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# Testing mixed int8 quantization
The following is the recipe on how to effectively debug `bitsandbytes` integration on Hugging Face `transformers`.
## Library requirements
+ `transformers>=4.22.... | transformers/tests/quantization/bnb/README.md/0 | {
"file_path": "transformers/tests/quantization/bnb/README.md",
"repo_id": "transformers",
"token_count": 1405
} |
# 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_common.py/0 | {
"file_path": "transformers/tests/test_modeling_common.py",
"repo_id": "transformers",
"token_count": 120034
} |
# 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
} |
# 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
} |
# coding=utf-8
# Copyright 2020 The Hugging Face 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 law... | transformers/tests/utils/test_model_output.py/0 | {
"file_path": "transformers/tests/utils/test_model_output.py",
"repo_id": "transformers",
"token_count": 3188
} |
# 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_config_docstrings.py/0 | {
"file_path": "transformers/utils/check_config_docstrings.py",
"repo_id": "transformers",
"token_count": 1326
} |
# coding=utf-8
# Copyright 2021 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/custom_init_isort.py/0 | {
"file_path": "transformers/utils/custom_init_isort.py",
"repo_id": "transformers",
"token_count": 5360
} |
import argparse
import os
past_versions_testing = {
"pytorch": {
"1.13": {
"torch": "1.13.1",
"torchvision": "0.14.1",
"torchaudio": "0.13.1",
"python": 3.9,
"cuda": "cu116",
"install": (
"python3 -m pip install --no-c... | transformers/utils/past_ci_versions.py/0 | {
"file_path": "transformers/utils/past_ci_versions.py",
"repo_id": "transformers",
"token_count": 2774
} |
- sections:
- local: index
title: TRL
- local: installation
title: Installation
- local: quickstart
title: Quickstart
title: Getting started
- sections:
- local: dataset_formats
title: Dataset Formats
- local: how_to_train
title: Training FAQ
- local: logging
title: Understanding L... | trl/docs/source/_toctree.yml/0 | {
"file_path": "trl/docs/source/_toctree.yml",
"repo_id": "trl",
"token_count": 1007
} |
# Examples of using peft with trl to finetune 8-bit models with Low Rank Adaption (LoRA)
The notebooks and scripts in this examples show how to use Low Rank Adaptation (LoRA) to fine-tune models in a memory efficient manner. Most of PEFT methods supported in peft library but note that some PEFT methods such as Prompt ... | trl/docs/source/peft_integration.md/0 | {
"file_path": "trl/docs/source/peft_integration.md",
"repo_id": "trl",
"token_count": 2079
} |
# 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/ultrafeedback-prompt.py/0 | {
"file_path": "trl/examples/datasets/ultrafeedback-prompt.py",
"repo_id": "trl",
"token_count": 1243
} |
# 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/calculator.py/0 | {
"file_path": "trl/examples/research_projects/tools/calculator.py",
"repo_id": "trl",
"token_count": 1430
} |
# 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
} |
# 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
} |
# 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
} |
# 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
} |
# 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
} |
# 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_value_head.py/0 | {
"file_path": "trl/trl/models/modeling_value_head.py",
"repo_id": "trl",
"token_count": 8204
} |
# 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_trainer.py/0 | {
"file_path": "trl/trl/trainer/bco_trainer.py",
"repo_id": "trl",
"token_count": 32834
} |
# 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/model_config.py/0 | {
"file_path": "trl/trl/trainer/model_config.py",
"repo_id": "trl",
"token_count": 2929
} |
# 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_trainer.py/0 | {
"file_path": "trl/trl/trainer/sft_trainer.py",
"repo_id": "trl",
"token_count": 10503
} |
# 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/ms_amp/non_distributed.py/0 | {
"file_path": "accelerate/benchmarks/fp8/ms_amp/non_distributed.py",
"repo_id": "accelerate",
"token_count": 1773
} |
<!--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/install.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/install.md",
"repo_id": "accelerate",
"token_count": 1129
} |
<!--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/model_size_estimator.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/model_size_estimator.md",
"repo_id": "accelerate",
