Base 7b V0.2

🚀 Model Card for Internist.ai 7b

Internist.ai 7b is a large language model in the medical domain, trained by medical doctors. It demonstrates the benefits of a physician - in - the - loop approach. The training data was carefully curated by medical doctors to ensure clinical relevance and meet the quality requirements for clinical practice.

With this 7b model, we present the first 7b model to score above the 60% pass threshold on MedQA (USMLE) and outperform models of similar size across most medical evaluations.

This model serves as a proof of concept, and larger models trained on a larger corpus of medical literature are in the planning. If you'd like to sponsor some compute to speed up this training, feel free to contact us.

🚀 Quick Start

This section provides a quick overview of how to use the model. For more detailed instructions, refer to the Uses section.

✨ Features

• Physician - in - the - loop: Trained by medical doctors with carefully curated training data for clinical relevance.
• High performance: Scores above 60% on MedQA (USMLE) and outperforms similar - sized models in most medical evaluations.
• Multidomain capabilities: Retains capabilities in other domains while focusing on medical knowledge.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The model uses the Alpaca format and is available as a chat template:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("internistai/base-7b-v0.2")
tokenizer = AutoTokenizer.from_pretrained("internistai/base-7b-v0.2")

messages = [
    {"role": "user", "content": "Describe the anatomy of nutcracker syndrome"},
]

encodeds = tokenizer.apply_chat_template(messages, add_generation_prompt=True ,return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Advanced Usage

There is no specific advanced usage example provided in the original document.

📚 Documentation

Model Details

Model Sources

• Trainer: [Axolotl](https://github.com/OpenAccess - AI - Collective/axolotl)
• Paper: Impact of High - Quality, Mixed - Domain Data on the Performance of Medical Language Models

Uses

This model was trained to demonstrate the benefit of using high - quality and relevant medical literature as well as general data to retain capabilities in other domains. Therefore, the model was not trained for any specific use and did not undergo additional instruction tuning to ensure safety.

The model in its current state can be useful for medical professionals as an assistant, whether for clinical decision support or documentation. We do not recommend the use of this model by non - professionals who may not be able to notice errors.

We recommend additional task - specific training and safety evaluation before using the model in a real - world setting.

Out - of - Scope Use

We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.

Professional Evaluation

We created a free - response evaluation dataset of 100 questions and prompted the model and GPT - 4 as a comparison with these questions. We then collected the prompt/answer pairs and presented them to 10 medical doctors of different specialties with questions to be answered with a 7 - point Likert scale (See the paper for more information).

Training Details

Training Data

Internist.ai 7b contains a total of 2.3B tokens:

• [General Domain](https://huggingface.co/datasets/Open - Orca/OpenOrca): OpenOrca - GPT4 is a state - of - the - art general domain dataset generated from Flan prompts using GPT - 4.
• Medical Guidelines: 11,332 articles from UpToDate were included, as well as domain - specific guidelines provided by physicians to cover the [USMLE Content Outline](https://www.usmle.org/sites/default/files/2021 - 08/USMLE_Content_Outline.pdf).
• Medical Books: 10,376 textbooks were sourced from PMC LitArch and our university library.
• Synthetic Data: We generated 400M tokens by prompting a larger model with instructions to transform and adapt extracts from the Medical Guidelines.

Data Availability: Considering the datasets contain proprietary information, we will not be releasing the datasets publicly. Regarding the synthetic dataset, as we show in the paper, the model trained exclusively on this dataset performs very poorly and was not up to our standards. Due to its poor quality, we decided not to release it.

Training Procedure

We used Axolotl to train on a server with 4 NVIDIA A100 80GB GPUs for a total of 450 GPU hours. We used FlashAttention, NEFTune and sample packing with the parameters described below.

Training Hyperparameters

Evaluation

Testing Data & Metrics

Testing Data

• MedQA (USMLE) - 4 options
• MedMCQA
• PubMedQA
• MMLU

Metrics

• Accuracy: we ran standardized 0 - shot benchmarks using [lm - evaluation - harness](https://github.com/maximegmd/lm - evaluation - harness/tree/big - refactor/lm_eval).

Results

We include benchmarks on MedQA (4 options), MedMCQA and PubMedQA of our model and models of similar size and achieve the first USMLE passing score of 60% on the MedQA benchmark.

*: PMC LLaMA 7b performed poorly on the benchmark, likely due to a mismatch of formatting and a lack of instruction tuning. We include in parenthesis the results reported by the authors when available.

**: Meditron 7b's results in MMLU are reported for transparency but are inconsistent with the average of 54.2 reported in their paper. Do not hesitate to communicate the details on each category so we can update the table.

Citation

BibTeX: If you use Internist.ai 7b, please cite us:

@article{10.1093/jamia/ocae120,
    author = {Griot, Maxime and Hemptinne, Coralie and Vanderdonckt, Jean and Yuksel, Demet},
    title = "{Impact of high-quality, mixed-domain data on the performance of medical language models}",
    journal = {Journal of the American Medical Informatics Association},
    volume = {31},
    number = {9},
    pages = {1875-1883},
    year = {2024},
    month = {05},
    abstract = "{To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliability.We curated a comprehensive collection of high-quality, domain-specific data and used it to train several models, each with different subsets of this data. These models were rigorously evaluated against standard medical benchmarks, such as the USMLE, to measure their performance. Furthermore, for a thorough effectiveness assessment, they were compared with other state-of-the-art medical models of comparable size.The models trained with a mix of high-quality, domain-specific, and general data showed superior performance over those trained on larger, less clinically relevant datasets (P < .001). Our 7-billion-parameter model Med5 scores 60.5\\% on MedQA, outperforming the previous best of 49.3\\% from comparable models, and becomes the first of its size to achieve a passing score on the USMLE. Additionally, this model retained its proficiency in general domain tasks, comparable to state-of-the-art general domain models of similar size.Our findings underscore the importance of integrating high-quality, domain-specific data in training large language models for medical purposes. The balanced approach between specialized and general data significantly enhances the model’s clinical relevance and performance.This study sets a new standard in medical language models, proving that a strategically trained, smaller model can outperform larger ones in clinical relevance and general proficiency, highlighting the importance of data quality and expert curation in generative artificial intelligence for healthcare applications.}",
    issn = {1527-974X},
    doi = {10.1093/jamia/ocae120},
    url = {https://doi.org/10.1093/jamia/ocae120},
    eprint = {https://academic.oup.com/jamia/article-pdf/31/9/1875/58868289/ocae120.pdf},
}

⚠️ Important Note

The model was designed by medical doctors for medical doctors and did not undergo specific training to address potential security issues when used by non - medical professionals. We highly recommend against the use of this model in a live environment without extensive evaluation through prospective clinical trials and additional training to meet the required safety levels.

💡 Usage Tip

The model in its current state can be useful for medical professionals as an assistant, but additional task - specific training and safety evaluation are recommended before using it in a real - world setting.