text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
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# coding=utf-8
# Copyright 2022 The HuggingFace Team 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 clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/tests/quantization/bnb/test_4bit.py/0 | {
"file_path": "transformers/tests/quantization/bnb/test_4bit.py",
"repo_id": "transformers",
"token_count": 13017
} |
# 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/hqq/test_hqq.py/0 | {
"file_path": "transformers/tests/quantization/hqq/test_hqq.py",
"repo_id": "transformers",
"token_count": 2377
} |
# 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": 43810
} |
# 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/trainer/test_trainer_tpu.py/0 | {
"file_path": "transformers/tests/trainer/test_trainer_tpu.py",
"repo_id": "transformers",
"token_count": 1651
} |
import unittest
import warnings
from dataclasses import dataclass
from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir
@dataclass
class FakeOriginalTokenizer:
vocab_file: str
class ConvertSlowTokenizerTest(unittest.TestCase):
def test_spm_convert... | transformers/tests/utils/test_convert_slow_tokenizer.py/0 | {
"file_path": "transformers/tests/utils/test_convert_slow_tokenizer.py",
"repo_id": "transformers",
"token_count": 524
} |
# coding=utf-8
# Copyright 2024 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_modeling_rope_utils.py/0 | {
"file_path": "transformers/tests/utils/test_modeling_rope_utils.py",
"repo_id": "transformers",
"token_count": 11234
} |
# coding=utf-8
# Copyright 2022 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_doc_toc.py/0 | {
"file_path": "transformers/utils/check_doc_toc.py",
"repo_id": "transformers",
"token_count": 1732
} |
"""Script for downloading all GLUE data.
Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e
Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized,
or you can download the original data from (https://d... | transformers/utils/download_glue_data.py/0 | {
"file_path": "transformers/utils/download_glue_data.py",
"repo_id": "transformers",
"token_count": 3917
} |
# 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/pr_slow_ci_models.py/0 | {
"file_path": "transformers/utils/pr_slow_ci_models.py",
"repo_id": "transformers",
"token_count": 2025
} |
import numpy as np
from transformers import Pipeline
def softmax(outputs):
maxes = np.max(outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
def _sanitize_parameters(self, **... | transformers/utils/test_module/custom_pipeline.py/0 | {
"file_path": "transformers/utils/test_module/custom_pipeline.py",
"repo_id": "transformers",
"token_count": 453
} |
# BCO Trainer
[](https://huggingface.co/models?other=bco,trl)
TRL supports the Binary Classifier Optimization (BCO).
The [BCO](https://huggingface.co/papers/2404.04656) authors train a binary classifier whose logit serves as a reward so that the classifier maps {pr... | trl/docs/source/bco_trainer.md/0 | {
"file_path": "trl/docs/source/bco_trainer.md",
"repo_id": "trl",
"token_count": 1222
} |
# 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/tools/triviaqa.py/0 | {
"file_path": "trl/examples/research_projects/tools/triviaqa.py",
"repo_id": "trl",
"token_count": 2589
} |
# 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/orpo.py/0 | {
"file_path": "trl/examples/scripts/orpo.py",
"repo_id": "trl",
"token_count": 1363
} |
# 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/generate_zen_dataset.py/0 | {
"file_path": "trl/scripts/generate_zen_dataset.py",
"repo_id": "trl",
"token_count": 15758
} |
# 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_collators.py/0 | {
"file_path": "trl/tests/test_collators.py",
"repo_id": "trl",
"token_count": 1448
} |
# 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/cli.py/0 | {
"file_path": "trl/trl/cli.py",
"repo_id": "trl",
"token_count": 1309
} |
# 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/utils.py/0 | {
"file_path": "trl/trl/models/utils.py",
"repo_id": "trl",
"token_count": 3540
} |
# 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/cpo_config.py/0 | {
"file_path": "trl/trl/trainer/cpo_config.py",
"repo_id": "trl",
"token_count": 3110
} |
# 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/nash_md_trainer.py/0 | {
"file_path": "trl/trl/trainer/nash_md_trainer.py",
"repo_id": "trl",
"token_count": 9816
} |
# 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/xpo_config.py/0 | {
"file_path": "trl/trl/trainer/xpo_config.py",
"repo_id": "trl",
"token_count": 566
} |
<!---
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 applicable law or ... | accelerate/docker/README.md/0 | {
"file_path": "accelerate/docker/README.md",
"repo_id": "accelerate",
"token_count": 916
} |
<!--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/concept_guides/deferring_execution.md/0 | {
"file_path": "accelerate/docs/source/concept_guides/deferring_execution.md",
"repo_id": "accelerate",
"token_count": 1349
} |
<!--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/distributed_inference.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/distributed_inference.md",