"token_count": 2017
} |
# This config template is for a multi-node setup. This assumes DDP, but can be interop'd with the other configs in this folder
# Generally it's recommended to look at the SLURM config template for a more robust multi-node setup
distributed_type: MULTI_GPU
# We need to specify the current machine's rank
machine_rank: 0
... | accelerate/examples/config_yaml_templates/multi_node.yaml/0 | {
"file_path": "accelerate/examples/config_yaml_templates/multi_node.yaml",
"repo_id": "accelerate",
"token_count": 216
} |
# 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/examples/inference/pippy/bert.py/0 | {
"file_path": "accelerate/examples/inference/pippy/bert.py",
"repo_id": "accelerate",
"token_count": 871
} |
# 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/manim_animations/big_model_inference/stage_4.py/0 | {
"file_path": "accelerate/manim_animations/big_model_inference/stage_4.py",
"repo_id": "accelerate",
"token_count": 2919
} |
# Copyright 2022 The HuggingFace Team and Brian Chao. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | accelerate/src/accelerate/commands/menu/input.py/0 | {
"file_path": "accelerate/src/accelerate/commands/menu/input.py",
"repo_id": "accelerate",
"token_count": 947
} |
# 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
#
# Unless required by applicabl... | accelerate/src/accelerate/state.py/0 | {
"file_path": "accelerate/src/accelerate/state.py",
"repo_id": "accelerate",
"token_count": 23024
} |
# 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_notebook.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_notebook.py",
"repo_id": "accelerate",
"token_count": 1371
} |
# 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/megatron_lm.py/0 | {
"file_path": "accelerate/src/accelerate/utils/megatron_lm.py",
"repo_id": "accelerate",
"token_count": 26942
} |
# 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/deepspeed/test_deepspeed.py/0 | {
"file_path": "accelerate/tests/deepspeed/test_deepspeed.py",
"repo_id": "accelerate",
"token_count": 26455
} |
# 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_examples.py/0 | {
"file_path": "accelerate/tests/test_examples.py",
"repo_id": "accelerate",
"token_count": 4893
} |
repos:
- repo: https://github.com/Narsil/pre-commit-rust
rev: 2eed6366172ef2a5186e8785ec0e67243d7d73d0
hooks:
- id: fmt
name: "Rust (fmt)"
- id: clippy
name: "Rust (clippy)"
args:
[
"--tests",
"--examples",
"--",
"-D... | candle/.pre-commit-config.yaml/0 | {
"file_path": "candle/.pre-commit-config.yaml",
"repo_id": "candle",
"token_count": 210
} |
//! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
//! Author: Evgeny Igumnov 2023 igumnovnsk@gmail.com
//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round... | candle/candle-book/src/simplified.rs/0 | {
"file_path": "candle/candle-book/src/simplified.rs",
"repo_id": "candle",
"token_count": 2903
} |
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use half::{bf16, f16};
use std::time::Instant;
fn run_sum(a: &Tensor) {
a.sum_keepdim(2).unwrap();
}
fn run_arg_min(a: &Tensor) {
a.argmin_keep... | candle/candle-core/benches/benchmarks/reduce.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/reduce.rs",
"repo_id": "candle",
"token_count": 2382
} |
use super::Cpu;
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl CurrentCpu {
#[cfg(target_arch = "aarch64")]
unsafe fn reduce_one(x: floa... | candle/candle-core/src/cpu/neon.rs/0 | {
"file_path": "candle/candle-core/src/cpu/neon.rs",
"repo_id": "candle",
"token_count": 897
} |
use crate::{Error, Tensor};
use std::ops::{
Bound, Range, RangeBounds, RangeFrom, RangeFull, RangeInclusive, RangeTo, RangeToInclusive,
};
impl Tensor {
/// Intended to be use by the trait `.i()`
///
/// ```
/// # use candle_core::{Tensor, DType, Device, IndexOp};
/// let a = Tensor::zeros((2, ... | candle/candle-core/src/indexer.rs/0 | {
"file_path": "candle/candle-core/src/indexer.rs",
"repo_id": "candle",
"token_count": 4032
} |
use super::{GgmlDType, QStorage};
use crate::backend::BackendStorage;
use crate::{DType, MetalDevice, MetalStorage, Result, Shape};
use metal::Buffer;
use std::sync::Arc;
pub struct QMetalStorage {
dtype: GgmlDType,
device: MetalDevice,
buffer: Arc<Buffer>,
}
impl QMetalStorage {
pub fn zeros(device: ... | candle/candle-core/src/quantized/metal.rs/0 | {
"file_path": "candle/candle-core/src/quantized/metal.rs",
"repo_id": "candle",
"token_count": 4704
} |
// Variables are wrappers around tensors that can be modified, they are typically used for holding
// weights and being modified by gradient descent.
