Update README.md

README.md

@@ -6,10 +6,10 @@ tags: []
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
-
+Finetuned "BioMistral/BioMistral-7B" with MedQA dataset. 
 
 ## Model Details
+A Collection of Open-Source Pretrained Large Language Models for Medical Domains finetuned with MedQA dataset.
 
 ### Model Description
 
@@ -17,40 +17,148 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** mychen76
+- **Model type:** BioMedical
+- **Finetuned from model:** BioMistral/BioMistral-7B
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
+- **dataset:** MedQA dataset
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+ 
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Load Model:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+base_model_id = "mychen76/biomistral_medqa_v1"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id,
+    add_eos_token=True,
+    add_bos_token=True,
+)
 
 ## 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. -->
+```
+*** Information ***
+```
+eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
 
-### Direct Use
+### Question type:
+information
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Question:
+What are the genetic changes related to X-linked lymphoproliferative disease ?
 
-[More Information Needed]
+### Answer:
+"""
 
-### Downstream Use [optional]
+model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=300)[0], skip_special_tokens=True))
+```
+result:
+```
+From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
+
+### Question type:
+information
+
+### Question:
+What are the genetic changes related to X-linked lymphoproliferative disease ?
+
+### Answer:
+X-linked lymphoproliferative disease (XLP) is a rare primary immunodeficiency syndrome. XLP is caused by mutations in SH2D1A gene, which encodes the cytoplasmic signaling protein SLAM-associated protein ( client protein-SLAM). SLAM is a member of the signaling lymphocytic activation molecule family of receptors, which are involved in the regulation of lymphocyte activation and proliferation. The SLAM receptor is expressed on the surface of B and T lymphocytes, natural killer cells, and monocytes. Mutations in SH2D1A gene lead to impaired signaling through the SLAM receptor, resulting in a deficiency in the activation and proliferation of B and T lymphocytes. This leads to a decrease in the number of B and T lymphocytes, resulting in a weakened immune response.
+```
+
+*** Frequency ***
+```
+eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
+
+### Question type:
+frequency
+
+### Question:
+How many people are affected by Smith-Lemli-Opitz syndrome ?
+
+### Answer:
+"""
+
+model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")
+
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=300)[0], skip_special_tokens=True))
+```
+result:
+```
+From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
+
+### Question type:
+frequency
+
+### Question:
+How many people are affected by Smith-Lemli-Opitz syndrome ?
+
+### Answer:
+Smith-Lemli-Opitz syndrome (SLOS) is a rare autosomal recessive disorder of human development. It is characterized by a wide range of symptoms, including growth and developmental delay, intellectual disability, characteristic facial features, and congenital heart defects. The prevalence of SLOS is estimated to be 1 in 15,000 to 1 in 25,000 live births.
+```
+
+*** Symptons ***
+```
+eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
+
+### Question type:
+symptoms
+
+### Question:
+What are the symptoms of Norrie disease ?
+
+### Answer:
+"""
+
+model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")
+
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=300)[0], skip_special_tokens=True))
+```
+Result:
+```
+Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer:
+
+### Question type:
+symptoms
+
+### Question:
+What are the symptoms of Norrie disease ?
+
+### Answer:
+Norrie disease is a rare, X-linked recessive disorder of the blood vessels. It is characterized by a variety of symptoms, including glaucoma, mental retardation, seizures, and deafness.
+```
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
+images
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 [More Information Needed]
@@ -67,9 +175,6 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
 
 [More Information Needed]
 
@@ -77,6 +182,7 @@ Use the code below to get started with the model.
 
 ### Training Data
 
+- **dataset:** keivalya/MedQuad-MedicalQnADataset
 <!-- This should link to a Dataset 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. -->
 
 [More Information Needed]
@@ -130,70 +236,15 @@ Use the code below to get started with the model.
 
 #### Summary
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
 ## 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]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+Arxiv : https://arxiv.org/abs/2402.10373
 
-[More Information Needed]
+@misc{labrak2024biomistral,
+      title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, 
+      author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour},
+      year={2024},
+      eprint={2402.10373},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}