"repo_id": "accelerate",
"token_count": 2864
} |
# What are these scripts?
All scripts in this folder originate from the `nlp_example.py` file, as it is a very simplistic NLP training example using Accelerate with zero extra features.
From there, each further script adds in just **one** feature of Accelerate, showing how you can quickly modify your own scripts to i... | accelerate/examples/by_feature/README.md/0 | {
"file_path": "accelerate/examples/by_feature/README.md",
"repo_id": "accelerate",
"token_count": 1692
} |
distributed_type: FSDP
fsdp_config:
fsdp_activation_checkpointing: false
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch: BACKWARD_PRE
fsdp_cpu_ram_efficient_loading: true
fsdp_forward_prefetch: false
fsdp_offload_params: false
fsdp_sharding_strategy: FULL_SHARD
fsdp_state_dict_type... | accelerate/examples/slurm/fsdp_config.yaml/0 | {
"file_path": "accelerate/examples/slurm/fsdp_config.yaml",
"repo_id": "accelerate",
"token_count": 167
} |
#!/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/sagemaker.py/0 | {
"file_path": "accelerate/src/accelerate/commands/config/sagemaker.py",
"repo_id": "accelerate",
"token_count": 4784
} |
# 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/hooks.py/0 | {
"file_path": "accelerate/src/accelerate/hooks.py",
"repo_id": "accelerate",
"token_count": 13482
} |
# 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 required by appl... | accelerate/src/accelerate/test_utils/scripts/external_deps/test_peak_memory_usage.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/external_deps/test_peak_memory_usage.py",
"repo_id": "accelerate",
"token_count": 4676
} |
# 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 applicabl... | accelerate/src/accelerate/utils/bnb.py/0 | {
"file_path": "accelerate/src/accelerate/utils/bnb.py",
"repo_id": "accelerate",
"token_count": 8809
} |
# 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/torch_xla.py/0 | {
"file_path": "accelerate/src/accelerate/utils/torch_xla.py",
"repo_id": "accelerate",
"token_count": 691
} |
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_CPU
downcast_bf16: 'no'
ipex_config:
ipex: true
machine_rank: 0
main_process_ip: 127.0.0.1
main_process_port: 29500
main_training_function: main
mixed_precision: 'no'
mpirun_config:
mpirun_ccl: '1'
mpirun_hostfile: /home/user/hostfile
num_mac... | accelerate/tests/test_configs/0_28_0_mpi.yaml/0 | {
"file_path": "accelerate/tests/test_configs/0_28_0_mpi.yaml",
"repo_id": "accelerate",
"token_count": 193
} |
# 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/tests/test_modeling_utils.py/0 | {
"file_path": "accelerate/tests/test_modeling_utils.py",
"repo_id": "accelerate",
"token_count": 20001
} |
# 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/utils/log_reports.py/0 | {
"file_path": "accelerate/utils/log_reports.py",
"repo_id": "accelerate",
"token_count": 3046
} |
# Hello world!
We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.
Open `src/main.rs` and fill in this content:
```rust
# extern crate candle_core;
use candle_core::{Device, Result, Tensor};
struct Model {
first: Tensor,
second: Tensor,
}
impl Model {
... | candle/candle-book/src/guide/hello_world.md/0 | {
"file_path": "candle/candle-book/src/guide/hello_world.md",
"repo_id": "candle",
"token_count": 2069
} |
//! Traits to Define Backend Behavior
//!
