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# 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

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- **Base Model:** [Mistral 8x7B model- Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
- **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]

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

## How to Get Started with the Model
```python
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] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation


### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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[More Information Needed]

#### Metrics

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[More Information Needed]

### Results

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#### Summary


## 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:**

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**APA:**

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## Model Card Authors [optional]

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## Model Card Contact

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