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Model: liyinghong/BioQwen-0.5B Source: Original Platform
2026-08-30 02:55:18 +08:00
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+---
+license: mit
+datasets:
+- yueqingyou/BioQwen
+language:
+- en
+- zh
+tags:
+- BioQwen
+- 0.5B
+- 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-0.5B'
+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).