text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
|---|---|---|---|
# Copyright 2020 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... | transformers/tests/pipelines/test_pipelines_zero_shot.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_zero_shot.py",
"repo_id": "transformers",
"token_count": 7368
} | 487 |
import gc
import unittest
from transformers import AutoModelForCausalLM
from transformers.testing_utils import require_compressed_tensors, require_torch
from transformers.utils import is_torch_available
if is_torch_available():
import torch
@require_compressed_tensors
@require_torch
class CompressedTensorsTest... | transformers/tests/quantization/compressed_tensor/test_load_sparse_model.py/0 | {
"file_path": "transformers/tests/quantization/compressed_tensor/test_load_sparse_model.py",
"repo_id": "transformers",
"token_count": 1238
} | 488 |
# coding=utf-8
# Copyright 2024 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 requir... | transformers/tests/quantization/quanto_integration/test_quanto.py/0 | {
"file_path": "transformers/tests/quantization/quanto_integration/test_quanto.py",
"repo_id": "transformers",
"token_count": 7774
} | 489 |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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 ag... | transformers/tests/test_modeling_tf_common.py/0 | {
"file_path": "transformers/tests/test_modeling_tf_common.py",
"repo_id": "transformers",
"token_count": 31259
} | 490 |
# Copyright 2024 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... | transformers/tests/trainer/test_trainer_fsdp.py/0 | {
"file_path": "transformers/tests/trainer/test_trainer_fsdp.py",
"repo_id": "transformers",
"token_count": 2775
} | 491 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 a... | transformers/tests/utils/test_cli.py/0 | {
"file_path": "transformers/tests/utils/test_cli.py",
"repo_id": "transformers",
"token_count": 1250
} | 492 |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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 ag... | transformers/tests/utils/test_model_card.py/0 | {
"file_path": "transformers/tests/utils/test_model_card.py",
"repo_id": "transformers",
"token_count": 1562
} | 493 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | transformers/utils/check_config_attributes.py/0 | {
"file_path": "transformers/utils/check_config_attributes.py",
"repo_id": "transformers",
"token_count": 7555
} | 494 |
# coding=utf-8
# Copyright 2022 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/LICENSE-2.0
#
# Unless r... | transformers/utils/create_dummy_models.py/0 | {
"file_path": "transformers/utils/create_dummy_models.py",
"repo_id": "transformers",
"token_count": 29298
} | 495 |
# Copyright 2024 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... | transformers/utils/notification_service_quantization.py/0 | {
"file_path": "transformers/utils/notification_service_quantization.py",
"repo_id": "transformers",
"token_count": 4809
} | 496 |
from transformers import Wav2Vec2FeatureExtractor
class CustomFeatureExtractor(Wav2Vec2FeatureExtractor):
pass
| transformers/utils/test_module/custom_feature_extraction.py/0 | {
"file_path": "transformers/utils/test_module/custom_feature_extraction.py",
"repo_id": "transformers",
"token_count": 37
} | 497 |
# Copyright 2025 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... | trl/examples/datasets/tldr_preference.py/0 | {
"file_path": "trl/examples/datasets/tldr_preference.py",
"repo_id": "trl",
"token_count": 1547
} | 498 |
# Copyright 2025 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... | trl/examples/research_projects/stack_llama_2/scripts/sft_llama2.py/0 | {
"file_path": "trl/examples/research_projects/stack_llama_2/scripts/sft_llama2.py",
"repo_id": "trl",
"token_count": 2891
} | 499 |
# Copyright 2025 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... | trl/examples/scripts/kto.py/0 | {
"file_path": "trl/examples/scripts/kto.py",
"repo_id": "trl",
"token_count": 1461
} | 500 |
# Copyright 2025 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... | trl/scripts/add_copyrights.py/0 | {
"file_path": "trl/scripts/add_copyrights.py",
"repo_id": "trl",
"token_count": 1106
} | 501 |
# Copyright 2025 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... | trl/tests/test_cli.py/0 | {
"file_path": "trl/tests/test_cli.py",
"repo_id": "trl",
"token_count": 1240
} | 502 |
# Copyright 2025 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... | trl/tests/test_modeling_geometric_mixture_wrapper.py/0 | {
"file_path": "trl/tests/test_modeling_geometric_mixture_wrapper.py",
"repo_id": "trl",
"token_count": 1095
} | 503 |
# Copyright 2025 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... | trl/tests/testing_utils.py/0 | {
"file_path": "trl/tests/testing_utils.py",
"repo_id": "trl",
"token_count": 1120
} | 504 |
# Copyright 2025 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... | trl/trl/models/modeling_sd_base.py/0 | {
"file_path": "trl/trl/models/modeling_sd_base.py",
"repo_id": "trl",
"token_count": 17406
} | 505 |
# Copyright 2025 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... | trl/trl/trainer/bco_config.py/0 | {
"file_path": "trl/trl/trainer/bco_config.py",
"repo_id": "trl",
"token_count": 3178
} | 506 |
