---
library_name: transformers
tags:
- medical
- biology
- chemistry
license: gemma
datasets:
- sid6i7/patient-doctor
language:
- en
pipeline_tag: text-generation
base_model:
- google/gemma-2-2b-it
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:

```python
pip install -U transformers
pip install -U torch
```
Then, copy the snippet from the section that is relevant for your usecase.

### Direct Use

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

```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Ellio98/doctor-gemma-2-2b-it")
model = AutoModelForCausalLM.from_pretrained(
    "Ellio98/doctor-gemma-2-2b-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

query = "I have a high fever and coughing since morning. What should I do ?"

prompt = [{"role": "user", "content": query}]

model_input = tokenizer(tokenizer.apply_chat_template(prompt, tokenize=False), return_tensors="pt")

outputs = model.generate(
                          input_ids=model_input["input_ids"].to("cuda"),
                          attention_mask=model_input["attention_mask"].to("cuda"),
                          max_new_tokens=32
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# user
# I have a high fever and coughing since morning. What should I do ?
# model
# Hi,From history it seems that you might be having viral infection giving this problem of fever with cough.
# Take paracetamol or ibuprofen for pain due to fever. Take plenty of water.
# If require take one antispasmodic medicine like Meftal spas as needed.
# Ok and take care.
```

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model might not be good at generating the desired output and might suggest some weird medical practices that does not make sense.
It is highly recommended to use this model just for research purpose and not as a commercial use.
Also, as no data anonymization was performed, the model will be generating random names, addresses or terms that are not intended.

## Future Works

Need to perform data anonymization to refine the model output to be generic.


## 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. -->
For fine-tuning the model, 5000 samples were indiced from the `sid6i7/patient-doctor` dataset, out of which 4500 were used for training, 250 were used for validation and testing.

#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- precision: bfloat16
- alpha: 32
- rank: 16
- lora dropout: 0.05
- target modules: 'o_proj', 'v_proj', 'up_proj', 'q_proj', 'down_proj', 'gate_proj', and 'k_proj'
- optimizer: paged_adamw_32bit
- learning rate: 2e-4


### Results

The loss progression during fine-tuning of gemma-2-2b on the dataset is as follows:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/635e4f9d19b4345264daecac/_lCch0VfuEVpGzjn1ilzS.png)

#### Hardware

The model was trained on 2 x T4 GPU. The total training time taken was 6 hours.