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--- |
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license: mit |
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datasets: |
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- keivalya/MedQuad-MedicalQnADataset |
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language: |
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- en |
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library_name: diffusers |
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tags: |
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- medical |
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--- |
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# Model Card for GaiaMiniMed |
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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) |
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## Model Details |
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### Model Description |
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- **Developed by:** [Tonic](https://www.huggingface.co/tonic) |
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- **Shared by :** [Tonic](https://www.huggingface.co/tonic) |
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- **Model type:** Medical Fine-Tuned Conversational Falcon 7b (Instruct) |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:**[tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) |
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- |
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### Model Sources [optional] |
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- **Repository:** [Github](https://github.com/Josephrp/AI-challenge-hackathon/blob/master/falcon_7b_instruct_GaiaMiniMed_dataset.ipynb) |
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- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}} |
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## Uses |
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Use this model like you would use Falcon Instruct Models |
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### Direct Use |
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This model is intended for educational purposes only , always consult a doctor for the best advice. |
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This model should perform better at medical QnA tasks in a conversational manner. |
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It is our hope that it will help improve patient outcomes and public health. |
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### Downstream Use |
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Use this model next to others and have group conversations to produce diagnoses , public health advisory , and personal hygene improvements. |
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### Out-of-Scope Use |
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This model is not meant as a decision support system in the wild, only for educational use. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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{{ bias_risks_limitations | default("[More Information Needed]", true)}} |
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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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{{ get_started_code | default("[More Information Needed]", true)}} |
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## Training Details |
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### Results |
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```json |
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TrainOutput(global_step=6150, training_loss=1.0597990553941183, |
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{'epoch': 6.0}) |
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``` |
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### Training Data |
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```json |
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DatasetDict({ |
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train: Dataset({ |
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features: ['qtype', 'Question', 'Answer'], |
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num_rows: 16407 |
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}) |
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}) |
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``` |
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### Training Procedure |
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#### Preprocessing [optional] |
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``` |
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trainable params: 4718592 || all params: 3613463424 || trainables%: 0.13058363808693696 |
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``` |
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#### Training Hyperparameters |
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- **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 --> |
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#### Speeds, Sizes, Times [optional] |
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```json |
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metrics={'train_runtime': 30766.4612, 'train_samples_per_second': 3.2, 'train_steps_per_second': 0.2, |
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'total_flos': 1.1252790565109983e+18, 'train_loss': 1.0597990553941183,", true)}} |
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``` |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** {{ hardware | default("[More Information Needed]", true)}} |
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- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}} |
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- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}} |
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- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}} |
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- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}} |
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## Technical Specifications |
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### Model Architecture and Objective |
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```json |
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PeftModelForCausalLM( |
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(base_model): LoraModel( |
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(model): FalconForCausalLM( |
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(transformer): FalconModel( |
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(word_embeddings): Embedding(65024, 4544) |
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(h): ModuleList( |
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(0-31): 32 x FalconDecoderLayer( |
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(self_attention): FalconAttention( |
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(maybe_rotary): FalconRotaryEmbedding() |
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(query_key_value): Linear4bit( |
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in_features=4544, out_features=4672, bias=False |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4544, out_features=16, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=16, out_features=4672, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(dense): Linear4bit(in_features=4544, out_features=4544, bias=False) |
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(attention_dropout): Dropout(p=0.0, inplace=False) |
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) |
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(mlp): FalconMLP( |
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(dense_h_to_4h): Linear4bit(in_features=4544, out_features=18176, bias=False) |
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(act): GELU(approximate='none') |
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(dense_4h_to_h): Linear4bit(in_features=18176, out_features=4544, bias=False) |
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) |
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(input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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(ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) |
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) |
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(lm_head): Linear(in_features=4544, out_features=65024, bias=False) |
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) |
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) |
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) |
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``` |
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### Compute Infrastructure |
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Google Collaboratory |
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#### Hardware |
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A100 |
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## Model Card Authors |
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[Tonic](https://huggingface.co/tonic) |
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## Model Card Contact |
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"[Tonic](https://huggingface.co/tonic) |