# Copyright 2025 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... | trl/trl/trainer/kto_trainer.py/0 | {
"file_path": "trl/trl/trainer/kto_trainer.py",
"repo_id": "trl",
"token_count": 34887
} | 507 |
# Copyright 2025 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... | trl/trl/trainer/sft_config.py/0 | {
"file_path": "trl/trl/trainer/sft_config.py",
"repo_id": "trl",
"token_count": 2814
} | 508 |
# 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/benchmarks/big_model_inference/big_model_inference.py/0 | {
"file_path": "accelerate/benchmarks/big_model_inference/big_model_inference.py",
"repo_id": "accelerate",
"token_count": 2241
} | 0 |
# Copyright 2024 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/LICENSE-2.0
#
# Unless required by appl... | accelerate/benchmarks/fp8/transformer_engine/ddp.py/0 | {
"file_path": "accelerate/benchmarks/fp8/transformer_engine/ddp.py",
"repo_id": "accelerate",
"token_count": 2220
} | 1 |
<!--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/basic_tutorials/notebook.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/notebook.md",
"repo_id": "accelerate",
"token_count": 5694
} | 2 |
<!--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/big_modeling.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/big_modeling.md",
"repo_id": "accelerate",
"token_count": 1618
} | 3 |
<!--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/usage_guides/quantization.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/quantization.md",
"repo_id": "accelerate",
"token_count": 1991
} | 4 |
#!/usr/bin/env python
# Copyright 2021 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/LICENSE-2.0
#
# U... | accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py/0 | {
"file_path": "accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py",
"repo_id": "accelerate",
"token_count": 12610
} | 5 |
# Copyright 2021 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/LICENSE-2.0
#
# Unless required by appl... | accelerate/examples/cv_example.py/0 | {
"file_path": "accelerate/examples/cv_example.py",
"repo_id": "accelerate",
"token_count": 3191
} | 6 |
#!/usr/bin/env python
# Copyright 2021 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/commands/config/cluster.py/0 | {
"file_path": "accelerate/src/accelerate/commands/config/cluster.py",
"repo_id": "accelerate",
"token_count": 19323
} | 7 |
#!/usr/bin/env python
# Copyright 2024 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/commands/merge.py/0 | {
"file_path": "accelerate/src/accelerate/commands/merge.py",
"repo_id": "accelerate",
"token_count": 776
} | 8 |
# 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/test_utils/scripts/test_sync.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_sync.py",
"repo_id": "accelerate",
"token_count": 7926
} | 9 |
# 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/utils/modeling.py/0 | {
"file_path": "accelerate/src/accelerate/utils/modeling.py",
"repo_id": "accelerate",
"token_count": 38832
} | 10 |
# Copyright 2024 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/tests/deepspeed/test_deepspeed_multiple_model.py/0 | {
"file_path": "accelerate/tests/deepspeed/test_deepspeed_multiple_model.py",
"repo_id": "accelerate",
"token_count": 3128
} | 11 |
# Copyright 2025 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/tests/test_fp8.py/0 | {
"file_path": "accelerate/tests/test_fp8.py",
"repo_id": "accelerate",
"token_count": 2978
} | 12 |
echo "hello world"
echo "this is a second command" | accelerate/tests/test_samples/test_command_file.sh/0 | {
"file_path": "accelerate/tests/test_samples/test_command_file.sh",
"repo_id": "accelerate",
"token_count": 14
} | 13 |
[
{
"question": "Which of the following best describes a Large Language Model (LLM)?",
"answer_a": "A model specializing in language recognition",
"answer_b": "A massive neural network that understands and generates human language",
"answer_c": "A model exclusively used for language ... | agents-course/quiz/data/unit_1.json/0 | {
"file_path": "agents-course/quiz/data/unit_1.json",
"repo_id": "agents-course",
"token_count": 154
} | 14 |
# Understanding AI Agents through the Thought-Action-Observation Cycle
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-3.jpg" alt="Unit 1 planning"/>
In the previous sections, we learned:
- **How tools are made available to the agent in the system prompt**... | agents-course/units/en/unit1/agent-steps-and-structure.mdx/0 | {
"file_path": "agents-course/units/en/unit1/agent-steps-and-structure.mdx",
"repo_id": "agents-course",
"token_count": 1799
} | 15 |
[book]
authors = ["Nicolas Patry"]
language = "en"
multilingual = false
src = "src"
title = "Candle Documentation"
| candle/candle-book/book.toml/0 | {
"file_path": "candle/candle-book/book.toml",
"repo_id": "candle",
"token_count": 38
} | 16 |
# Advanced Cuda usage
| candle/candle-book/src/inference/cuda/README.md/0 | {
"file_path": "candle/candle-book/src/inference/cuda/README.md",
"repo_id": "candle",
"token_count": 6
} | 17 |
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) {
a.affine(12.34, 56.78).unwrap();
}
fn run_affine_benchmark(c: &mut Criterion, device: &Device, dtype:... | candle/candle-core/benches/benchmarks/affine.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/affine.rs",
"repo_id": "candle",
"token_count": 590
} | 18 |
//! 1D and 2D Convolutions
//!