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape};
pub trait BackendStorage: Sized {
type Device: BackendDevice;
fn try_clone(&self, _: &Layout) -> Result<Self>;
fn dtype(&self) -> DType;
fn device(&... | candle/candle-core/src/backend.rs/0 | {
"file_path": "candle/candle-core/src/backend.rs",
"repo_id": "candle",
"token_count": 2125
} |
/// Helper functions to plug cuda kernels in candle.
use crate::{Layout, Result, Shape, WithDType};
pub use cudarc;
use cudarc::driver::{CudaSlice, DeviceRepr, ValidAsZeroBits};
use super::{CudaDevice, CudaError, WrapErr};
pub type S = super::CudaStorageSlice;
pub trait Map1 {
fn f<T: DeviceRepr + WithDType + Va... | candle/candle-core/src/cuda_backend/utils.rs/0 | {
"file_path": "candle/candle-core/src/cuda_backend/utils.rs",
"repo_id": "candle",
"token_count": 3748
} |
//! Just enough pickle support to be able to read PyTorch checkpoints.
// This hardcodes objects that are required for tensor reading, we may want to make this a bit more
// composable/tensor agnostic at some point.
use crate::{Context, DType, Error as E, Layout, Result, Tensor};
use byteorder::{LittleEndian, ReadBytes... | candle/candle-core/src/pickle.rs/0 | {
"file_path": "candle/candle-core/src/pickle.rs",
"repo_id": "candle",
"token_count": 14325
} |
use crate::{Result, Tensor};
use rayon::prelude::*;
#[derive(Debug, Clone, Copy)]
struct ArgSort {
asc: bool,
last_dim: usize,
}
impl ArgSort {
fn asort<T: crate::WithDType>(&self, vs: &[T], layout: &crate::Layout) -> Vec<u32> {
#[allow(clippy::uninit_vec)]
// Safety: indexes are set later... | candle/candle-core/src/sort.rs/0 | {
"file_path": "candle/candle-core/src/sort.rs",
"repo_id": "candle",
"token_count": 4723
} |
use candle_core::{test_device, DType, Device, IndexOp, Result, Tensor};
fn matmul(device: &Device) -> Result<()> {
let data = vec![1.0f32, 2.0, 3.0, 4.0];
let a = Tensor::from_slice(&data, (2, 2), device)?;
let data = vec![1.0f32, 2.0, 3.0, 4.0];
let b = Tensor::from_slice(&data, (2, 2), device)?;
... | candle/candle-core/tests/matmul_tests.rs/0 | {
"file_path": "candle/candle-core/tests/matmul_tests.rs",
"repo_id": "candle",
"token_count": 2363
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::bigcode::{Config, GPTBigCode};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers:... | candle/candle-examples/examples/bigcode/main.rs/0 | {
"file_path": "candle/candle-examples/examples/bigcode/main.rs",
"repo_id": "candle",
"token_count": 2134
} |
/*
* Adapted from
* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/reduce_kernel_utils.cuh
* Copyright (c) 2023, The vLLM team.
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
... | candle/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh/0 | {
"file_path": "candle/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh",
"repo_id": "candle",
"token_count": 529
} |
# 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
} |
* 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
} |
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
} |
# 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
} |
# 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
} |
# 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
} |
# 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
} |
#[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
} |
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
} |
#[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
} |
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
} |
// 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
} |
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
} |
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
} |
//! 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
} |
#[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
} |
[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
} |
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
} |
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
} |
//! 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
} |
//! Implementation of the Depth Anything model from FAIR.
//!
//! See:
//! - ["Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data"](https://github.com/LiheYoung/Depth-Anything)
//!
use std::sync::Arc;
use candle::D::Minus1;
use candle::{Module, Result, Tensor};
use candle_nn::ops::Identity;
use candle... | candle/candle-transformers/src/models/depth_anything_v2.rs/0 | {
"file_path": "candle/candle-transformers/src/models/depth_anything_v2.rs",
"repo_id": "candle",
"token_count": 9140
} |
// 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 super::{conv, quantization, seanet, transformer};
use candle::{DType, Device, Module, Result, StreamTensor, StreamingModule, Tensor};
use candle_nn... | candle/candle-transformers/src/models/mimi/encodec.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mimi/encodec.rs",
"repo_id": "candle",
"token_count": 3773
} |
//! Candle implementations for various deep learning models
//!
