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README.md

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-base_model: NousResearch/Llama-2-7b-chat-hf
-library_name: peft
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-
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.12.0
+#  LLaMA-2-7B Chat - AI Medical Chatbot
+
+## Model Overview
+This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot) dataset, which consists of medical question-answering tasks. It leverages the latest in language model technology for generating accurate and respectful medical assistance responses, providing helpful advice on common medical questions.
+
+Fine-tuned using LoRA (Low-Rank Adaptation) and quantization techniques, this model is optimized for efficiency while maintaining high-quality, contextually relevant outputs. The training was performed using [PEFT](https://github.com/huggingface/peft) to make the model more lightweight and accessible for a broader range of applications.
+
+### Model Details
+- **Model Type**: LLaMA-2 7B (Chat)
+- **Base Model**: [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf)
+- **Fine-tuned on**: [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot)
+- **Parameter Count**: 7 Billion
+- **Quantization**: Applied for efficient inference
+- **Training Methodology**: LoRA + PEFT
+- **Task**: Medical Question Answering (QA)
+
+## Model Purpose
+This model was fine-tuned to assist users in answering medical questions, particularly common healthcare concerns such as symptoms, treatments, and remedies. It is **not intended** to replace professional medical consultation but can be used to offer informative, general responses that can help guide users toward appropriate care.
+
+## Intended Use
+- **Primary Use Case**: Medical question-answering chatbot
+- **Intended Users**: Developers or healthcare professionals seeking a chatbot interface for initial user engagement or educational purposes. 
+- **Environment**: Can be deployed in environments where lightweight models are necessary (due to quantization and PEFT optimization).
+- **Primary Applications**: Healthcare chatbots, medical assistants, online medical question-answering systems.
+
+### Example Inputs
+- **User**: "What are the common treatments for a sore throat?"
+- **Model**: "For a sore throat, common treatments include rest, hydration, throat lozenges, warm saltwater gargles, and over-the-counter pain relief medications. If the sore throat persists, you should consult a doctor as it may indicate a bacterial infection."
+
+- **User**: "Can you suggest a homeopathic remedy for hair loss?"
+- **Model**: "Homeopathic remedies like Arnica, Phosphorus, and Silicea are often recommended for hair loss. However, it's important to consult a homeopathic practitioner for a tailored treatment."
+
+## Training Dataset
+- **Dataset**: [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot)
+  - This dataset contains a wide variety of medical queries and corresponding answers. The dataset covers questions about symptoms, diagnoses, treatments, and remedies.
+
+## Training Process
+The model was trained using the following setup:
+- **Optimizer**: AdamW
+- **Batch Size**: 2
+- **Gradient Accumulation**: 4 steps
+- **Learning Rate**: 2e-4
+- **Max Steps**: 5000
+- **Epochs**: 500 (with early stopping)
+- **Quantization**: Applied for memory efficiency
+- **LoRA**: Used for parameter-efficient fine-tuning
+
+## Limitations
+- **Not a Substitute for Medical Advice**: This model is trained to assist with general medical questions but should **not** be used to make clinical decisions or substitute professional medical advice.
+- **Biases**: The model's responses may reflect the biases inherent in the dataset it was trained on.
+- **Data Limitation**: The model may not have been exposed to niche or highly specialized medical knowledge and could provide incomplete or incorrect information in such cases.
+
+## Ethical Considerations
+This model is designed to assist with medical-related queries and provide useful responses. However, users are strongly encouraged to consult licensed healthcare providers for serious medical conditions, diagnoses, or treatment plans. Misuse of the model for self-diagnosis or treatment is discouraged.
+
+### Warning
+The outputs of this model should not be relied upon for critical or life-threatening situations. It is essential to consult a healthcare professional before taking any medical action based on this model's suggestions.
+
+## How to Use
+
+You can load and use this model for medical chatbot applications with ease using the Hugging Face library:
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+
+model_id = "NousResearch/Llama-2-7b-chat-hf"
+config = PeftConfig.from_pretrained( 'MassMin/llama2_ai_medical_chatbot')
+model = AutoModelForCausalLM.from_pretrained(model_id)
+model = PeftModel.from_pretrained(model,  'MassMin/llama2_ai_medical_chatbot')
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=256
+)
+
+prompt='Input your question?.'
+result = pipe(f"<s>[INST] {prompt} [/INST]")
+print(result[0]['generated_text'])