use crate::{op::BackpropOp, op::Op, Error, Result, Tensor};
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ParamsConv1D {
pub(crate) b_size: usize,
// Maybe we should have a version without l_in as this bit depends on the input and not only on
// the weights.
pub(crate... | candle/candle-core/src/conv.rs/0 | {
"file_path": "candle/candle-core/src/conv.rs",
"repo_id": "candle",
"token_count": 5821
} | 19 |
use crate::backend::BackendDevice;
use crate::cpu_backend::CpuDevice;
use crate::{CpuStorage, DType, Result, Shape, Storage, WithDType};
/// A `DeviceLocation` represents a physical device whereas multiple `Device`
/// can live on the same location (typically for cuda devices).
#[derive(Debug, Copy, Clone, PartialEq, ... | candle/candle-core/src/device.rs/0 | {
"file_path": "candle/candle-core/src/device.rs",
"repo_id": "candle",
"token_count": 6435
} | 20 |
use super::{GgmlDType, QStorage};
use crate::quantized::k_quants::GgmlType;
use crate::{backend::BackendDevice, cuda_backend::WrapErr};
use crate::{CudaDevice, CudaStorage, Result};
use half::f16;
use cudarc::driver::{CudaSlice, CudaView, DeviceSlice};
#[derive(Clone, Debug)]
struct PaddedCudaSlice {
inner: CudaS... | candle/candle-core/src/quantized/cuda.rs/0 | {
"file_path": "candle/candle-core/src/quantized/cuda.rs",
"repo_id": "candle",
"token_count": 14789
} | 21 |
//! StreamTensror useful for streaming ops.
//!
use crate::{Result, Shape, Tensor};
pub trait Dim: crate::shape::Dim + Copy {}
impl<T: crate::shape::Dim + Copy> Dim for T {}
/// A stream tensor is used in streaming module. It can either contain an actual tensor or be
/// empty.
#[derive(Clone)]
pub struct StreamTenso... | candle/candle-core/src/streaming.rs/0 | {
"file_path": "candle/candle-core/src/streaming.rs",
"repo_id": "candle",
"token_count": 3130
} | 22 |
use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let t = Tensor::from_vec(data, (... | candle/candle-core/tests/pool_tests.rs/0 | {
"file_path": "candle/candle-core/tests/pool_tests.rs",
"repo_id": "candle",
"token_count": 2112
} | 23 |
//! Helper functions for the tinystories dataset. This uses the pre-tokenized version as generated
//! by the tools from https://github.com/karpathy/llama2.c
use candle::{Device, Result, Tensor};
pub struct Dataset {
valid_tokens: Vec<memmap2::Mmap>,
train_tokens: Vec<memmap2::Mmap>,
}
fn mmap_file(p: &std::p... | candle/candle-datasets/src/nlp/tinystories.rs/0 | {
"file_path": "candle/candle-datasets/src/nlp/tinystories.rs",
"repo_id": "candle",
"token_count": 2092
} | 24 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::Parser;
use candle::{DType, Device, Result, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::model... | candle/candle-examples/examples/blip/main.rs/0 | {
"file_path": "candle/candle-examples/examples/blip/main.rs",
"repo_id": "candle",
"token_count": 2436
} | 25 |
# candle-endocec
[EnCodec](https://huggingface.co/facebook/encodec_24khz) is a high-quality audio
compression model using an encoder/decoder architecture with residual vector
quantization.
