Fast model execution with CUDA/HIP graph Quantizations:GPTQ,AWQ, INT4, INT8, and FP8. Optimized CUDA kernels, including integration with FlashAttention and FlashInfer. Speculative decoding Chunked prefill Performance benchmark: We include a performance benchmark at the end ofour blog post. It ...
列出所有 Xinference 支持的指定类型的模型: xinference registrations -t LLM 列出所有在运行的模型: xinference list 停止某个正在运行的模型: xinference terminate --model-uid "qwen2" 更多内容参考3.1节 2. Xinference 安装 安装Xinference 用于推理的基础依赖,以及支持用 ggml推理 和 PyTorch推理的依赖。 2....
In many production-level machine learning (ML) applications, inference is not limited to running a forward pass on a single ML model. Instead, a pipeline of ML models often needs to be executed. Take, for example, a conversational AI pipeline that consists of three modules: an a...
ClearML - Model-Serving Orchestration and Repository Solution kubernetesdevopsmachine-learningaideep-learningtritontensorflow-servingmodel-servingservingmlopsserving-pytorch-modelstriton-inference-serverclearmlserving-ml UpdatedJan 13, 2025 Python triton-inference-server/onnxruntime_backend ...
SWIFT可以无缝集成到ModelScope生态系统中,打通数据集读取、模型下载、模型训练、模型推理、模型上传等流程。此外,SWIFT与PEFT完全兼容, 熟悉PEFT的用户可以使用SWIFT能力结合ModelScope的模型进行便捷地训练推理。 作为ModelScope独立自研的开源轻量级tuner ResTuning,该技术在cv、多模态等领域均经过了系列验证,在训练效果和...
model_id: HuggingFace 上模型的 ID model_uri: 表示可从中加载模型的 URI 的字符串,例如“file:///path/to/llama-2-7b”。如果模型 URI 不存在,推理将尝试使用模型 ID 从 HuggingFace 下载模型。 model_file_name_template:ggml 模型需要。用于基于量化定义模型文件名的字符串模板。
Inferencerefers to the process of using a trained model to make predictions on new, unseen data. The process involves applying the learned parameters of the model to new inputs in order to predict the corresponding outputs. Learningrefers to the process of updating the parameters of a model bas...
is","The president of the United States is","The capital of France is","The future of AI is",]sampling_params=SamplingParams(temperature=0.8,top_p=0.95)llm=LLM(model="qwen/Qwen-1_8B",trust_remote_code=True)outputs=llm.generate(prompts, sampling_params)#Print the outputs.foroutputin...
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json name: blue endpoint_name: my-endpoint model: path: ../../model-1/model/ code_configuration: code: ../../model-1/onlinescoring/ scoring_script: score.py environment: conda_file: ../../model-1/enviro...
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json name: blue endpoint_name: my-endpoint model: path: ../../model-1/model/ code_configuration: code: ../../model-1/onlinescoring/ scoring_script: score.py environment: conda_file: ../../model-1/enviro...