return model, tokenizer models = load_model_and_tokenizer('D:\AI\glm-4\model\glm-9b-chat')# input('Enter model path: ')) model = models[0] # print(model.device) # print(type(model.device)) tokenizer = models[1] ''' AutoTokenizer.from_pretrained( MODEL_PATH, trust_remote_code=Tru...
import os from transformers import AutoModel, AutoTokenizer # 设置模型路径 MODEL_PATH = os.environ.get('MODEL_PATH', '/path/to/your/glm-4-model') # 加载模型和分词器 model = AutoModel.from_pretrained(MODEL_PATH) tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) # 示例输入 input_text ...
DashInfer格式模型:modelscope.cn/models/da python依赖: pip install modelscope dashinfer jinja2 tabulate torch transformers 推理代码: import copy import random from modelscope import snapshot_download from dashinfer.helper import EngineHelper, ConfigManager model_path = snapshot_download("dash-infer/glm...
c) 模型会下载到 ~/.cache/modelscope/hub/ZhipuAI/glm-4-9b-chat 3. 命令行交互方式运行 a) 修改basic_demo/trans_cli_demo.py 中的 MODEL_PATH 由MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/glm-4-9b-chat') 改为MODEL_PATH = '~/.cache/modelscope/hub/ZhipuAI/glm-4-9b-chat' b...
model_path, device_map="auto", trust_remote_code=True, quantization_config=nf4_config, ) tok = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)@app.post("/chat/completions")asyncdefgenerate_response(query: Query): iids = tok.apply_chat_template( ...
create( model="glm-4-long", messages=[ {"role": "user", "content": message_content} ], ) print(response.choices[0].message) 多文件问答示例 代码语言:python 代码运行次数:0 运行 AI代码解释 from zhipuai import ZhipuAI from pathlib import Path import json # 填写您自己的APIKey client = ...
pip install modelscope dashinfer jinja2 tabulate torch transformers 推理代码: import copy import random from modelscope import snapshot_download from dashinfer.helper import EngineHelper, ConfigManager model_path = snapshot_download("dash-infer/glm-4-9b-chat-DI") config_file = model_path + "/"...
model_name_or_path: xxx # 当前仅支持本地加载,填写GLM-4-9B-Chat本地权重路径 adapter_name_or_path: saves/glm4_9b_chat/lora/sft/checkpoint-1000/ template: glm4 finetuning_type: lora 通过下面的命令启动推理: llamafactory-cli chat examples/inference/glm4_9b_chat_lora_sft.yaml 训练前推理...
model_name="glm-4", ) 2、加载文档 这里特定领域用户的数据来源于一个示例的ordersample.csv文件,这个文件可以从我的github上获取:https://github.com/taoxibj/docs/blob/main/ordersample.csv 文件具体内容如下: 把orersample.csv下载到jupyter notebook当前ipynb文件目录,使用CSV文档加载器,加载文档内容: ...
模型部署(代码方法) 部署过程中还有很多抱错,可能还是我的方法不对 部署基础模型: python model_server.py --model-path glm-4-voice-9b 部署Tokenizer和Decoder模型及web界面: python web_demo.py 部署过程有些包还是会有问题,我看也有人提了。多模态要走的路还很长 发布于 2024-10-27 22:52・上海 ...