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  1. README_zh.md +33 -1
README_zh.md CHANGED
@@ -41,7 +41,7 @@ GLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- MODEL_PATH = 'ZhipuAI/glm-4-9b-chat-1m-hf'
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  tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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  model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
@@ -75,6 +75,38 @@ out = model.generate(**generate_kwargs)
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  print(tokenizer.decode(out[0][input_len:], skip_special_tokens=True))
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  ```
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  ## 协议
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  GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ MODEL_PATH = 'THUDM/glm-4-9b-chat-1m-hf'
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  tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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  model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
 
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  print(tokenizer.decode(out[0][input_len:], skip_special_tokens=True))
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  ```
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+ ### vLLM 推理代码 (需要版本号大于等于0.6.4)
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+ ```Python
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+ from transformers import AutoTokenizer
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+ from vllm import LLM, SamplingParams
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+
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+ # THUDM/glm-4-9b-chat-1m-hf
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+ # max_model_len, tp_size = 1048576, 4
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+ # 如果遇见 OOM 现象,建议减少max_model_len,或者增加tp_size
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+ max_model_len, tp_size = 131072, 1
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+ model_name = "THUDM/glm-4-9b-chat-1m-hf"
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+ prompt = [{"role": "user", "content": "你好"}]
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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+ llm = LLM(
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+ model=model_name,
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+ tensor_parallel_size=tp_size,
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+ max_model_len=max_model_len,
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+ trust_remote_code=True,
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+ enforce_eager=True,
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+ # GLM-4-9B-Chat-1M 如果遇见 OOM 现象,建议开启下述参数
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+ # enable_chunked_prefill=True,
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+ # max_num_batched_tokens=8192
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+ )
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+ stop_token_ids = [151329, 151336, 151338]
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+ sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)
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+
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+ inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
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+ outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)
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+
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+ print(outputs[0].outputs[0].text)
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+ ```
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+
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  ## 协议
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  GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。