rohitanurag
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ClinicalGPT-Pubmed-Instruct-V1.0

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+# 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.