---
license: mit
datasets:
- AGBonnet/augmented-clinical-notes
language:
- en
base_model:
- BioMistral/BioMistral-7B
pipeline_tag: text-generation
tags:
- clinical
- biology
---
# Model Card for Model ID

## How to use

Loading the model from Hunggingface:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
model = AutoModelForCausalLM.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
```
Lightweight model loading can be used - using 4-bit quantization!
```python
!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git

from transformers import  AutoTokenizer, BitsAndBytesConfig, AutoModelForCausalLM
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
model = AutoModelForCausalLM.from_pretrained("ZiweiChen/BioMistral-Clinical-7B", quantization_config=bnb_config)

```
How to Generate text:
```python
model_device = next(model.parameters()).device

prompt = """
### Question:

How to treat severe obesity?

### Answer:
"""
model_input = tokenizer(prompt, return_tensors="pt").to(model_device)

with torch.no_grad():
    output = model.generate(**model_input, max_new_tokens=100)
    answer = tokenizer.decode(output[0], skip_special_tokens=True)
    print(answer)
```
## Model Details

### Model Description