//! This crate provides implementations of popular machine learning models and architectures for different modalities.
//!
//! - Large language models: [`llama`], [`phi3`], [`mamba`], [`mixtral`], [`bert`], ...
//! - Text to text models: [`t5`], ...
//! ... | candle/candle-transformers/src/models/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mod.rs",
"repo_id": "candle",
"token_count": 1059
} |
//! Pixtral Language-Image Pre-Training
//!
//! Pixtral is an architecture trained for multimodal learning
//! using images paired with text descriptions.
//!
//! - 💻 Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/pixtral)
//! - 📝 [Blog Pos... | candle/candle-transformers/src/models/pixtral/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/pixtral/mod.rs",
"repo_id": "candle",
"token_count": 578
} |
// Adapted from:
// https://github.com/ChaoningZhang/MobileSAM/blob/master/mobile_sam/modeling/tiny_vit_sam.py
use candle::{IndexOp, Result, Tensor, D};
use candle_nn::{Conv2dConfig, Module, VarBuilder};
const MBCONV_EXPAND_RATIO: usize = 4;
const MLP_RATIO: usize = 4;
const LOCAL_CONV_SIZE: usize = 3;
const IMG_SIZE:... | candle/candle-transformers/src/models/segment_anything/tiny_vit.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/tiny_vit.rs",
"repo_id": "candle",
"token_count": 10372
} |
#![allow(dead_code)]
//! # Variational Auto-Encoder (VAE) Models.
//!
//! Auto-encoder models compress their input to a usually smaller latent space
//! before expanding it back to its original shape. This results in the latent values
//! compressing the original information.
use super::unet_2d_blocks::{
DownEncode... | candle/candle-transformers/src/models/stable_diffusion/vae.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/vae.rs",
"repo_id": "candle",
"token_count": 6467
} |
use super::common::{AttnBlock, GlobalResponseNorm, LayerNormNoWeights, TimestepBlock, WLayerNorm};
use candle::{DType, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
pub struct ResBlockStageB {
depthwise: candle_nn::Conv2d,
norm: WLayerNorm,
channelwise_lin1: candle_nn::Linear,
... | candle/candle-transformers/src/models/wuerstchen/diffnext.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/diffnext.rs",
"repo_id": "candle",
"token_count": 8148
} |
//load Candle Bert Module wasm module
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url) {
const cacheName = "bert-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await cachedResponse.... | candle/candle-wasm-examples/bert/bertWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/bert/bertWorker.js",
"repo_id": "candle",
"token_count": 779
} |
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url, cacheModel = true) {
if (!cacheModel)
return new Uint8Array(await (await fetch(url)).arrayBuffer());
const cacheName = "moondream-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.ma... | candle/candle-wasm-examples/moondream/moondreamWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/moondream/moondreamWorker.js",
"repo_id": "candle",
"token_count": 2273
} |
Dockerfile
.vscode/
.idea
.gitignore
LICENSE
README.md
node_modules/
.svelte-kit/
.env*
!.env
.env.local | chat-ui/.dockerignore/0 | {
"file_path": "chat-ui/.dockerignore",
"repo_id": "chat-ui",
"token_count": 51
} |
.DS_Store
node_modules
/build
/.svelte-kit
/package
/chart
.env
.env.*
!.env.example
# Ignore files for PNPM, NPM and YARN
pnpm-lock.yaml
package-lock.json
yarn.lock
| chat-ui/.prettierignore/0 | {
"file_path": "chat-ui/.prettierignore",
"repo_id": "chat-ui",
"token_count": 72
} |
{{- if $.Values.ingress.enabled }}
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
annotations: {{ toYaml .Values.ingress.annotations | nindent 4 }}
labels: {{ include "labels.standard" . | nindent 4 }}
name: {{ include "name" . }}
namespace: {{ .Release.Namespace }}
spec:
{{ if $.Values.ingress.clas... | chat-ui/chart/templates/ingress.yaml/0 | {
"file_path": "chat-ui/chart/templates/ingress.yaml",
"repo_id": "chat-ui",
"token_count": 400
} |
# Google
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | No |
| [Multimodal](../multimodal) | No |
Chat UI can connect to the google Vertex API endpoints ([List of supported models](https://cloud.google.com/vertex-ai/generative-ai/d... | chat-ui/docs/source/configuration/models/providers/google.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/google.md",
"repo_id": "chat-ui",
"token_count": 1138
} |
# Running Locally
You may start an instance locally for non-production use cases. For production use cases, please see the other installation options.
