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---
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
```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) |