## Running one example
```bash
cargo run --example encodec --features encodec --release -- code-to-audio \
candle-examples/e... | candle/candle-examples/examples/encodec/README.md/0 | {
"file_path": "candle/candle-examples/examples/encodec/README.md",
"repo_id": "candle",
"token_count": 305
} | 26 |
* GLM4
GLM-4-9B is the open-source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI.
- [[https://github.com/THUDM/GLM4][Github]]
- [[https://huggingface.co/THUDM/glm-4-9b][huggingface]]
** Running with ~cuda~
#+begin_src shell
cargo run --example glm4 --release --f... | candle/candle-examples/examples/glm4/README.org/0 | {
"file_path": "candle/candle-examples/examples/glm4/README.org",
"repo_id": "candle",
"token_count": 1034
} | 27 |
use candle::backend::BackendStorage;
use candle::{CpuStorage, CustomOp1, DType, Device, IndexOp, Layout, Result, Shape, Tensor, D};
use candle_nn::var_builder::ShardedVarBuilder as VarBuilder;
use candle_nn::{Embedding, Linear, Module, RmsNorm};
use cudarc::nccl::safe::{Comm, ReduceOp};
use std::rc::Rc;
use std::sync::... | candle/candle-examples/examples/llama_multiprocess/model.rs/0 | {
"file_path": "candle/candle-examples/examples/llama_multiprocess/model.rs",
"repo_id": "candle",
"token_count": 7294
} | 28 |
# candle-mimi
[Mimi](https://huggingface.co/kyutai/mimi) is a state of the art audio
compression model using an encoder/decoder architecture with residual vector
quantization. The candle implementation supports streaming meaning that it's
possible to encode or decode a stream of audio tokens on the flight to provide
l... | candle/candle-examples/examples/mimi/README.md/0 | {
"file_path": "candle/candle-examples/examples/mimi/README.md",
"repo_id": "candle",
"token_count": 228
} | 29 |
# candle-moondream
[Moondream](https://github.com/vikhyat/moondream) is a computer-vision model can answer real-world questions about images. It's tiny by today's models, with only 1.6B parameters. That enables it to run on a variety of devices, including mobile phones and edge devices.
## Running some examples
First... | candle/candle-examples/examples/moondream/README.md/0 | {
"file_path": "candle/candle-examples/examples/moondream/README.md",
"repo_id": "candle",
"token_count": 367
} | 30 |
# candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
[Phi-1.5](https://huggingface.co/microsoft/phi-1_5),
[Phi-2](https://huggingface.co/microsoft/phi-2), and
[Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) are language models using
only 1.3, 2.7, and 3.8 billion paramet... | candle/candle-examples/examples/phi/README.md/0 | {
"file_path": "candle/candle-examples/examples/phi/README.md",
"repo_id": "candle",
"token_count": 1048
} | 31 |
# candle-reinforcement-learning
Reinforcement Learning examples for candle.
