$ git clone https://github.com/gan/glm4v-assistant.git $ cd glm4v-assistant $ git clone https://github.com/2noise/ChatTTS.git $ conda create -n glm-asnt python=3.10 $ conda activate glm-asnt $ conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing $ pip insta...
$ git clone https://github.com/gan/glm4v-assistant.git $ cd glm4v-assistant $ git clone https://github.com/2noise/ChatTTS.git $ conda create -n glm-asnt python=3.10 $ conda activate glm-asnt $ conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing $ pip insta...
Sample GLM4V + ChatTTS AI assistant. Contribute to khcloud-AI/glm4v-assistant development by creating an account on GitHub.
+ 自行构建服务端,并使用 `OpenAI API` 的请求格式与 GLM-4-9B-Chat 模型进行对话。本 demo 支持 Function Call 和 All Tools功能。 + 自行构建服务端,并使用 `OpenAI API` 的请求格式与 GLM-4-9B-Chat GLM-4v-9B 或者模型进行对话。本 demo 支持 Function Call 和 All Tools功能。 + 修改`open_ap...
vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for the actual cause. [rank0]: Traceback (most recent call last): [rank0]: File "/root/ljm/ChatGLM4/GLM-4/api_server_vLLM/v...
model.apply(split_mlp) elif isinstance(model.config.eos_token_id, list): from ipex_llm.transformers.models.chatglm2 import split_mlp # glm4 family if hasattr(model.transformer, "vision"): if model.config.num_layers != 40: from ipex_llm.transformers.models.chatglm4v import merge_qkv model...
希望能够基于GLM4V构建一个General Agent,不仅仅需要其多模态能力,还需要其能够执行一些纯文本的任务,所以需要用纯文本的对话数据进行微调。 { "messages": [ { "role": "user", "content": "You are an intelligent agent that can help user generate a plan to finish some task. The task is: Search ...
$ git clone https://github.com/gan/glm4v-assistant.git $ cd glm4v-assistant $ git clone https://github.com/2noise/ChatTTS.git $ conda create -n glm-asnt python=3.10 $ conda activate glm-asnt $ conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing $ pip install...
MODEL_PATH='<path>' model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, low_cpu_mem_usage=True, trust_remote_code=True, torch_dtype=torch.float16, device_map="auto" ) 报错大概是这个错误栈,transformers的错误栈:File "/home/lichengjie/workspace/inference/xinference/model/llm/pytorch/gl...
模型是glm4-v-9b,显卡是3090和4090 启动命令: xinference launch --model-engine Transformers --model-name glm-4v --size-in-billions 9 --model-format pytorch --quantization none 问题描述: xinference刚刚升级到0.12.2版本后,3090和4090同时出现OOM(单机单卡)