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Update tuning scripts and add task creation to sync_vikunja.js

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Variet-Worker
2026-04-06 21:49:56 +09:00
parent 626a089b6b
commit 7c7a899fd5
61 changed files with 8705 additions and 1566 deletions
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scripts/help_gpu_flags.txt Normal file
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ggml_cuda_init: found 2 CUDA devices (Total VRAM: 24575 MiB):
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes, VRAM: 12287 MiB
Device 1: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes, VRAM: 12287 MiB
-dev, --device <dev1,dev2,..> comma-separated list of devices to use for offloading (none = don't
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE)
--list-devices print list of available devices and exit
-ot, --override-tensor <tensor name pattern>=<buffer type>,...
override tensor buffer type
(env: LLAMA_ARG_OVERRIDE_TENSOR)
-cmoe, --cpu-moe keep all Mixture of Experts (MoE) weights in the CPU
-ncmoe, --n-cpu-moe N keep the Mixture of Experts (MoE) weights of the first N layers in the
-sm, --split-mode {none,layer,row} how to split the model across multiple GPUs, one of:
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE)
-ts, --tensor-split N0,N1,N2,... fraction of the model to offload to each GPU, comma-separated list of
(env: LLAMA_ARG_TENSOR_SPLIT)
-mg, --main-gpu INDEX the GPU to use for the model (with split-mode = none), or for
intermediate results and KV (with split-mode = row) (default: 0)
-fit, --fit [on|off] whether to adjust unset arguments to fit in device memory ('on' or
target margin per device for --fit, comma-separated list of values,
single value is broadcast across all devices, default: 1024
--check-tensors check model tensor data for invalid values (default: false)
--op-offload, --no-op-offload whether to offload host tensor operations to device (default: true)
-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...
override tensor buffer type for draft model
-cmoed, --cpu-moe-draft keep all Mixture of Experts (MoE) weights in the CPU for the draft
-ncmoed, --n-cpu-moe-draft N keep the Mixture of Experts (MoE) weights of the first N layers in the
-devd, --device-draft <dev1,dev2,..> comma-separated list of devices to use for offloading the draft model
use --list-devices to see a list of available devices