初始化项目，由ModelHub XC社区提供模型

Model: QiHongzhi/AnesGLM
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- zh
+base_model:
+- THUDM/glm-4-9b
+pipeline_tag: text-generation
+---
+
+# AnesGLM is a large language model designed for anesthesiology question answering tasks in Chinese.
+
+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.
+
+## How to use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"
+
+tokenizer = AutoTokenizer.from_pretrained("QiHongzhi/AnesGLM", trust_remote_code=True)
+
+query = "什么是肺泡最小有效浓度（MAC）？"
+
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": query}],
+    add_generation_prompt=True,
+    tokenize=True,
+    return_tensors="pt",
+    return_dict=True
+)
+
+inputs = inputs.to(device)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "QiHongzhi/AnesGLM",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+).to(device).eval()
+
+gen_kwargs = {"max_length": 512, "do_sample": True, "top_k": 1}
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, **gen_kwargs)
+    outputs = outputs[:, inputs["input_ids"].shape[1]:]
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))