README.md

---
library_name: transformers
tags:
- llm
- llama3
- rare disease
license: llama3
language:
- en
pipeline_tag: text-generation
---

## Model Details
 ReidLM is a fine-tuned version of Meta's LLaMA 3 model, specifically optimized for generating high-quality, contextually accurate responses in the domain of rare diseases. <br>
 Utilizing the Evol-Instruct methodology, this model was fine-tuned with a dataset of over 400 rare diseases.

### Model Description

- **Developed by:** MSRIT Students
- **Model type:** Transformer-based Large Language Model (LLM)
- **Language(s) (NLP):** English
- **License:** Llama 3 Community License Agreement
- **Finetuned from model:** Meta-Llama-3-8B-Instruct

## Uses

ReidLM is designed for direct use in generating insightful and reliable information to support healthcare professionals and researchers in diagnosing and managing rare diseases. It can be used as an educational tool for training medical students and professionals about rare diseases.

### Out-of-Scope Use

ReidLM is specifically designed for generating information related to rare diseases and should not be used for the following purposes:

- **Non-Medical Domains:** ReidLM is optimized for rare disease information and may not perform well in other domains such as finance, law, general health conditions, or any other non-medical fields.<br>
- **General Conversational AI:**  While capable of generating detailed information on rare diseases, ReidLM may not be suitable for general conversational AI tasks that require a broad understanding of various topics. <br>


## Bias, Risks, and Limitations

ReidLM, like all large language models, has inherent biases and limitations that users should be aware of:<br>
- **Ethical Concerns:** There is a risk of over-reliance on AI for medical decisions, which should always be validated by healthcare professionals.<br>
- **Accuracy:** While the model strives for accuracy, it may generate incorrect or incomplete information, especially in highly specialized or novel cases.

## Getting Started with the Model

Use the code below to get started with the model.
## Use with Transformers AutoModelForCausalLM

```
import transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the pre-trained model and tokenizer
model_name = "Tanvi03/ReidLM"  
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(prompt, max_length=1000):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs.input_ids, max_length=max_length, num_return_sequences=1)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

prompt = "Explain MEN-1 with respect to how it affects the pituitary gland. What is the other name for this syndrome?"
generated_text = generate_text(prompt)
print(generated_text)
```



<!--## Training Details -->
### Training Data
This link provides the Evol-Instruct question-and-answer dataset
https://raw.githubusercontent.com/M-e-e-n-a/Synthetic-Dataset-Creation/main/combined_dataset.json
<!-- 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. -->

<!--### Training Procedure -->

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#### Training Hyperparameters

  num_train_epochs=3, <br>
  per_device_train_batch_size=4,<br>
  gradient_accumulation_steps=2,<br>
  optim="paged_adamw_8bit",<br>
  save_steps=1000,<br>
  logging_steps=30,<br>
  learning_rate=2e-4,<br>
  weight_decay=0.01,<br>
  fp16=True,<br>
  max_grad_norm=1.0,<br>
  warmup_ratio=0.1<br>
  <!-- -->
<!---#### Speeds, Sizes, Times [optional]

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<!---## Evaluation

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<!---### Testing Data, Factors & Metrics

#### Testing Data

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<!---#### Factors

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<!---### Results

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#### Summary



## Model Examination [optional]

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<!---## Environmental Impact

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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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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Software

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## Citation [optional]

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## Results

## Evaluation Metrics

<table>
  <thead>
    <tr>
      <th>Metrics</th>
      <th>Llama-2-7b</th>
      <th>Mistral-7b</th>
      <th>Mixtral-47B</th>
      <th>ReidLM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td align="center">ROUGE-1</td>
      <td align="center">0.3117</td>
      <td align="center">0.3188</td>
      <td align="center">0.2637</td>
      <td align="center">0.3281</td>
    </tr>
    <tr>
      <td align="center">ROUGE-2</td>
      <td align="center">0.1867</td>
      <td align="center">0.1176</td>
      <td align="center">0.1573</td>
      <td align="center">0.1270</td>
    </tr>
    <tr>
      <td align="center">ROUGE-L</td>
      <td align="center">0.1818</td>
      <td align="center">0.1449</td>
      <td align="center">0.2637</td>
      <td align="center">0.2031</td>
    </tr>
    <tr>
      <td align="center">ROUGE-LSUM</td>
      <td align="center">0.1818</td>
      <td align="center">0.1449</td>
      <td align="center">0.2637</td>
      <td align="center">0.2031</td>
    </tr>
    <tr>
      <td align="center">METEOR</td>
      <td align="center">0.0693</td>
      <td align="center">0.3088</td>
      <td align="center">0.4377</td>
      <td align="center">0.3662</td>
    </tr>
    <tr>
      <td align="center">BERTScore</td>
      <td align="center">0.8262</td>
      <td align="center">0.8538</td>
      <td align="center">0.9070</td>
      <td align="center">0.8782</td>
    </tr>
    <tr>
      <td align="center">G-Eval</td>
      <td align="center">0.35</td>
      <td align="center">0.42</td>
      <td align="center">0.78</td>
      <td align="center">0.87</td>
    </tr>
    <tr>
      <td align="center">QAG Score</td>
      <td align="center">0.1046</td>
      <td align="center">0.2061</td>
      <td align="center">0.3762</td>
      <td align="center">0.2609</td>
    </tr>
  </tbody>
</table>