quantize_model 功能说明 训练后量化接口,根据用户设置的量化配置文件对图结构进行量化处理,该函数在config_file指定的层插入权重量化层,完成权重量化,并插入数据量化层,将修改后的网络存为新的模型文件。 函数原型 quantize_model(graph, modified_model_file, modifi
quantize_model 功能说明 训练后量化接口,根据用户设置的量化配置文件对网络结构进行改图处理,插入权重量化和数据量化等相关算子,然后返回修改后的网络。 函数原型 network = quantize_model(config_file, network, *input_data) 参数说明 参数名 输入/返回值 含义 使用
运行代码,检查是否仍然出现错误: 在进行上述更改后,重新运行你的代码,并检查是否还会出现“runtimeerror: gpu is required to quantize or run quantize model”的错误。 如果以上步骤都无法解决问题,建议检查具体的量化策略或查阅相关框架的文档,看是否有特别的GPU使用要求或限制。
net = cv.dnn.readNetFromONNX(args.model) cv2.error: OpenCV(5.0.0-pre) opencv/modules/dnn/src/onnx/onnx_importer.cpp:1070: error: (-2:Unspecified error) in function 'handleNode' > Node [DequantizeLinear@ai.onnx]:(onnx_node!up_block_6.features.6.weight_quantized_node) parse error:...
net = cv.dnn.readNetFromONNX(args.model) cv2.error: OpenCV(5.0.0-pre) opencv/modules/dnn/src/onnx/onnx_importer.cpp:1070: error: (-2:Unspecified error) in function 'handleNode' > Node [DequantizeLinear@ai.onnx]:(onnx_node!up_block_6.features.6.weight_quantized_node) parse error:...
model quantize,:The model quantize component provides mainstream model quantization algorithms for you to compress and accelerate models. This way, high-performance inference can be implemented. This topic describes ...
which are loaded and run on a GPU. However, you can now offload some layers of your LLM to the GPU with llama.cpp. To give you an example, there are 35 layers for a 7b parameter model. This drastically speeds up inference and allows you to run LLMs that don’t fit in your VRAM...
3)We are facing hardware divergence while trying to deploy quantized model to different hardwares. 因此作者提出一个新的量化框架,将硬件描述和学习算法整合到一个循环中: 新的量化框架 1)由模型和硬件决定量化bits和Topology: Given the model and a description for the target hardware, the system will ge...
python -m mlc_llm convert path_to_model --quantization q4f16_1 -o path_to_output 从model_path推断出weight format(huggingface-torch, huggingface-safetensor, awq) 从model_path/config.json推断出model的类型(llama、qwen,...), 返回定义好的Model类型,这个Model类型定义好了每个模型的定义LlamaForCasual...
which is to directly derive a compressed model from the original one by applying either pruning masks or quantization functions. The resulting model can be fine-tuned with a few iterations to recover the accuracy to some extent. Alternatively, the compressed model can be re-trained with the full...