Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
----
+datasets:
+- starmpcc/Asclepius-Synthetic-Clinical-Notes
+language:
+- en
+pipeline_tag: text2text-generation
+tags:
+- medical
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is official model checkpoint for Asclepius-7B [arxiv](todo)
+This model is the first publicly shareable clinical LLM, trained with synthetic data.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Model type:** Clinical LLM (Large Language Model)
+- **Language(s) (NLP):** English
+- **License:** CC-BY-NC-SA 4.0
+- **Finetuned from model [optional]:** LLaMA-7B
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/starmpcc/Asclepius
+- **Paper [optional]:** TODO Arxiv
+- **Data:** https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model can perform below 8 clinical NLP tasks, with clincal notes.
+- Named Entity Recognition
+- Abbreviation Expansion
+- Relation Extraction
+- Temporal Information Extraction
+- Coreference Resolution
+- Paraphrasing
+- Summarization
+- Question Answering
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!
+
+## How to Get Started with the Model
+
+```python
+prompt = """You are an intelligent clinical languge model.
+Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
+Write a response that appropriately completes the instruction.
+The response should provide the accurate answer to the instruction, while being concise.
+
+[Discharge Summary Begin]
+{note}
+[Discharge Summary End]
+
+[Instruction Begin]
+{question}
+[Instruction End] 
+"""
+
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-7B")
+model = AutoModel.from_pretrained("starmpcc/Asclepius-7B")
+
+note = "This is a sample note"
+question = "What is the diagnosis?"
+
+model_input = prompt.format(note=note, question=question)
+input_ids = tokenizer(model_input, return_tensors="pt").input_ids
+output = model.generate(input_ids)
+print(tokenizer.decode(output[0]))
+```
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- Initial training was conducted using causal language modeling on synthetic clinical notes.
+- It was then fine-tuned with clinical instruction-response pairs.
+- For a comprehensive overview of our methods, our upcoming paper will serve as a resource.
+
+#### Training Hyperparameters
+
+- We followed config used in [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
+- 
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Pre-Training (1 epoch): 1h 33m with 8x A100 80G
+- Instruction Fine-Tuning (3 epoch): 7h 26m with 8x A100 80G
+
+
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+
+