// We do not expose a public way to create variables as this would break the invariant that the
// tensor within a variable is actually with `is_variable` set to `true`.
... | candle/candle-core/src/variable.rs/0 | {
"file_path": "candle/candle-core/src/variable.rs",
"repo_id": "candle",
"token_count": 2150
} |
#![allow(unused)]
use anyhow::{Context, Result};
use std::io::Write;
use std::path::PathBuf;
struct KernelDirectories {
kernel_glob: &'static str,
rust_target: &'static str,
include_dirs: &'static [&'static str],
}
const KERNEL_DIRS: [KernelDirectories; 1] = [KernelDirectories {
kernel_glob: "examples... | candle/candle-examples/build.rs/0 | {
"file_path": "candle/candle-examples/build.rs",
"repo_id": "candle",
"token_count": 391
} |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use candle_transformers::models::codegeex4_9b::*;
use clap::Parser;
use hf_hub::{Repo, RepoType};
use tokenizers::Tokenizer;
struct TextGeneration {
model: Model,
device: Device,
tokenizer:... | candle/candle-examples/examples/codegeex4-9b/main.rs/0 | {
"file_path": "candle/candle-examples/examples/codegeex4-9b/main.rs",
"repo_id": "candle",
"token_count": 3921
} |
//! DINOv2: Learning Robust Visual Features without Supervision
//! https://github.com/facebookresearch/dinov2
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use c... | candle/candle-examples/examples/dinov2/main.rs/0 | {
"file_path": "candle/candle-examples/examples/dinov2/main.rs",
"repo_id": "candle",
"token_count": 791
} |
# candle-fastvit
[FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://arxiv.org/abs/2303.14189).
This candle implementation uses a pre-trained FastViT network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 c... | candle/candle-examples/examples/fastvit/README.md/0 | {
"file_path": "candle/candle-examples/examples/fastvit/README.md",
"repo_id": "candle",
"token_count": 258
} |
# hiera
[Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/abs/2306.00989)
This candle implementation uses pre-trained Hiera models from timm for inference.
The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
##... | candle/candle-examples/examples/hiera/README.md/0 | {
"file_path": "candle/candle-examples/examples/hiera/README.md",
"repo_id": "candle",
"token_count": 260
} |
/// This follows the lines of:
/// https://github.com/johnma2006/mamba-minimal/blob/master/model.py
/// Simple, minimal implementation of Mamba in one file of PyTorch.
use candle::{IndexOp, Module, Result, Tensor, D};
use candle_nn::{RmsNorm, VarBuilder};
use candle_transformers::models::with_tracing::{linear, linear_... | candle/candle-examples/examples/mamba-minimal/model.rs/0 | {
"file_path": "candle/candle-examples/examples/mamba-minimal/model.rs",
"repo_id": "candle",
"token_count": 3488
} |
# candle-mobileclip
MobileCLIP is family of efficient CLIP-like models using FastViT-based image encoders.
See [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/abs/2311.17049)
## Running on an example on cpu
```
$ cargo run --example mobileclip --release -- --images "c... | candle/candle-examples/examples/mobileclip/README.md/0 | {
"file_path": "candle/candle-examples/examples/mobileclip/README.md",
"repo_id": "candle",
"token_count": 379
} |
## Using ONNX models in Candle
This example demonstrates how to run [ONNX](https://github.com/onnx/onnx) based models in Candle.
It contains small variants of two models, [SqueezeNet](https://arxiv.org/pdf/1602.07360.pdf) (default) and [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf).
You can run the examples wi... | candle/candle-examples/examples/onnx/README.md/0 | {
"file_path": "candle/candle-examples/examples/onnx/README.md",
"repo_id": "candle",
"token_count": 832
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::io::Write;
use std::path::PathBuf;
use candle_transformers::models::quantized_t5 as t5;
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_transformers::generation::LogitsP... | candle/candle-examples/examples/quantized-t5/main.rs/0 | {
"file_path": "candle/candle-examples/examples/quantized-t5/main.rs",
"repo_id": "candle",
"token_count": 3631
} |
# candle-replit-code: code completion specialized model.
[replit-code-v1_5-3b](https://huggingface.co/replit/replit-code-v1_5-3b) is a
language model specialized for code completion. This model uses 3.3B parameters
in `bfloat16` (so the GPU version will only work on recent nvidia cards).
