# Step 4: send the info for each function call and function response to the model for tool_call in tool_calls: function_name = tool_call.function.name function_to_call = available_functions[function_name] funct
# Note: set parallel_function_calls=True to enable parallel function calling parallel_function_calls=True, # Default: False # Note: set function_choice='auto' to let the model decide whether to call a function or not # function_choice='auto', # 'auto' is the default if function_choice ...
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls. ...
Large Language Models (LLMs) have shown remarkable results on various complex reasoning benchmarks. The reasoning capabilities of LLMs enable them to execute function calls, using user-provided functions to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skills, or lac...
// Calls the provided work function and returns the number of milliseconds// that it takes to call that function.template<classFunction> __int64time_call(Function&&f) {__int64 begin = GetTickCount(); f();returnGetTickCount() - begin; }// Creates a square matrix with the given number of...
The bitonic_sort function divides the sequence into two partitions, sorts those partitions in opposite directions, and then merges the results. This function calls itself two times recursively to sort each partition.c++ Copy const bool INCREASING = true; const bool DECREASING = false; // ...
不过,貌似跟server的配置还有oracle的配置有关,增大到一定值,效果就不明显了。所以,一般用8,10,12,16的比较常见。我试过用30,发现和16的效果一样。不过,数值越大,占用的资源也会相对增大的。如果是在一些package、function or procedure中写的话,还是不要写那么大,免得占用太多资源被DBA开K。
When these conditions hold,surrogateoptcomputes the objective function of the particles in a population in parallel. Note Even when running in parallel,surrogateoptoccasionally calls the objective function serially on the host machine. Therefore, ensure that your objective function has no assumptions abou...
Creates a local compute context object that uses the doParallel back-end for HPC computations performed using rxExec. This compute context can be used only to distribute computations via the rxExec function; it is ignored by Revolution HPA functions. This is the main generator for S4 class RxLo...
The function delegate that returns the initial state of the local data for each task. body Func<Int32,ParallelLoopState,TLocal,TLocal> The delegate that is invoked once per iteration. localFinally Action<TLocal> The delegate that performs a final action on the local state of each task. ...