## Configuration
The default config for Chat UI is stored in the `.env` file. You will need to override some values to get Chat UI to run locally. Start by creating a... | chat-ui/docs/source/installation/local.md/0 | {
"file_path": "chat-ui/docs/source/installation/local.md",
"repo_id": "chat-ui",
"token_count": 416
} |
<script lang="ts">
interface Props {
title?: string;
classNames?: string;
children?: import("svelte").Snippet;
}
let { title = "", classNames = "", children }: Props = $props();
</script>
<div class="flex items-center rounded-xl bg-gray-100 p-1 text-sm dark:bg-gray-800 {classNames}">
<span
class="from-pri... | chat-ui/src/lib/components/AnnouncementBanner.svelte/0 | {
"file_path": "chat-ui/src/lib/components/AnnouncementBanner.svelte",
"repo_id": "chat-ui",
"token_count": 235
} |
<script lang="ts">
import {
MessageWebSearchUpdateType,
type MessageWebSearchUpdate,
} from "$lib/types/MessageUpdate";
import { isMessageWebSearchSourcesUpdate } from "$lib/utils/messageUpdates";
import CarbonError from "~icons/carbon/error-filled";
import EosIconsLoading from "~icons/eos-icons/loading";
im... | chat-ui/src/lib/components/OpenWebSearchResults.svelte/0 | {
"file_path": "chat-ui/src/lib/components/OpenWebSearchResults.svelte",
"repo_id": "chat-ui",
"token_count": 1931
} |
<script lang="ts">
import CarbonUpload from "~icons/carbon/upload";
interface Props {
classNames?: string;
files: File[];
mimeTypes: string[];
}
let { classNames = "", files = $bindable(), mimeTypes }: Props = $props();
/**
* Due to a bug with Svelte, we cannot use bind:files with multiple
* So we use... | chat-ui/src/lib/components/UploadBtn.svelte/0 | {
"file_path": "chat-ui/src/lib/components/UploadBtn.svelte",
"repo_id": "chat-ui",
"token_count": 351
} |
<script lang="ts">
interface Props {
classNames?: string;
}
let { classNames = "" }: Props = $props();
</script>
<svg
class={classNames}
xmlns="http://www.w3.org/2000/svg"
aria-hidden="true"
fill="currentColor"
focusable="false"
role="img"
width="1em"
height="1em"
preserveAspectRatio="xMidYMid meet"
vi... | chat-ui/src/lib/components/icons/IconCopy.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconCopy.svelte",
"repo_id": "chat-ui",
"token_count": 324
} |
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
/**
* Returns the lock id if the lock was acquired, false otherwise
*/
export async function acquireLock(key: string): Promise<ObjectId | false> {
try {
const id = new ObjectId();
const insert = await collections.semaphores... | chat-ui/src/lib/migrations/lock.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/lock.ts",
"repo_id": "chat-ui",
"token_count": 400
} |
import { env } from "$env/dynamic/private";
import { GridFSBucket, MongoClient } from "mongodb";
import type { Conversation } from "$lib/types/Conversation";
import type { SharedConversation } from "$lib/types/SharedConversation";
import type { AbortedGeneration } from "$lib/types/AbortedGeneration";
import type { Sett... | chat-ui/src/lib/server/database.ts/0 | {
"file_path": "chat-ui/src/lib/server/database.ts",
"repo_id": "chat-ui",
"token_count": 3102
} |
import { GoogleGenerativeAI, HarmBlockThreshold, HarmCategory } from "@google/generative-ai";
import type { Content, Part, SafetySetting, TextPart } from "@google/generative-ai";
import { z } from "zod";
import type { Message, MessageFile } from "$lib/types/Message";
import type { TextGenerationStreamOutput } from "@hu... | chat-ui/src/lib/server/endpoints/google/endpointGenAI.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/google/endpointGenAI.ts",
"repo_id": "chat-ui",
"token_count": 1780
} |
import type { ToolResult, Tool } from "$lib/types/Tool";
import {
