| # ClinicalGPT-Pubmed-Instruct-V1.0 | |
| ## Overview | |
| ClinicalGPT-Pubmed-Instruct-V1.0 is a specialized language model fine-tuned on the mistralai/Mistral-7B-Instruct-v0.2 base model. While primarily trained on 10 million PubMed abstracts and titles, this model excels at generating responses to life science-related medical questions with relevant citations from various scientific sources. | |
| ## Key Features | |
| - Built on Mistral-7B-Instruct-v0.2 base model | |
| - Primary training on 10M PubMed abstracts and titles | |
| - Generates answers with scientific citations from multiple sources | |
| - Specialized for medical and life science domains | |
| ## Applications | |
| - **Life Science Research**: Generate accurate, referenced answers for biomedical and healthcare queries | |
| - **Pharmaceutical Industry**: Support healthcare professionals with evidence-based responses | |
| - **Medical Education**: Aid students and educators with scientifically-supported content from various academic sources | |
| ## System Requirements | |
| ### GPU Requirements | |
| - **Minimum VRAM**: 16-18 GB for inference in BF16 (BFloat16) precision | |
| - **Recommended GPUs**: | |
| - NVIDIA A100 (20GB) - Ideal for BF16 precision | |
| - Any GPU with 16+ GB VRAM | |
| - Performance may vary based on available memory | |
| ### Software Prerequisites | |
| - Python 3.x | |
| - PyTorch | |
| - Transformers library | |
| ### Basic Implementation | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # Set parameters | |
| model_dir = "rohitanurag/ClinicalGPT-Pubmed-Instruct-V1.0" | |
| max_new_tokens = 1500 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
| model = AutoModelForCausalLM.from_pretrained(model_dir).to(device) | |
| # Define your question | |
| question = "What is the role of the tumor microenvironment in cancer progression?" | |
| prompt = f"""Please provide the answer to the question asked. | |
| ### Question: {question} | |
| ### Answer: """ | |
| # Tokenize input | |
| inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device) | |
| # Generate output | |
| output_ids = model.generate( | |
| inputs.input_ids, | |
| attention_mask=inputs.attention_mask, | |
| max_new_tokens=1000, | |
| repetition_penalty=1.2, | |
| pad_token_id=tokenizer.eos_token_id, | |
| ) | |
| # Decode and print | |
| generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| print(f"Generated Answer:\n{generated_text}") | |
| ``` | |
| ## Sample Output | |
| ``` | |
| ### Question: What is the role of the tumor microenvironment in cancer progression, and how does it influence the response to therapy? | |
| ### Answer: | |
| The tumor microenvironment (TME) refers to the complex network of cells, extracellular matrix components, signaling molecules, and immune cells that surround a growing tumor. It plays an essential role in regulating various aspects of cancer development and progression... | |
| ### References: | |
| 1. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646-74. doi:10.1016/j.cell.2011.03.019 | |
| 2. Coussens LM, Pollard JW. Angiogenesis and Metastasis. Nature Reviews Cancer. 2006;6(1):57-68. doi:10.1038/nrc2210 | |
| 3. Mantovani A, et al. Cancer's Educated Environment: How the Tumour Microenvironment Promotes Progression. Cell. 2017;168(6):988-1001.e15. doi:10.1016/j.cell.2017.02.011 | |
| 4. Cheng YH, et al. Targeting the Tumor Microenvironment for Improved Therapy Response. Journal of Clinical Oncology. 2018;34(18_suppl):LBA10001. doi:10.1200/JCO.2018.34.18_suppl.LBA10001 | |
| 5. Kang YS, et al. Role of the Tumor Microenvironment in Cancer Immunotherapy. Current Opinion in Pharmacology. 2018;30:101-108. doi:10.1016/j.ycoop.20 | |
| ``` | |
| ## Model Details | |
| - **Base Model**: Mistral-7B-Instruct-v0.2 | |
| - **Primary Training Data**: 10 million PubMed abstracts and titles | |
| - **Specialization**: Medical question-answering with scientific citations | |
| - **Output**: Generates detailed answers with relevant academic references | |
| ## Future Development | |
| ClinicalGPT-Pubmed-Instruct-V2.0 is under development, featuring: | |
| - Training on 20 million scientific articles | |
| - Inclusion of full-text articles from various academic sources | |
| - Enhanced performance for life science tasks | |
| - Expanded citation capabilities across multiple scientific databases | |
| ## Contributors | |
| - Rohit Anurag – Principal Data Scientist | |
| - Aneesh Paul – Data Scientist | |
| ## License | |
| Licensed under the Apache License, Version 2.0. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 | |
| ## Citation | |
| If you use this model in your research, please cite it appropriately. | |
| ## Support | |
| For issues and feature requests, please use the GitHub issue tracker. | |