InMedData
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InMD-X-ONC

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+tags:
+- medical
+license: cc-by-nc-sa-4.0
+language:
+- en
+pipeline_tag: text-generation
+base_model: Intel/neural-chat-7b-v3-1
 ---
+## InMD-X: Large Language Models for Internal Medicine Doctors
+We introduce InMD-X, a collection of
+multiple large language models specifically designed
+to cater to the unique characteristics and demands
+of Internal Medicine Doctors (IMD). InMD-X represents
+a groundbreaking development in natural language
+processing, offering a suite of language models
+fine-tuned for various aspects of the internal medicine
+field. These models encompass a wide range of medical
+sub-specialties, enabling IMDs to perform more
+efficient and accurate research, diagnosis, and documentation.
+InMD-X’s versatility and adaptability
+make it a valuable tool for improving the healthcare
+industry, enhancing communication between healthcare
+professionals, and advancing medical research.
+Each model within InMD-X is meticulously tailored
+to address specific challenges faced by IMDs, ensuring
+the highest level of precision and comprehensiveness
+in clinical text analysis and decision support.
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+- **Model type:** [CausalLM]
+- **Language(s) (NLP):** [English]
+- **License:** [CC-BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+- **Finetuned from model [optional]:** [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Paper [optional]:** [InMD-X](http://arxiv.org/abs/2402.11883)
 ## Uses
+```python
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+peft_model_id = "InMedData/InMD-X-ONC"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer = tokenizer,
+    device_map="auto" # if you have GPU
+)
+def inference(pipeline, Qustion,answer_only = False):
+    sequences = pipeline("Answer the next question in one sentence.\n"+
+    Qustion,
+    do_sample=True,
+    top_k=10,
+    top_p = 0.9,
+    temperature = 0.2,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=500, # can increase the length of sequence
+    )
+    Answers = []
+    for seq in sequences:
+        Answer = seq['generated_text'].split(Qustion)[-1].replace("\n","")
+        Answers.append(Answer)
+    return Answers
+question = 'What is the association between long-term beta-blocker use after myocardial infarction (MI) and the risk of reinfarction and death?'
+answers = inference(pipeline, question)
+print(answers)
+```
+### List of LoRA config
+based on [Parameter-Efficient Fine-Tuning (PEFT)](https://github.com/huggingface/peft)
+Parameter | PT | SFT
+:------:| :------:| :------:
+r | 8 | 8
+lora alpha | 32 | 32
+lora dropout | 0.05 | 0.05
+target | q, k, v, o,up, down, gate | q, k, v, o,up,down, gate
+### List of Training arguments
+based on [Transformer Reinforcement Learning (TRL)](https://github.com/huggingface/trl)
+Parameter | PT | SFT
+:------:| :------:| :------:
+train epochs | 3 | 1
+per device train batch size | 1 | 1
+optimizer | adamw_hf | adamw_hf
+evaluation strategy | no | no
+learning_rate | 1e-4 | 1e-4
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Experimental setup
+- **Ubuntu 22.04.3 LTS**
+- **GPU - NVIDIA A100(40GB)**
+- **Python**: 3.10.12
+- **Pytorch**:2.1.1+cu118
+- **Transformer**:4.37.0.dev0
+## Limitations
+InMD-X consists of a collection of segmented models. The integration of the models has not yet been fully accomplished, resulting in each model being fragmented.
+Due to the absence of benchmarks, the segmented models have not been adequately evaluated. Future research will involve the development of new benchmarks and the integration of models to facilitate an objective evaluation.
+## Non-commercial use
+These models are available exclusively for research purposes and are not intended for commercial use.
+<!-- ## Citation
 **BibTeX:**
+-->
+## INMED DATA
+INMED DATA is developing large language models (LLMs) specifically tailored for medical applications. For more information, please visit our website [TBD].