MessageReasoningUpdateType,
MessageUpdateType,
type MessageUpdate,
} from "$lib/types/MessageUpdate";
import { AbortedGenerations } from "../abortedGenerations";
import type { TextGenerationContext } from "./types";
import type { EndpointMessage } fro... | chat-ui/src/lib/server/textGeneration/generate.ts/0 | {
"file_path": "chat-ui/src/lib/server/textGeneration/generate.ts",
"repo_id": "chat-ui",
"token_count": 2248
} |
import { env } from "$env/dynamic/private";
import { getJson, type GoogleParameters } from "serpapi";
import type { WebSearchSource } from "$lib/types/WebSearch";
import { isURL } from "$lib/utils/isUrl";
type SerpApiResponse = {
organic_results: {
link: string;
}[];
};
export default async function searchWebSerp... | chat-ui/src/lib/server/websearch/search/endpoints/serpApi.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/endpoints/serpApi.ts",
"repo_id": "chat-ui",
"token_count": 236
} |
export function switchTheme() {
const { classList } = document.querySelector("html") as HTMLElement;
const metaTheme = document.querySelector('meta[name="theme-color"]') as HTMLMetaElement;
if (classList.contains("dark")) {
classList.remove("dark");
metaTheme.setAttribute("content", "rgb(249, 250, 251)");
loc... | chat-ui/src/lib/switchTheme.ts/0 | {
"file_path": "chat-ui/src/lib/switchTheme.ts",
"repo_id": "chat-ui",
"token_count": 164
} |
import { defaultModel } from "$lib/server/models";
import type { Assistant } from "./Assistant";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface Settings extends Timestamps {
userId?: User["_id"];
sessionId?: string;
/**
* Note: Only conversations with this se... | chat-ui/src/lib/types/Settings.ts/0 | {
"file_path": "chat-ui/src/lib/types/Settings.ts",
"repo_id": "chat-ui",
"token_count": 369
} |
export function getHref(
url: URL | string,
modifications: {
newKeys?: Record<string, string | undefined | null>;
existingKeys?: { behaviour: "delete_except" | "delete"; keys: string[] };
}
) {
const newUrl = new URL(url);
const { newKeys, existingKeys } = modifications;
// exsiting keys logic
if (existingK... | chat-ui/src/lib/utils/getHref.ts/0 | {
"file_path": "chat-ui/src/lib/utils/getHref.ts",
"repo_id": "chat-ui",
"token_count": 373
} |
import { browser } from "$app/environment";
import { isDesktop } from "./isDesktop";
export async function share(url: string, title: string, appendLeafId: boolean = false) {
if (!browser) return;
// Retrieve the leafId from localStorage
const leafId = localStorage.getItem("leafId");
if (appendLeafId && leafId) {... | chat-ui/src/lib/utils/share.ts/0 | {
"file_path": "chat-ui/src/lib/utils/share.ts",
"repo_id": "chat-ui",
"token_count": 331
} |
export async function GET({ locals }) {
if (locals.user) {
const res = {
id: locals.user._id,
username: locals.user.username,
name: locals.user.name,
email: locals.user.email,
avatarUrl: locals.user.avatarUrl,
hfUserId: locals.user.hfUserId,
};
return Response.json(res);
}
return Response.js... | chat-ui/src/routes/api/user/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/user/+server.ts",
"repo_id": "chat-ui",
"token_count": 148
} |
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
/**
* Ideally, we'd be able to detect the client-side abort, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
*/
e... | chat-ui/src/routes/conversation/[id]/stop-generating/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/stop-generating/+server.ts",
"repo_id": "chat-ui",
"token_count": 260
} |
<script lang="ts">
import Modal from "$lib/components/Modal.svelte";
import CarbonClose from "~icons/carbon/close";
import CarbonTrashCan from "~icons/carbon/trash-can";
import CarbonArrowUpRight from "~icons/carbon/arrow-up-right";
