for each function which can result in high latency, cost, and sometimes inaccurate behavior. LLMCompiler addresses this by decomposing problems into multiple tasks that can be executed in parallel, thereby efficiently orchestrating multi-function calling. With LLMCompiler, the user specifies the tools ...
把LLMCompiler 论文读比较有趣,将传统程序编译器思想引入到任务,函数调用规划器、任务调度单元和执行器。介绍如何 LLCompiler 通过调度任务执行顺序,从而达到减少延时和降低成本的目的, 视频播放量 141、弹幕量 0、点赞数 4、投硬币枚数 2、收藏人数 2、转发人数 0, 视
for multi-function calling often require sequential reasoning and acting for each function which can result in high latency, cost, and sometimes inaccurate behavior. To address this, we introduce LLMCompiler, which executes functions in parallel to efficiently orchestrate multi-function calling. Drawing...
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: L...
The realization inspiration comes from An LLM Compiler for Parallel Function Calling. Here is an example of using SQL to query data to illustrate the core role of the framework. The core process of generating an execution plan for SQL includes syntax parsing, semantic analysis, optimizer ...
Here, I’ll present thePlan-and-Executeapproach that fuses the planning module and the agent core. This is an advanced implementation and essentially compiles a plan before execution. For more details, seeAn LLM Compiler for Parallel Function Calling. ...
This is realized using a technique known as parallel composition which is usually performed on-the-fly during the reachability analysis to reduce space complexity. The composed automaton is a directed graph, called the reachability graph, with nodes representing the state of the system and arcs ...
magentic Seamlessly integrate LLMs as Python functions. Use type annotations to specify structured output. Mix LLM queries and function calling with regular Python code to create complex LLM-powered functionality. Mirascope Intuitive convenience tooling for lightning-fast, efficient development and ensuring...
Namespace(backend='vllm', dataset='ShareGPT_V3_unfiltered_cleaned_split.json', input_len=None, output_len=None, model='/data/wjc/Qwen/Qwen2-7B-Instruct/', tokenizer='/data/wjc/Qwen/Qwen2-7B-Instruct/', quantization=None, tensor_parallel_size=2, n=1, use_beam_search=False, num_promp...
2023-10-07, the EasyEdit has added the support for editing models with multiple GPUs, using huggingface Accelerate. 2023-9-21, the EasyEdit has added the support for Parameter-Efficient Fine-Tuning through AdaLoRA to inject knowledge into the LLM. 2023-8-31, the EasyEdit has added the suppor...