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--- |
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license: apache-2.0 |
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base_model: |
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- mistralai/Mistral-7B-Instruct-v0.2 |
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pipeline_tag: question-answering |
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library_name: peft |
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tags: |
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- medical |
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- lifescience |
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- drugdiscovery |
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--- |
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# ClinicalGPT-Pubmed-Instruct-V1.0 |
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## Overview |
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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. |
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## Key Features |
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- Built on Mistral-7B-Instruct-v0.2 base model |
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- Primary training on 10M PubMed abstracts and titles |
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- Generates answers with scientific citations from multiple sources |
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- Specialized for medical and life science domains |
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## Applications |
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- **Life Science Research**: Generate accurate, referenced answers for biomedical and healthcare queries |
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- **Pharmaceutical Industry**: Support healthcare professionals with evidence-based responses |
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- **Medical Education**: Aid students and educators with scientifically-supported content from various academic sources |
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## System Requirements |
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### GPU Requirements |
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- **Minimum VRAM**: 16-18 GB for inference in BF16 (BFloat16) precision |
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- **Recommended GPUs**: |
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- NVIDIA A100 (20GB) - Ideal for BF16 precision |
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- Any GPU with 16+ GB VRAM |
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- Performance may vary based on available memory |
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### Software Prerequisites |
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- Python 3.x |
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- PyTorch |
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- Transformers library |
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### Basic Implementation |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Set parameters |
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model_dir = "partex-nv/ClinicalGPT-Pubmed-Instruct-V1.0" |
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max_new_tokens = 1500 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained(model_dir) |
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model = AutoModelForCausalLM.from_pretrained(model_dir).to(device) |
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# Define your question |
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question = "What is the role of the tumor microenvironment in cancer progression?" |
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prompt = f"""Please provide the answer to the question asked. |
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### Question: {question} |
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### Answer: """ |
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# Tokenize input |
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inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device) |
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# Generate output |
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output_ids = model.generate( |
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inputs.input_ids, |
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attention_mask=inputs.attention_mask, |
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max_new_tokens=1000, |
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repetition_penalty=1.2, |
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pad_token_id=tokenizer.eos_token_id, |
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) |
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# Decode and print |
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generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(f"Generated Answer:\n{generated_text}") |
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``` |
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## Sample Output |
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``` |
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### Question: What is the role of the tumor microenvironment in cancer progression, and how does it influence the response to therapy? |
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### Answer: |
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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... |
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### References: |
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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 |
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2. Coussens LM, Pollard JW. Angiogenesis and Metastasis. Nature Reviews Cancer. 2006;6(1):57-68. doi:10.1038/nrc2210 |
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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 |
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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 |
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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 |
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``` |
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## Model Details |
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- **Base Model**: Mistral-7B-Instruct-v0.2 |
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- **Primary Training Data**: 10 million PubMed abstracts and titles |
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- **Specialization**: Medical question-answering with scientific citations |
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- **Output**: Generates detailed answers with relevant academic references |
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## Future Development |
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ClinicalGPT-Pubmed-Instruct-V2.0 is under development, featuring: |
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- Training on new 20 million pubmed articles |
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- Inclusion of full-text articles from various academic sources |
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- Enhanced performance for life science tasks |
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- Expanded citation capabilities across multiple scientific databases |
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## Contributors |
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- Rohit Anurag – Principal Data Scientist |
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- Aneesh Paul – Data Scientist |
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## License |
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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 |
