File size: 1,442 Bytes
935df2c a3bb362 2fc7a45 a3bb362 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
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
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 = """
How to treat severe obesity?
"""
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
|