48 lines
1.5 KiB
Markdown
48 lines
1.5 KiB
Markdown
---
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license: apache-2.0
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language:
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- zh
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base_model:
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- THUDM/glm-4-9b
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pipeline_tag: text-generation
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---
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# AnesGLM is a large language model designed for anesthesiology question answering tasks in Chinese.
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We develop AnesGLM, a Chinese large language model specialized for anesthesiology knowledge understanding and question answering. It is built upon THUDM/glm-4-9b and further adapted with domain-specific data from anesthesiology question answering and examination-style tasks. The model is designed to provide more accurate and professional responses for clinical anesthesiology education and knowledge-intensive QA scenarios.
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## How to use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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tokenizer = AutoTokenizer.from_pretrained("QiHongzhi/AnesGLM", trust_remote_code=True)
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query = "什么是肺泡最小有效浓度（MAC）？"
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inputs = tokenizer.apply_chat_template(
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[{"role": "user", "content": query}],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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return_dict=True
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)
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inputs = inputs.to(device)
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model = AutoModelForCausalLM.from_pretrained(
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"QiHongzhi/AnesGLM",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True
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).to(device).eval()
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gen_kwargs = {"max_length": 512, "do_sample": True, "top_k": 1}
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with torch.no_grad():
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outputs = model.generate(**inputs, **gen_kwargs)
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outputs = outputs[:, inputs["input_ids"].shape[1]:]
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |