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<!--Copyright 2023 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
accelerate/docs/source/basic_tutorials/troubleshooting.md/0
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<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
accelerate/docs/source/usage_guides/ipex.md/0
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import argparse import runhouse as rh import torch from nlp_example import training_function from accelerate.utils import PrepareForLaunch, patch_environment def launch_train(*args): num_processes = torch.cuda.device_count() print(f"Device count: {num_processes}") with patch_environment( world_s...
accelerate/examples/multigpu_remote_launcher.py/0
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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
accelerate/src/accelerate/checkpointing.py/0
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# Copyright 2022 The HuggingFace Team and Brian Chao. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless requir...
accelerate/src/accelerate/commands/menu/helpers.py/0
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#!/usr/bin/env python # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unles...
accelerate/src/accelerate/test_utils/examples.py/0
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from .constants import ( MODEL_NAME, OPTIMIZER_NAME, RNG_STATE_NAME, SAFE_MODEL_NAME, SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, SAMPLER_NAME, SCALER_NAME, SCHEDULER_NAME, TORCH_DISTRIBUTED_OPERATION_TYPES, TORCH_LAUNCH_PARAMS, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ) fro...
accelerate/src/accelerate/utils/__init__.py/0
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compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: 'NO' downcast_bf16: 'no' fsdp_config: {} gpu_ids: all machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main megatron_lm_config: {} mixed_precision: 'no' num_machines: 1 num_processes: 1 rdzv_backend: static sa...
accelerate/tests/test_configs/latest.yaml/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LI...
alignment-handbook/scripts/run_dpo.py/0
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# Creating apps
candle/candle-book/src/apps/README.md/0
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# Running a model In order to run an existing model, you will need to download and use existing weights. Most models are already available on https://huggingface.co/ in [`safetensors`](https://github.com/huggingface/safetensors) format. Let's get started by running an old model : `bert-base-uncased`.
candle/candle-book/src/inference/inference.md/0
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use crate::benchmarks::{BenchDevice, BenchDeviceHandler}; use candle_core::{DType, Device, Tensor}; use criterion::{black_box, criterion_group, Criterion, Throughput}; use std::time::Instant; fn run(a: &Tensor, b: &Tensor, c: &Tensor) { a.where_cond(b, c).unwrap(); } const fn create_cond_arr<const N: usize>() -> ...
candle/candle-core/benches/benchmarks/where_cond.rs/0
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use crate::backend::{BackendDevice, BackendStorage}; use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT}; use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType}; use half::{bf16, f16}; use rayon::prelude::*; const USE_IM2COL_CONV1D: bool = true; const USE_IM2COL_CONV2D: bool = true; // TODO: Maybe we...
candle/candle-core/src/cpu_backend.rs/0
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