Botje
2020-01-31 18:24
从这里的答案开始，您可以使用等效于JNI的
Charset.forName("UTF-8").decode(bb).toString()
如下所示，其中每个段落大致实现一个步骤，最后一个将您的对象字段设置为结果：
jobject bb = env->NewDirectByteBuffer((void *) cStringValue, strlen(cStringValue));
jclass cls_Charset = env->FindClass("java/nio/charset/Charset");
jmethodID mid_Charset_forName = env->GetStaticMethodID(cls_Charset, "forName", "(Ljava/lang/String;)Ljava/nio/charset/Charset;");
jobject charset = env->CallStaticObjectMethod(cls_Charset, mid_Charset_forName, env->NewStringUTF("UTF-8"));
jmethodID mid_Charset_decode = env->GetMethodID(cls_Charset, "decode", "(Ljava/nio/ByteBuffer;)Ljava/nio/CharBuffer;");
jobject cb = env->CallObjectMethod(charset, mid_Charset_decode, bb);
jclass cls_CharBuffer = env->FindClass("java/nio/CharBuffer");
jmethodID mid_CharBuffer_toString = env->GetMethodID(cls_CharBuffer, "toString", "()Ljava/lang/String;");
jstring str = env->CallObjectMethod(cb, mid_CharBuffer_toString);
env->SetObjectField(jPosRec, myJniPosRec->_myJavaStringValue, str);
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Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)
谢谢。完美运作。