---
license: mit
datasets:
- keivalya/MedQuad-MedicalQnADataset
language:
- en
library_name: diffusers
tags:
- medical
---

# Model Card for GaiaMiniMed

This is a medical fine tuned model from the [Falcon-7b-Instruction](https://huggingface.co/tiiuae/falcon-7b-instruct) Base using 500 steps & 6 epochs with [MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) Dataset from [keivalya](https://huggingface.co/datasets/keivalya)


## Model Details

### Model Description

- **Developed by:** [Tonic](https://www.huggingface.co/tonic)
- **Shared by :** [Tonic](https://www.huggingface.co/tonic)
- **Model type:** Medical Fine-Tuned Conversational Falcon 7b (Instruct)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:**[tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
- 
### Model Sources [optional]

- **Repository:** [Github](https://github.com/Josephrp/AI-challenge-hackathon/blob/master/falcon_7b_instruct_GaiaMiniMed_dataset.ipynb)
- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}

## Uses

Use this model like you would use Falcon Instruct Models

### Direct Use

This model is intended for educational purposes only , always consult a doctor for the best advice.

This model should perform better at medical QnA tasks in a conversational manner.

It is our hope that it will help improve patient outcomes and public health.

### Downstream Use

Use this model next to others and have group conversations to produce diagnoses , public health advisory , and personal hygene improvements.

### Out-of-Scope Use

This model is not meant as a decision support system in the wild, only for educational use.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

{{ bias_risks_limitations | default("[More Information Needed]", true)}}

## How to Get Started with the Model

Use the code below to get started with the model.

{{ get_started_code | default("[More Information Needed]", true)}}

## Training Details

### Results


![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/F8GfMSJcAaH7pXvpUK_r3.png)

```json

TrainOutput(global_step=6150, training_loss=1.0597990553941183,
{'epoch': 6.0})
```


### Training Data


```json 

DatasetDict({
    train: Dataset({
        features: ['qtype', 'Question', 'Answer'],
        num_rows: 16407
    })
})

```


### Training Procedure 


#### Preprocessing [optional]

```

trainable params: 4718592 || all params: 3613463424 || trainables%: 0.13058363808693696

```

#### Training Hyperparameters

- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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

```json

metrics={'train_runtime': 30766.4612, 'train_samples_per_second': 3.2, 'train_steps_per_second': 0.2,
'total_flos': 1.1252790565109983e+18, 'train_loss': 1.0597990553941183,", true)}}

```

## 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:** {{ hardware | default("[More Information Needed]", true)}}
- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}

## Technical Specifications

### Model Architecture and Objective

```json

PeftModelForCausalLM(
  (base_model): LoraModel(
    (model): FalconForCausalLM(
      (transformer): FalconModel(
        (word_embeddings): Embedding(65024, 4544)
        (h): ModuleList(
          (0-31): 32 x FalconDecoderLayer(
            (self_attention): FalconAttention(
              (maybe_rotary): FalconRotaryEmbedding()
              (query_key_value): Linear4bit(
                in_features=4544, out_features=4672, bias=False
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4544, out_features=16, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=16, out_features=4672, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (dense): Linear4bit(in_features=4544, out_features=4544, bias=False)
              (attention_dropout): Dropout(p=0.0, inplace=False)
            )
            (mlp): FalconMLP(
              (dense_h_to_4h): Linear4bit(in_features=4544, out_features=18176, bias=False)
              (act): GELU(approximate='none')
              (dense_4h_to_h): Linear4bit(in_features=18176, out_features=4544, bias=False)
            )
            (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
          )
        )
        (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
      )
      (lm_head): Linear(in_features=4544, out_features=65024, bias=False)
    )
  )
)

```

### Compute Infrastructure

Google Collaboratory

#### Hardware

A100


## Model Card Authors 

[Tonic](https://huggingface.co/tonic)

## Model Card Contact

"[Tonic](https://huggingface.co/tonic)