Writer Joey Haymaker Designer Michael Lanning Introduction 6 Prompt engineering 7 LLM output configuration 8 Output length 8 Sampling controls 9 Temperature 9 Top-K and top-P 10 Putting it all together AI or by using the API, because by prompting the model directly you will have access to the configuration such as temperature etc. This whitepaper discusses prompt engineering in detail. We will look configurations of a LLM. LLM output configuration Once you choose your model you will need to figure out the model configuration. Most LLMs come with various configuration options that control the LLM’s output

vectors and the intermediate hidden states of routed experts) to ensure stable training. Under this configuration, DeepSeek-V2 comprises 236B total parameters, of which 21B are activated for each token. Training expert is 1408. Among the routed experts, 6 experts will be activated for each token. Under this configuration, DeepSeek-V2-Lite comprises 15.7B total parameters, of which 2.4B are activated for each token

kinds of hardware platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment

vta_config.json Step 9: Go to vta/hardware/intel and run make command Step 10: Get the generated .sof file programmed into hardware Step 11: Evaluate the unit test script test_vta_insn.py with python3. Hooray

to access the FPGA runtime APIs© Copyright 2018 Xilinx Registering TVM op in Python at runtime File contrib_xlnx.py: … @tvm.register_func("tvm.accel.accel_fused") def accel_fused(graph_path, output_layout

@function_tool save_results(output): db.insert({ : output, : datetime.time()}) return "File saved" search_agent = Agent( name= , instructions= tools=[WebSearchTool(),save_results]