ltd  
- **Funded by [optional]:** _More Information Needed_  
- **Shared by [optional]:** sonisatish119  
- **Model type:** Medical NLP, LLM  
- **Language(s) (NLP):** English  
- **License:** Apache 2.0  
- **Finetuned from model:** `unsloth/Llama-3.2-1B-Instruct`  

### Model Sources

- **Repository:** [PhysioMindAI-Llama3-Medical](https://huggingface.co/sonisatish119/PhysioMindAI-Llama3-Medical)  
- **Paper [optional]:** _More Information Needed_  
- **Demo [optional]:** _More Information Needed_  

## Uses

### Direct Use

PhysioMindAI-Llama3-Medical can be used for:
- ✅ Medical question answering  
- ✅ Clinical note summarization  
- ✅ Symptom checker and risk assessment  
- ✅ Generating patient-friendly explanations  

### Downstream Use

- 🏥 Can be integrated into healthcare chatbots and virtual assistants  
- 🛠️ Can be fine-tuned further for specific medical domains  

### Out-of-Scope Use

⚠️ Not intended for real-time clinical decision-making without human oversight  
⚠️ Should not be used for emergency medical advice  

## Bias, Risks, and Limitations

### Recommendations

⚠️ Users should be aware of potential biases in training data and limitations in accuracy.  
✅ Always verify critical medical information with professionals.  

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sonisatish119/PhysioMindAI-Llama3-Medical"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

input_text = "What are the symptoms of anxiety?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(output[0], skip_special_tokens=True))