Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model
OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks.
🏥 Biomedical Specialization: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency.
🎓 Superior Performance: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks.
🧠 Advanced Training Techniques: OpenBioLLM-70B builds upon the powerful foundations of the Meta-Llama-3-70B-Instruct and Meta-Llama-3-70B-Instruct models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include:
The model can be fine-tuned for more specialized tasks and datasets as needed.
OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.
We are excited to share OpenBioLLM-70B with researchers and developers around the world.
Use with transformers
Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.
See the snippet below for usage with Transformers:
python
1import transformers
2import torch
34model_id ="aaditya/OpenBioLLM-Llama3-70B"56pipeline = transformers.pipeline(7"text-generation",8 model=model_id,9 model_kwargs={"torch_dtype": torch.bfloat16},10 device="auto",11)1213messages =[14{"role":"system","content":"You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."},15{"role":"user","content":"How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"},16]1718prompt = pipeline.tokenizer.apply_chat_template(19 messages,20 tokenize=False,21 add_generation_prompt=True22)2324terminators =[25 pipeline.tokenizer.eos_token_id,26 pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")27]2829outputs = pipeline(30 prompt,31 max_new_tokens=256,32 eos_token_id=terminators,33 do_sample=True,34 temperature=0.0,35 top_p=0.9,36)37print(outputs[0]["generated_text"][len(prompt):])
Training procedure
Training hyperparameters
Click to see details
learning_rate: 0.0002
lr_scheduler: cosine
train_batch_size: 12
eval_batch_size: 8
GPU: H100 80GB SXM5
num_devices: 8
optimizer: adamw_bnb_8bit
lr_scheduler_warmup_steps: 100
num_epochs: 4
Peft hyperparameters
Click to see details
adapter: qlora
lora_r: 128
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: true
-lora_target_modules:
q_proj
v_proj
k_proj
o_proj
gate_proj
down_proj
up_proj
Training results
Framework versions
Transformers 4.39.3
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.1
Axolotl
Lm harness for evaluation
Benchmark Results
🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.
🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.
Clinical KG
Medical Genetics
Anatomy
Pro Medicine
College Biology
College Medicine
MedQA 4 opts
PubMedQA
MedMCQA
Avg
OpenBioLLM-70B
92.93
93.197
83.904
93.75
93.827
85.749
78.162
78.97
74.014
86.05588
Med-PaLM-2 (5-shot)
88.3
90
77.8
95.2
94.4
80.9
79.7
79.2
71.3
84.08
GPT-4
86.04
91
80
93.01
95.14
76.88
78.87
75.2
69.52
82.85
Med-PaLM-1 (Flan-PaLM, 5-shot)
80.4
75
63.7
83.8
88.9
76.3
67.6
79
57.6
74.7
OpenBioLLM-8B
76.101
86.1
69.829
78.21
84.213
68.042
58.993
74.12
56.913
72.502
Gemini-1.0
76.7
75.8
66.7
77.7
88
69.2
58
70.7
54.3
70.79
GPT-3.5 Turbo 1106
74.71
74
72.79
72.79
72.91
64.73
57.71
72.66
53.79
66
Meditron-70B
66.79
69
53.33
71.69
76.38
63
57.1
76.6
46.85
64.52
gemma-7b
69.81
70
59.26
66.18
79.86
60.12
47.21
76.2
48.96
64.18
Mistral-7B-v0.1
68.68
71
55.56
68.38
68.06
59.54
50.82
75.4
48.2
62.85
Apollo-7B
62.26
72
61.48
69.12
70.83
55.49
55.22
39.8
53.77
60
MedAlpaca-7b
57.36
69
57.04
67.28
65.28
54.34
41.71
72.8
37.51
58.03
BioMistral-7B
59.9
64
56.5
60.4
59
54.7
50.6
77.5
48.1
57.3
AlpaCare-llama2-7b
49.81
49
45.92
33.82
50
43.35
29.77
72.2
34.42
45.36
ClinicalGPT
30.56
27
30.37
19.48
25
24.27
26.08
63.8
28.18
30.52
Detailed Medical Subjectwise accuracy
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Use Cases & Examples
🚨 **Below results are from the quantized version of OpenBioLLM-70B
Summarize Clinical Notes
OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries
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Answer Medical Questions
OpenBioLLM-70B can provide answers to a wide range of medical questions.
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Click to see details
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Clinical Entity Recognition
OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.
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Biomarkers Extraction
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Classification
OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization
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De-Identification
OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.
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Advisory Notice!
While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.
Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations.
OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.
Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:
Thorough testing and evaluation in relevant clinical scenarios
Alignment with evidence-based guidelines and best practices
Mitigation of potential biases and failure modes
Integration with human oversight and interpretation
Compliance with regulatory and ethical standards
Always consult a qualified healthcare provider for personal medical needs.
Citation
If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:
@misc{OpenBioLLMs,
author = {Ankit Pal, Malaikannan Sankarasubbu},
title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}
}
The accompanying paper is currently in progress and will be released soon.
💌 Contact
We look forward to hearing you and collaborating on this exciting project!
Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.
