可以通过点击代码,选择 View -> Quick Documentation (也可以点击快捷键,但我的MacPro M1没有f1键)快速查看函数的注释内容。 显示效果如下: 二、函数的参数 2.1 参数传递 """ @author GroupiesM @date 2022/6/29 17:32 @introduction 形参:定义函数时,定义的参数变量 实参:调用函数时,传递的...
id = "meta-llama/Llama-3.2-11B-Vision-Instruct"model = MllamaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device="cuda",)processor = AutoProcessor.from_pretrained(model_id)url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve...
pip install llama-cpp-python[server] python3 -m llama_cpp.server --model models/7B/ggml-model.bin Navigate to http://localhost:8000/docs to see the OpenAPI documentation. Docker image A Docker image is available on GHCR. To run the server: docker run --rm -it -p 8000:8000 -v /pa...
Wolfram Alpha API:https://products.wolframalpha.com/llm-api/documentation 内置工具使用 Python 语法。输出 Python 代码以进行函数调用的功能是代码解释器工具的一部分,必须使用Environment关键字在系统提示符中启用该功能,如下所示。 {% if Messages %} # system prompt {% if System or Tools %} <|start_hea...
model_id="meta-llama/Llama-3.2-11B-Vision-Instruct"model=MllamaForConditionalGeneration.from_pretrained(model_id,torch_dtype=torch.bfloat16,device="cuda",)processor=AutoProcessor.from_pretrained(model_id)url="https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b926...
See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF 切换环境为7xV100-32G, 再次运行, 成功执行 ... ppl: 2.3968958854675293 Beginning of Epoch 4/4, Total Micro Batches 57 [2023-10-02 21:43:03,280] [INFO] [logging.py:96:log_dist] [Rank 0] step=180, skipped=7, lr=[...
For more information on working with a Modelfile, see theModelfiledocumentation. ollama createis used to create a model from a Modelfile. ollama create mymodel -f ./Modelfile Pull a model ollama pull llama3.2 This command can also be used to update a local model. Only the diff will ...
LlamaIndex provides a lot of detailed examples for GenAI application development in theirblogsanddocumentation. The Neo4j integration covers both the vector store as well as query generation from natural language and knowledge graph construction.
首先,onnx.load("super_resolution.onnx")将加载保存的模型并输出一个onnx.ModelProto结构(用于绑定ML模型的顶层文件/容器格式,更多信息参考onnx.proto documentation文档)。 然后,onnx_checker .check_model(onnx_model)将验证模型的结构,并确认模型有一个有效的模式。
例如，让 Llama 2 对使用 PyTorch 的利弊问题创建更有针对性的技术回答：complete_and_print ("Explain the pros and cons of using PyTorch.")# More likely to explain the pros and cons of PyTorch covers general areas like documentation, the PyTorch community, and mentions a steep learning curve...