import { enhance } from "$app/forms";
import { base } from "$app/paths";
impor... | chat-ui/src/routes/settings/(nav)/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1841
} |
import { base } from "$app/paths";
import { redirect } from "@sveltejs/kit";
export async function load({ parent }) {
const { enableCommunityTools } = await parent();
if (enableCommunityTools) {
return {};
}
redirect(302, `${base}/`);
}
| chat-ui/src/routes/tools/+layout.ts/0 | {
"file_path": "chat-ui/src/routes/tools/+layout.ts",
"repo_id": "chat-ui",
"token_count": 82
} |
{
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
"data": {
"values": "<DVC_METRIC_DATA>"
},
"title": "<DVC_METRIC_TITLE>",
"mark": {
"type": "line"
},
"encoding": {
"x": {
"field": "<DVC_METRIC_X>",
"type": "quantitative",
... | datasets/.dvc/plots/default.json/0 | {
"file_path": "datasets/.dvc/plots/default.json",
"repo_id": "datasets",
"token_count": 419
} |
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "huggingface/datasets"
authors:
- family-names: Lhoest
given-names: Quentin
- family-names: Villanova del Moral
given-names: Albert
orcid: "https://orcid.org/0000-0003-1727-1045"
- family-names: von Platen
given-names: Patri... | datasets/CITATION.cff/0 | {
"file_path": "datasets/CITATION.cff",
"repo_id": "datasets",
"token_count": 1428
} |
# Load audio data
You can load an audio dataset using the [`Audio`] feature that automatically decodes and resamples the audio files when you access the examples.
Audio decoding is based on the [`soundfile`](https://github.com/bastibe/python-soundfile) python package, which uses the [`libsndfile`](https://github.com/l... | datasets/docs/source/audio_load.mdx/0 | {
"file_path": "datasets/docs/source/audio_load.mdx",
"repo_id": "datasets",
"token_count": 1529
} |
# Semantic segmentation
Semantic segmentation datasets are used to train a model to classify every pixel in an image. There are
a wide variety of applications enabled by these datasets such as background removal from images, stylizing
images, or scene understanding for autonomous driving. This guide will show you how ... | datasets/docs/source/semantic_segmentation.mdx/0 | {
"file_path": "datasets/docs/source/semantic_segmentation.mdx",
"repo_id": "datasets",
"token_count": 2142
} |
# Create a video dataset
This guide will show you how to create a video dataset with `VideoFolder` and some metadata. This is a no-code solution for quickly creating a video dataset with several thousand videos.
<Tip>
You can control access to your dataset by requiring users to share their contact information first.... | datasets/docs/source/video_dataset.mdx/0 | {
"file_path": "datasets/docs/source/video_dataset.mdx",
"repo_id": "datasets",
"token_count": 1813
} |
import platform
from argparse import ArgumentParser
import fsspec
import huggingface_hub
import pandas
import pyarrow
from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDatasetsC... | datasets/src/datasets/commands/env.py/0 | {
"file_path": "datasets/src/datasets/commands/env.py",
"repo_id": "datasets",
"token_count": 476
} |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.download_config import DownloadConfig
from ..table import array_cast
from ..utils.file_utils im... | datasets/src/datasets/features/video.py/0 | {
"file_path": "datasets/src/datasets/features/video.py",
"repo_id": "datasets",
"token_count": 5033
} |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import tqdm as hf_tqdm
from ..utils.typing import NestedDataStructureLike, PathL... | datasets/src/datasets/io/csv.py/0 | {
"file_path": "datasets/src/datasets/io/csv.py",
"repo_id": "datasets",
"token_count": 2556
} |
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