This has been tested with `gymnasium` version `0.29.1`. You can install the
Python package with:
```bash
pip install "gymnasium[accept-rom-license]"
```
In order to run the examples, use the following commands. Note the additional
`--package... | candle/candle-examples/examples/reinforcement-learning/README.md/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/README.md",
"repo_id": "candle",
"token_count": 198
} | 32 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use clap::{Parser, ValueEnum};
use candle_transformers::models::quantized_rwkv_v5::Model as Q5;
use candle_transformers::models::quantized_rwkv_v6::Model as Q6;
use candle_transformers:... | candle/candle-examples/examples/rwkv/main.rs/0 | {
"file_path": "candle/candle-examples/examples/rwkv/main.rs",
"repo_id": "candle",
"token_count": 5059
} | 33 |
use symphonia::core::audio::{AudioBufferRef, Signal};
use symphonia::core::codecs::{DecoderOptions, CODEC_TYPE_NULL};
use symphonia::core::conv::FromSample;
fn conv<T>(samples: &mut Vec<f32>, data: std::borrow::Cow<symphonia::core::audio::AudioBuffer<T>>)
where
T: symphonia::core::sample::Sample,
f32: symphoni... | candle/candle-examples/examples/whisper/pcm_decode.rs/0 | {
"file_path": "candle/candle-examples/examples/whisper/pcm_decode.rs",
"repo_id": "candle",
"token_count": 1267
} | 34 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod model;
use model::{Multiples, YoloV8, YoloV8Pose};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use candle_transformers::object_detection::{non_maximum_sup... | candle/candle-examples/examples/yolo-v8/main.rs/0 | {
"file_path": "candle/candle-examples/examples/yolo-v8/main.rs",
"repo_id": "candle",
"token_count": 7410
} | 35 |
#pragma once
#define C10_CUDA_CHECK(EXPR) \
do { \
const cudaError_t __err = EXPR; \
} while (0)
#define C10_CUDA_KERNEL_LAUNCH_CHECK() C10_CUDA_CHECK(cudaGetLastError())
| candle/candle-flash-attn/kernels/error.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/error.h",
"repo_id": "candle",
"token_count": 216
} | 36 |
use core::ffi::{c_int, c_void};
extern "C" {
pub(crate) fn run_mha(
q_ptr: *const c_void,
k_ptr: *const c_void,
v_ptr: *const c_void,
o_ptr: *const c_void,
softmax_lse_ptr: *const c_void,
alibi_slopes_ptr: *const c_void,
cu_seqlens_q_ptr: *const i32,
... | candle/candle-flash-attn/src/ffi.rs/0 | {
"file_path": "candle/candle-flash-attn/src/ffi.rs",
"repo_id": "candle",
"token_count": 702
} | 37 |
// Kernels adapted from llama.cpp ggml-cuda.cu
// https://github.com/ggerganov/llama.cpp/blob/master/ggml-cuda.cu
#include "cuda_fp16.h"
#include "cuda_bf16.h"
#include<stdint.h>
#define GGML_UNUSED(x) (void)(x)
#define GGML_CUDA_ASSUME(x)
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 2... | candle/candle-kernels/src/quantized.cu/0 | {
"file_path": "candle/candle-kernels/src/quantized.cu",
"repo_id": "candle",
"token_count": 85791
} | 38 |
use crate::utils::EncoderProvider;
use crate::{ConstantValues, Kernels, MetalKernelError, Source, Value};
use metal::{Buffer, ComputeCommandEncoderRef, Device, MTLSize, NSUInteger};
use std::ffi::c_void;
#[derive(Copy, Clone, PartialEq, Eq, Hash, Debug)]
pub enum GemmDType {
BF16,
F16,
F32,
}
#[allow(clip... | candle/candle-metal-kernels/src/mlx_gemm.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/mlx_gemm.rs",
"repo_id": "candle",
"token_count": 3374
} | 39 |
use candle_metal_kernels::{call_unary_contiguous, call_unary_strided, unary, Kernels};
use half::{bf16, f16};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
let... | candle/candle-metal-kernels/tmp/unary.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/unary.rs",
"repo_id": "candle",
"token_count": 3489
} | 40 |
//! Group Normalization.
//!
//! This layer applies Group Normalization over a mini-batch of inputs.
use candle::{DType, Result, Tensor};
// This group norm version handles both weight and bias so removes the mean.
#[derive(Clone, Debug)]
pub struct GroupNorm {
weight: Tensor,
bias: Tensor,
eps: f64,
n... | candle/candle-nn/src/group_norm.rs/0 | {
"file_path": "candle/candle-nn/src/group_norm.rs",
"repo_id": "candle",
"token_count": 1372
} | 41 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{Device, Result, Tensor};
#[test]
fn kv_cache() -> Result<()> {
let mut cache = candle_nn::kv_cache::Cache::new(0, 16);
for _ in [0, 1] {
assert_eq!(cache.current_seq_len(), 0);... | candle/candle-nn/tests/kv_cache.rs/0 | {
"file_path": "candle/candle-nn/tests/kv_cache.rs",
"repo_id": "candle",
"token_count": 2202
} | 42 |
[package]
name = "candle-pyo3"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[lib]
name = "candle"
crate-type = ["cdylib"]
[dependencies]
accelerate-src = { ... | candle/candle-pyo3/Cargo.toml/0 | {
"file_path": "candle/candle-pyo3/Cargo.toml",
"repo_id": "candle",
"token_count": 315
} | 43 |
from candle import Tensor, QTensor, DType
from typing import (
Dict,
Tuple,
Any,
Optional,
Union,
Iterator,
Set,
overload,
Mapping,
TypeVar,
List,
)
from collections import OrderedDict, namedtuple
TensorLike = Union[Tensor, QTensor]
T = TypeVar("T", bound="Module")
class _... | candle/candle-pyo3/py_src/candle/nn/module.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/module.py",
"repo_id": "candle",
"token_count": 12028
} | 44 |
import candle
print(f"mkl: {candle.utils.has_mkl()}")
print(f"accelerate: {candle.utils.has_accelerate()}")
print(f"num-threads: {candle.utils.get_num_threads()}")
print(f"cuda: {candle.utils.cuda_is_available()}")
t = candle.Tensor(42.0)
print(t)
print(t.shape, t.rank, t.device)
print(t + t)
t = can... | candle/candle-pyo3/test.py/0 | {
"file_path": "candle/candle-pyo3/test.py",
"repo_id": "candle",
"token_count": 340
} | 45 |
//! BigCode implementation in Rust based on the GPT-BigCode model.
