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...
a team of researchers from UC Berkeley, ICSI, and LBNL have developed LLMCompiler, a framework designed to enhance the efficiency and accuracy of LLMs in such tasks. LLMCompiler enables parallel execution of function calls through its components: ...
for responses in llm.chat( messages=messages, functions=functions, stream=True, extra_generate_cfg=dict( # 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 whethe...
This development consists of checking the value of activating theparallelToolCallsoption of theOpenAiStreamingChatModelclass. It could be useful when the user is asking the following question: Add a dog for Betty Davis. His name is Moopsie. His birthday is on 2 October 2024. Before calling the...
llm_with_tools.invoke("Please call the first tool two times").tool_calls [{'name': 'add', 'args': {'a': 2, 'b': 2}, 'id': 'call_Hh4JOTCDM85Sm9Pr84VKrWu5'}] As we can see, even though we explicitly told the model to call a tool twice, by disabling parallel tool calls...
The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skills, or lack of access to private data. This development has allowed LLMs to select and coordinate multiple functions ...
(SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can a...
. Instead of producing answerssequentially(left), SoT (right) produces different parts of answersin parallel. Given the question, SoT first prompts the LLM to give a skeleton of the answer and then conducts batched decoding or parallel API calls to simultaneously expand multiple ...
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You also have to change your malloc/new and free/delete calls to cudaMallocManaged and cudaFree so that you are allocating space on the GPU. Finally, you need to wait for a GPU calculation to complete before using the results on the CPU, which you can accomplish with cudaDeviceSynchronize...