388 + set(_configure_date "Unknown") 389 + endif() 390 + include_directories(${CMAKE_BINARY_DIR}) 391 + configure_file(extra/version_string.ver.in version_string.ver @ONLY) aten/src/ATen/cpu/tbb/extra/version_string.ver.inCopy file name to clipboard +11 Original file line ...
this package provides a drop-in `python3.10-*` package with this small patch on top of upstream: diff --git a/Lib/sysconfig.py b/Lib/sysconfig.py index a218726..f44f97c 100644 --- a/Lib/sysconfig.py +++ b/Lib/sysconfig.py @@ -81,6 +81,7 @@ _INSTALL_SCHEMES = { 'data': ...
Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon Jun 19 18:14:09 2023 +0300 cuda : faster k-quants on older GPUs (#1930) * k_quants: hopefully much faster Q4_K on older GPUs On the GTX-1660 that I have available to represent "old GPUs", token prediction drops fro...
id2label.get(i, "Unknown") for i in range(self.output_shape[0])] 26 26 27 27 def __call__(self, strings): 28 - assert not isinstance( 29 - strings, str 30 - ), "shap.models.TransformersPipeline expects a list of strings not a single string!" 28 + assert not ...
unk_id=1, # Unknown 的 ID bos_id=2, # Begin of Sentence 的 ID eos_id=3, # End of Sentence 的 ID user_defined_symbols=["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] # 自定义符号 )106 changes: 106 additions & 0 deletions 106 pretrain/accuracy.py Original file line ...
_errorformat("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); + LLAMA_LOG_WARN("%s: missingor unrecognized pre-tokenizer type, using: 'default'\n", __func__); 0 comments on commit 15220 Please sign in to comment Footer © 2025 GitHub...
CUDA official sample codes. Contribute to zchee/cuda-sample development by creating an account on GitHub.
cuda() return model, tokenizer def load_via_deepspeed(model_name): from transformers.deepspeed import HfDeepSpeedConfig config = AutoConfig.from_pretrained(model_name) world_size = int(os.getenv('WORLD_SIZE', '1')) dtype = config.torch_dtype # torch.bfloat16 if model_name in ["big...
echo "Unknown model: $m" exit 1 esac MODEL_PATH="CodeLlama-$m" echo "Downloading ${MODEL_PATH}" mkdir -p ${TARGET_FOLDER}"/${MODEL_PATH}" for s in $(seq -f "0%g" 0 ${SHARD}) do wget ${PRESIGNED_URL/'*'/"${MODEL_PATH}/consolidated.${s}.pth"} -O ${TARGET_FOLDER}"...
device = 'cuda' if torch.cuda.is_available() else 'cpu' best_acc = 0 start_epoch = 0 print('==> Preparing data..') if args.dataset=='cifar10_relabel': from data.cifar10_relabel import CIFAR10 as Dataset trainset=Dataset('train',seed=args.seed) knownlist,unknownlist=trainset....