//!
//! [StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
//! model specialized to code generation. The initial model was trained on 80
//! programming languages. See "StarCoder: A State-of-the-Art LLM for Code", Mukherjee et... | candle/candle-transformers/src/models/bigcode.rs/0 | {
"file_path": "candle/candle-transformers/src/models/bigcode.rs",
"repo_id": "candle",
"token_count": 6580
} | 46 |
#![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
use std::{f32::consts::PI, sync::Arc};
use candle::{
shape::Dim, CpuStorage, CustomOp1, DType, Device, Error, IndexOp, Layout, Result, Shape,
Tensor, WithDType, D,
};
use candle_nn::{embedding, rms_norm, Activation, Embedding, Linear, Mo... | candle/candle-transformers/src/models/deepseek2.rs/0 | {
"file_path": "candle/candle-transformers/src/models/deepseek2.rs",
"repo_id": "candle",
"token_count": 18452
} | 47 |
//! Gemma inference implementation.
//!
//! See ["Gemma: Open Models Based on Gemini Technology"](https://blog.google/technology/developers/gemma-open-ai-model/)
//!
//! Based on implementation from Google and PyTorch
use std::sync::Arc;
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{linear_b... | candle/candle-transformers/src/models/gemma.rs/0 | {
"file_path": "candle/candle-transformers/src/models/gemma.rs",
"repo_id": "candle",
"token_count": 7496
} | 48 |
// Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{Module, Result, StreamTensor, StreamingModule, Tensor, D};
use candle_nn::{Conv1d, VarBuilder};
#[allow(clippy::enum_variant_names)]
#[de... | candle/candle-transformers/src/models/mimi/conv.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mimi/conv.rs",
"repo_id": "candle",
"token_count": 11113
} | 49 |
//! # MobileOne
//!
//! MobileOne inference implementation based on timm and candle-repvgg
//!
//! See ["MobileOne: An Improved One millisecond Mobile Backbone"](https://arxiv.org/abs/2206.04040)
use candle::{DType, Result, Tensor, D};
use candle_nn::{
batch_norm, conv2d, conv2d_no_bias, linear, ops::sigmoid, Batc... | candle/candle-transformers/src/models/mobileone.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mobileone.rs",
"repo_id": "candle",
"token_count": 4729
} | 50 |
use candle::{Module, Result, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
use super::vision_model;
use crate::models::mistral;
#[derive(serde::Deserialize, Debug, Clone)]
pub struct Config {
pub projector_hidden_act: candle_nn::Activation,
pub text_config: mistral::Config,
pub vision_config: visi... | candle/candle-transformers/src/models/pixtral/llava.rs/0 | {
"file_path": "candle/candle-transformers/src/models/pixtral/llava.rs",
"repo_id": "candle",
"token_count": 1393
} | 51 |
//! RWKV v5 model implementation with quantization support.
//!
