README.md

---
license: apache-2.0
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
language:
- en
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text-generation
library_name: transformers
tags:
- chemistry
- medical
- Doctor
- AI_Doctor
- Illness
- MedicalAI
- MBBS
- AI_AGENT
---
# Model Card for Model ID

 🩺 Medical Diagnosis AI Model - Powered by Mistral-7B & LoRA 🚀
🔹 Model Overview:
Base Model: Mistral-7B (7.7 billion parameters)
Fine-Tuning Method: LoRA (Low-Rank Adaptation)
Quantization: bnb_4bit (reduces memory footprint while retaining performance)
🔹 Parameter Details:
Original Mistral-7B Parameters: 7.7 billion
LoRA Fine-Tuned Parameters: ~4.48% of total model parameters (~340 million)
Final Merged Model Size (bnb_4bit Quantized): ~4.5GB
🔹 Key Features:
✅ Accurate Diagnoses for symptoms like chest pain, dizziness, and breathlessness
✅ Step-by-Step Medical Reasoning using Chain-of-Thought (CoT) prompting
✅ Efficient Inference with reduced VRAM usage (ideal for GPUs with limited memory)

🔹 Use Case:
Designed to assist healthcare professionals by offering clear, evidence-backed insights for improved clinical decision-making.
📝 Note: While this model offers valuable insights, it's intended to support — not replace — professional medical judgment. 

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details
Original Mistral-7B Parameters: 7.7 billion
LoRA Fine-Tuned Parameters: 4.48% of total model parameters (~340 million)
Final Merged Model Size (bnb_4bit Quantized): ~4.5GB
🔹 Key Features:
✅ Accurate Diagnoses for symptoms like chest pain, dizziness, and breathlessness
✅ Step-by-Step Medical Reasoning using Chain-of-Thought (CoT) prompting
✅ Efficient Inference with reduced VRAM usage (ideal for GPUs with limited memory)

### Model Description
This model leverages the powerful Mistral-7B language model, known for its strong reasoning capabilities and deep language understanding. Through LoRA fine-tuning, the model now excels in medical-specific tasks like:
✅ Diagnosing conditions from symptoms such as chest pain, dizziness, and shortness of breath
✅ Providing detailed, step-by-step medical reasoning using Chain-of-Thought (CoT) prompting
✅ Generating confident, evidence-backed answers with improved precision

- **Developed by:** [Ritvik Gaur]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Medical LLM]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [Mistral-7B-Instruct-v3]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations
Please dont fully rely on this model for real life illness, this model is just for support of real verifies health applications that requires LLM.

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

!pip install -q -U bitsandbytes

!pip install -q -U peft

!pip install -q -U trl

!pip install -q -U tensorboardX

!pip install -q wandb


from transformers import AutoModelForCausalLM, AutoTokenizer

# ✅ Load the uploaded model
model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")
tokenizer = AutoTokenizer.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")

# ✅ Sample inference
prompt = "Patient reports chest pain and dizziness with nose bleeding, What’s the likely diagnosis is it cancer ?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Python code for usage:
from transformers import AutoModelForCausalLM, AutoTokenizer
# ✅ Load the uploaded model
model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")
tokenizer = AutoTokenizer.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct")
# ✅ Sample inference
prompt = "Patient reports chest pain and dizziness. What’s the likely diagnosis?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))


[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed]
- 
Parameter	Value	Description

Base Model	mistralai/Mistral-7B-Instruct	Chosen for its strong reasoning capabilities.

Fine-Tuning Framework	LoRA (Low-Rank Adaptation)	Efficiently fine-tuned only ~4.48% of total parameters.

Quantization	bnb_4bit	Enabled for reduced VRAM consumption.

Train Batch Size	12	Optimized to balance GPU utilization and convergence.

Eval Batch Size	12	Matches training batch size to ensure stable evaluation.

Gradient Accumulation Steps	3	Effective batch size = 36 for improved stability.

Learning Rate	3e-5	Lowered to ensure smoother convergence

Warmup Ratio	0.2	Gradual learning rate ramp-up for improved stability

Scheduler Type	Cosine	Ensures smooth and controlled learning rate decay

Number of Epochs	5	Balanced to ensure convergence without overfitting

Max Gradient Norm	0.5	Prevents exploding gradients

Weight Decay	0.08	Regularization for improved generalization 

bf16 Precision	True	Maximizes GPU utilization and precision

Gradient Checkpointing	Enabled	Reduces memory usage during training


🔎 LoRA Configuration
Parameter	Value	Description
Rank Dimension	128	Balanced for strong expressiveness without excessive memory overhead
LoRA Alpha	128	Ensures stable gradient updates
LoRA Dropout	0.1	Helps prevent overfitting


#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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