library_name: transformers license: mit language:
- en base_model: meta-llama/Llama-2-7b-hf
Model Card for Clinical Studies Instruction-Finetuned LLaMA-7B
Model Details
Model Description
This model is a fine-tuned version of the LLaMA-2-7B model from Meta, optimized using 1.5 lakh open-source clinical studies data for instruction-based tasks. The fine-tuning process has tailored the model to better understand and generate responses in the domain of clinical studies, making it particularly useful for tasks involving medical and clinical research data.
- Developed by: Shudhanshu Shekhar
- Model type: Instruction-finetuned LLaMA-2-7B
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: Meta's LLaMA-2-7B
Model Sources
- Repository: [Link to your Hugging Face model repository]
- Paper [optional]: [Link to any related paper]
- Demo [optional]: [Link to a demo if available]
Uses
Direct Use
This model can be directly used for generating responses or insights from clinical studies data, facilitating tasks such as summarization, information retrieval, and instruction following in the medical and clinical research fields.
Downstream Use
The model can be further fine-tuned or integrated into applications focused on clinical decision support, medical research analysis, or healthcare-related natural language processing tasks.
Out-of-Scope Use
This model is not suitable for real-time medical diagnosis, treatment recommendations, or any other critical medical decision-making processes without human oversight. Misuse in contexts requiring precise and validated medical information could lead to incorrect or harmful outcomes.
Bias, Risks, and Limitations
While the model has been fine-tuned on clinical studies data, it may still exhibit biases present in the original data. Users should be cautious when interpreting outputs, particularly in sensitive or critical contexts such as healthcare. The model may also produce outdated or incorrect information if the underlying data is not current.
Recommendations
Users should critically evaluate the model's outputs and consider the context in which it is being used. It is advisable to have human oversight when deploying the model in healthcare or clinical environments.
How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dextersud/sudbits_llama_pharma_clincal_model")
model = AutoModelForCausalLM.from_pretrained("dextersud/sudbits_llama_pharma_clincal_model")
input_text = "Summarize the following clinical study on..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))