//! RWKV v5 is an attention-free language model optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Linear attention mechanism
//! - GroupNorm layer normalization
//! - ... | candle/candle-transformers/src/models/quantized_rwkv_v5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_rwkv_v5.rs",
"repo_id": "candle",
"token_count": 5686
} | 52 |
use candle::{DType, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::image_encoder::ImageEncoderViT;
use super::mask_decoder::MaskDecoder;
use super::prompt_encoder::PromptEncoder;
use super::tiny_vit::{tiny_vit_5m, TinyViT};
const PROMPT_EMBED_DIM: usize = 256;
pub const IMAGE_SIZE: usize = ... | candle/candle-transformers/src/models/segment_anything/sam.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/sam.rs",
"repo_id": "candle",
"token_count": 8444
} | 53 |
use candle::{Device, Result, Tensor};
pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
if steps == 0 {
Tensor::from_vec(Vec::<f64>::new(), steps, &Device::Cpu)
} else if steps == 1 {
Tensor::from_vec(vec![start], steps, &Device::Cpu)
} else {
let delta = (sto... | candle/candle-transformers/src/models/stable_diffusion/utils.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/utils.rs",
"repo_id": "candle",
"token_count": 971
} | 54 |
use candle::{Result, Tensor};
#[derive(Debug, Clone)]
pub struct DDPMWSchedulerConfig {
scaler: f64,
s: f64,
}
impl Default for DDPMWSchedulerConfig {
fn default() -> Self {
Self {
scaler: 1f64,
s: 0.008f64,
}
}
}
pub struct DDPMWScheduler {
init_alpha_cump... | candle/candle-transformers/src/models/wuerstchen/ddpm.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/ddpm.rs",
"repo_id": "candle",
"token_count": 1537
} | 55 |
## Running BERT with Candle and WASM
Here, we provide two examples of how to run Bert using a Candle-compiled WASM binary and runtime.
### Vanilla JS and WebWorkers
To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
```bash
sh build-lib.sh
```
This will bundle the ... | candle/candle-wasm-examples/bert/README.md/0 | {
"file_path": "candle/candle-wasm-examples/bert/README.md",
"repo_id": "candle",
"token_count": 226
} | 56 |
## Running [llama2.c](https://github.com/karpathy/llama2.c) Examples
Here, we provide two examples of how to run [llama2.c](https://github.com/karpathy/llama2.c) written in Rust using a Candle-compiled WASM binary and runtimes.
### Pure Rust UI
To build and test the UI made in Rust you will need [Trunk](https://trun... | candle/candle-wasm-examples/llama2-c/README.md/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/README.md",
"repo_id": "candle",
"token_count": 449
} | 57 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Moondream Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link
... | candle/candle-wasm-examples/moondream/index.html/0 | {
"file_path": "candle/candle-wasm-examples/moondream/index.html",
"repo_id": "candle",
"token_count": 6120
} | 58 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
struct Embeddings {
original_width: u32,
original_height: u32,
width: u32,
height: u32,
data: Tensor,
}
#[wasm_bindgen]
pub struct Model {
sam: sam::Sam,
embedd... | candle/candle-wasm-examples/segment-anything/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2399
} | 59 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Whisper Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
... | candle/candle-wasm-examples/whisper/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/lib-example.html",
"repo_id": "candle",
"token_count": 6488
} | 60 |
use crate::console_log;
use crate::worker::{ModelData, RunData, Worker, WorkerInput, WorkerOutput};
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
async fn fetch_url(url: &str) -> Result<Vec<u8>, JsValue> {
use web_sys:... | candle/candle-wasm-examples/yolo/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/app.rs",
"repo_id": "candle",
"token_count": 5961
} | 61 |
backend-test:J
xytest"Relu
SingleReluZ
x
b
y
B | candle/test.onnx/0 | {
"file_path": "candle/test.onnx",
"repo_id": "candle",
"token_count": 76
} | 62 |
{
"license": "Apache-2.0",
"creators": [
{
"affiliation": "Hugging Face",
"name": "Quentin Lhoest"
},
{
"orcid": "0000-0003-1727-1045",
"affiliation": "Hugging Face",
"name": "Albert Villanova del Moral"
},
{
... | datasets/.zenodo.json/0 | {
"file_path": "datasets/.zenodo.json",
"repo_id": "datasets",
"token_count": 1953
} | 63 |
# Differences between Dataset and IterableDataset
There are two types of dataset objects, a [`Dataset`] and an [`IterableDataset`].
Whichever type of dataset you choose to use or create depends on the size of the dataset.
In general, an [`IterableDataset`] is ideal for big datasets (think hundreds of GBs!) due to its ... | datasets/docs/source/about_mapstyle_vs_iterable.mdx/0 | {
"file_path": "datasets/docs/source/about_mapstyle_vs_iterable.mdx",
"repo_id": "datasets",
"token_count": 3723
} | 64 |
# Load image data
Image datasets have [`Image`] type columns, which contain PIL objects.
<Tip>
To work with image datasets, you need to have the `vision` dependency installed. Check out the [installation](./installation#vision) guide to learn how to install it.
