对话模型
基座模型
长上下文
在上下文长度为1M的情况下进行大海捞针实验，结果如下：
在LongBench-Chat上进一步评估长文本能力，结果如下：
多语言
GLM-4-9B-Chat 和 Llama-3-8B-Instruct 的测试在六个多语言数据集上进行。测试结果以及每个数据集选择的对应语言如下表所示：
函数调用
多模态
GLM-4V-9B是一种具有视觉理解能力的多模态语言模型。其相关经典任务的评测结果如下：
使用 transformers 后端进行推理:
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerdevice = "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))
使用 vLLM 后端进行推理:
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)
快速调用 GLM-4V-9B 多模态模型：
import torchfrom PIL import Imagefrom transformers import AutoModelForCausalLM, AutoTokenizerdevice = "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 modeinputs = 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]))
