Overview

SNOWTEAM/medico-mistral is a specialized language model designed for medical applications. This transformer-based decoder-only language model is based on the Mistral 8x7B model and has been fine-tuned through global parameter adjustments, leveraging a comprehensive dataset that includes 4.8 million research papers and 10,000 medical books.

Model Description

Base Model: Mistral 8x7B model- Instruct
Model type: Transformer-based decoder-only language model
Language(s) (NLP): English

Training Dataset

Dataset Size: 4.8 million research papers and 10,000 medical books.
Data Diversity: Includes a wide range of medical fields, ensuring comprehensive coverage of medical knowledge.
Preprocessing:
Books: We collected 10,000 textbooks from various sources such as the open-library, university libraries, and reputable publishers, covering a wide range of medical specialties. For preprocessing, we extracted text content from PDF files, then performed data cleaning through de-duplication and content filtering. This involved removing extraneous elements such as URLs, author lists, superfluous information, document contents, references, and citations.
Papers: Academic papers are a valuable knowledge resource due to their high-quality, cutting-edge medical information. We started with the S2ORC (Lo et al. 2020) dataset, which contains 81.1 million English-language academic papers. From this, we selected biomedical-related papers based on the presence of corresponding PubMed Central (PMC) IDs. This resulted in approximately 4.8 million biomedical papers, totaling over 75 billion tokens.

Model Sources [optional]

Repository: https://huggingface.co/SNOWTEAM/medico-mistral
Paper [optional]:
Demo [optional]:

How to Get Started with the Model

import transformers
import torch

model_path = "SNOWTEAM/medico-mistral"
model = AutoModelForCausalLM.from_pretrained(
    model_path,device_map="auto", 
    max_memory=max_memory_mapping,
    torch_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained("SNOWTEAM/medico-mistral")
input_text = ""
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids=input_ids.cuda(),
                            max_new_tokens=300,
                            pad_token_id=tokenizer.eos_token_id,)
output_text = tokenizer.batch_decode(output_ids[:, input_ids.shape[1]:],skip_special_tokens=True)[0]
print(output_text)

Training Details

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Citation [optional]

BibTeX:

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APA:

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Model Card Authors [optional]

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Model Card Contact

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