</Tip>
When you load an image dataset and call the i... | datasets/docs/source/image_load.mdx/0 | {
"file_path": "datasets/docs/source/image_load.mdx",
"repo_id": "datasets",
"token_count": 1404
} | 65 |
# Process
🤗 Datasets provides many tools for modifying the structure and content of a dataset. These tools are important for tidying up a dataset, creating additional columns, converting between features and formats, and much more.
This guide will show you how to:
- Reorder rows and split the dataset.
- Rename and ... | datasets/docs/source/process.mdx/0 | {
"file_path": "datasets/docs/source/process.mdx",
"repo_id": "datasets",
"token_count": 12668
} | 66 |
# Use with PyTorch
This document is a quick introduction to using `datasets` with PyTorch, with a particular focus on how to get
`torch.Tensor` objects out of our datasets, and how to use a PyTorch `DataLoader` and a Hugging Face `Dataset`
with the best performance.
## Dataset format
By default, datasets return regu... | datasets/docs/source/use_with_pytorch.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pytorch.mdx",
"repo_id": "datasets",
"token_count": 3446
} | 67 |
from argparse import ArgumentParser
from typing import Optional
from datasets.commands import BaseDatasetsCLICommand
from datasets.hub import convert_to_parquet
def _command_factory(args):
return ConvertToParquetCommand(
args.dataset_id,
args.token,
args.revision,
args.trust_remot... | datasets/src/datasets/commands/convert_to_parquet.py/0 | {
"file_path": "datasets/src/datasets/commands/convert_to_parquet.py",
"repo_id": "datasets",
"token_count": 652
} | 68 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | datasets/src/datasets/features/features.py/0 | {
"file_path": "datasets/src/datasets/features/features.py",
"repo_id": "datasets",
"token_count": 41469
} | 69 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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 ... | datasets/src/datasets/inspect.py/0 | {
"file_path": "datasets/src/datasets/inspect.py",
"repo_id": "datasets",
"token_count": 6417
} | 70 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ArrowConfig(datasets.BuilderConfig):
"""BuilderConfig for Arrow."""
features: Opt... | datasets/src/datasets/packaged_modules/arrow/arrow.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/arrow/arrow.py",
"repo_id": "datasets",
"token_count": 1641
} | 71 |
import itertools
import warnings
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Fe... | datasets/src/datasets/packaged_modules/pandas/pandas.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/pandas/pandas.py",
"repo_id": "datasets",
"token_count": 1040
} | 72 |
from .parallel import ParallelBackendConfig, parallel_backend, parallel_map
| datasets/src/datasets/parallel/__init__.py/0 | {
"file_path": "datasets/src/datasets/parallel/__init__.py",
"repo_id": "datasets",
"token_count": 19
} | 73 |
from functools import partial
from huggingface_hub import hf_hub_url
from huggingface_hub.utils import get_session, hf_raise_for_status
hf_dataset_url = partial(hf_hub_url, repo_type="dataset")
def check_auth(hf_api, repo_id, token=None):
headers = hf_api._build_hf_headers(token=token)
path = f"{hf_api.end... | datasets/src/datasets/utils/hub.py/0 | {
"file_path": "datasets/src/datasets/utils/hub.py",
"repo_id": "datasets",
"token_count": 180
} | 74 |
from collections.abc import Iterator
from typing import Iterable
class tracked_str(str):
origins = {}
def set_origin(self, origin: str):
if super().__repr__() not in self.origins:
self.origins[super().__repr__()] = origin
def get_origin(self):
return self.origins.get(super().... | datasets/src/datasets/utils/track.py/0 | {
"file_path": "datasets/src/datasets/utils/track.py",
"repo_id": "datasets",
"token_count": 827
} | 75 |
import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Value
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.json.json import Json, JsonConfig
@pytest.fixture
def jsonl_file(tmp_path):
filename = tmp_path / "f... | datasets/tests/packaged_modules/test_json.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_json.py",
"repo_id": "datasets",
"token_count": 3820
} | 76 |
import warnings
import pytest
import datasets.utils.deprecation_utils
from datasets.exceptions import (
ChecksumVerificationError,
ExpectedMoreDownloadedFilesError,
ExpectedMoreSplitsError,
NonMatchingChecksumError,
NonMatchingSplitsSizesError,
SplitsVerificationError,
UnexpectedDownloaded... | datasets/tests/test_exceptions.py/0 | {
"file_path": "datasets/tests/test_exceptions.py",
"repo_id": "datasets",
"token_count": 360
} | 77 |
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