Update README.md

README.md

@@ -6,16 +6,14 @@ language:
 - ja
 metrics:
 - accuracy
+license: cc-by-nc-sa-4.0
 ---
 
 # JMedLLM-7B-v1
 
 ⚠️ Do not use it for medical purposes. Only for research purposes.
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
+This model is a Japanese medical LLM based on QWen2-7B-Instruct. 
 
 
 ## Model Details
@@ -26,21 +24,20 @@ metrics:
 
 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:** stardust-coder
+- **Funded by [optional]:** AIST KAKUSEI(2023)
+- **Shared by [optional]:** stardust-coder
+- **Language(s) (NLP):** Japanese
+- **License:** cc-by-nc-sa-4.0
+- **Finetuned from model [optional]:** QWen2-7B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [stardust-coder/jmedllm-7b-v1](https://huggingface.co/stardust-coder/jmedllm-7b-v1)
+- **Paper:** Coming soon...
+- **Demo:** None
 
 ## Uses
 
@@ -50,37 +47,65 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- 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]
+- Ask benchmark medical questions like medical license exams.
+- Further research purposes.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+Any medical uses.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+This model carries risks with use. 
+Evauation is only conducted with [IgakuQA](https://github.com/jungokasai/IgakuQA) in English and Japanese, and has not covered, nor could it cover all scenarios. 
+Its potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. 
+This model is not designed for any medical uses. 
+Those who download this model should perform safety testing and tuning before any usage.
+Users (both direct and downstream) should be aware of the risks, biases and limitations of the model. 
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import argparse
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base_model", type=str)
+    parser.add_argument("--peft_model", type=str)
+    return parser.parse_args()
+
+def main():
+    args = get_args()
+    base_model = AutoModelForCausalLM.from_pretrained(
+        args.base_model,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(args.base_model)
+    model = PeftModel.from_pretrained(base_model, args.peft_model, device_map="auto")
+
+    prompt = "hoge"
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+    with torch.no_grad():
+      generated_tokens = model.generate(
+          inputs=input_ids,
+          do_sample=False,
+      )[0]
+    generated_text = tokenizer.decode(generated_tokens)
+    print(generated_text)
+
+if __name__ == "__main__" :
+    main()
+```
 
 ## Training Details
 
@@ -88,125 +113,44 @@ Use the code below to get started with the model.
 
 <!-- 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]
+1. Naika-Text : collected from a medical journal (not made public)
+2. USMLEJP(train split) : translated into Japanese by hand (not made public)
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+1. Full parameter, 5 epoch
+2. LoRA, 5 epoch
 
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** dtype = AUTO, LoRA target modules = ALL <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
+#### Train run time
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+1. 'train_runtime': 27214.5232, 'epoch': 5, 'global_step': 1890
+2. 'train_runtime': 102718.0035, 'epoch': 5, 'global_step': 3145
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### 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]
+Coming soon...
 
 ## Technical Specifications [optional]
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+QWen2-7B
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+G.large x 1 in ABCI
 
 #### 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
-
-[More Information Needed]
-
+[MS-SWIFT](https://github.com/modelscope/swift)
 
 
 # Acknowledgement