import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True)
query = "你好"
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("THUDM/glm-4-9b-chat",torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,trust_remote_code=True).to(device).eval()
gen_kwargs = {"max_length": 2500, "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))

from transformers import AutoTokenizerfrom vllm import LLM, SamplingParams
# GLM-4-9B-Chat-1M# max_model_len, tp_size = 1048576, 4# 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_sizemax_model_len, tp_size = 131072, 1model_name = "THUDM/glm-4-9b-chat"prompt = [{"role": "user", "content": "你好"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)llm = LLM(model=model_name,tensor_parallel_size=tp_size,max_model_len=max_model_len,trust_remote_code=True,enforce_eager=True,# GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数# enable_chunked_prefill=True,# max_num_batched_tokens=8192)stop_token_ids = [151329, 151336, 151338]sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)
inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)

import torchfrom PIL import Imagefrom transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True)
query = '描述这张图片'image = Image.open("your image").convert('RGB')inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True)# chat mode
inputs = inputs.to(device)model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4v-9b",torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,trust_remote_code=True).to(device).eval()
gen_kwargs = {"max_length": 2500, "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]))