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---
license: mit
datasets:
- yueqingyou/BioQwen
language:
- en
- zh
tags:
- BioQwen
- 1.8B
- Biomedical
- Multi-Tasks
---
# BioQwen: A Small-Parameter, High-Performance Bilingual Model for Biomedical Multi-Tasks
For model inference, please refer to the following example code:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
transformers.logging.set_verbosity_error()
max_length = 512
model_path = 'yueqingyou/BioQwen-1.8B'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto', torch_dtype=torch.bfloat16, attn_implementation='flash_attention_2').eval()
def predict(prompt):
zh_system = "你是千问生物智能助手，一个专注于生物领域的先进人工智能。"
en_system = "You are BioQwen, an advanced AI specializing in the field of biology."
english_count, chinese_count = 0, 0
for char in prompt:
if '\u4e00' <= char <= '\u9fff':
chinese_count += 1
elif 'a' <= char.lower() <= 'z':
english_count += 1
lang = 'zh' if chinese_count > english_count else 'en'
messages = [
{"role": "system", "content": zh_system if lang == 'zh' else en_system},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
with torch.no_grad():
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=max_length,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
top_p = 0.9,
temperature = 0.3,
repetition_penalty = 1.1
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response.strip()
prompt = 'I am suffering from irregular periods. I am currently taking medication Levothyroxine 50. My T3 is 0.87 ng/mL, T4 is 8.30 ug/dL, TSH is 2.43 uIU/mL. I am 34 years old, weigh 75 kg, and 5 feet tall. Please advice.'
print(f'Question:\t{prompt}\n\nAnswer:\t{predict(prompt)}')
```
For more detailed information and code, please refer to [GitHub](https://github.com/yueqingyou/BioQwen). |