## Running some example
```b... | candle/candle-examples/examples/replit-code/README.md/0 | {
"file_path": "candle/candle-examples/examples/replit-code/README.md",
"repo_id": "candle",
"token_count": 426
} |
//! SAM: Segment Anything Model
//! https://github.com/facebookresearch/segment-anything
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::DType;
use candle_nn::VarBuilder;
use candle_transformers::models::segment_anything::sam;
use clap::Pars... | candle/candle-examples/examples/segment-anything/main.rs/0 | {
"file_path": "candle/candle-examples/examples/segment-anything/main.rs",
"repo_id": "candle",
"token_count": 3137
} |
# candle-stable-lm
StableLM-3B-4E1T is a 3 billion parameter decoder-only language model
pre-trained on 1 trillion tokens of diverse English and code datasets for 4
epochs. See the [HuggingFace Hub Model
Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t).
Note that this model is gated so you will have to requ... | candle/candle-examples/examples/stable-lm/README.md/0 | {
"file_path": "candle/candle-examples/examples/stable-lm/README.md",
"repo_id": "candle",
"token_count": 432
} |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{Device, IndexOp, Tensor};
use candle_nn::{ops::softmax, VarBuilder};
use clap::{Parser, ValueEnum};
use hf_hub::{api::sync::Api, Repo, RepoType};
use rand::{di... | candle/candle-examples/examples/whisper-microphone/main.rs/0 | {
"file_path": "candle/candle-examples/examples/whisper-microphone/main.rs",
"repo_id": "candle",
"token_count": 12459
} |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
#include "cute/algorithm/copy.hpp"
#include "cutlass/cutlass.h"
#include "cutlass/layout/layout.h"
#include <cu... | candle/candle-flash-attn/kernels/kernel_traits_sm90.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/kernel_traits_sm90.h",
"repo_id": "candle",
"token_count": 3269
} |
#include "cuda_utils.cuh"
#define BINARY_OP_OUT(TYPENAME, OUT_TYPENAME, FN_NAME, FUNC) \
extern "C" __global__ void FN_NAME( \
const size_t numel, \
const size_t num_dims, \
const size_t *dims_and_strides, \
const TYPENAME *lhs, \
const TYPENAME *rhs, \
OUT_TYPENAME *out \
) { \
const size_... | candle/candle-kernels/src/binary_op_macros.cuh/0 | {
"file_path": "candle/candle-kernels/src/binary_op_macros.cuh",
"repo_id": "candle",
"token_count": 1561
} |
#include <metal_stdlib>
METAL_FUNC uint get_strided_index(
uint idx,
constant size_t &num_dims,
constant size_t *dims,
constant size_t *strides
) {
uint strided_i = 0;
for (uint d = 0; d < num_dims; d++) {
uint dim_idx = num_dims - 1 - d;
strided_i += (idx % dims[dim_idx]) * str... | candle/candle-metal-kernels/src/affine.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/affine.metal",
"repo_id": "candle",
"token_count": 1498
} |
#include <metal_stdlib>
using namespace metal;
METAL_FUNC uint get_strided_index(
uint idx,
constant size_t &num_dims,
constant size_t *dims,
constant size_t *strides
) {
uint strided_i = 0;
for (uint d = 0; d < num_dims; d++) {
uint dim_idx = num_dims - 1 - d;
strided_i += (idx... | candle/candle-metal-kernels/src/ternary.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/ternary.metal",
"repo_id": "candle",
"token_count": 2256
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, Device, Result, Tensor};
use candle_nn::{linear, AdamW, Linear, Module, Optimizer, ParamsAdamW, VarBuilder, VarMap};
fn gen_data() -> Result<(Tensor, Tensor)> {
// Generate some sam... | candle/candle-nn/examples/basic_optimizer.rs/0 | {
"file_path": "candle/candle-nn/examples/basic_optimizer.rs",
"repo_id": "candle",
"token_count": 595
} |
//! Various optimization algorithms.
use candle::{Result, Tensor, Var};
/// The interface optimizers should implement.
pub trait Optimizer: Sized {
type Config: Sized;
fn new(vars: Vec<Var>, config: Self::Config) -> Result<Self>;
fn step(&mut self, grads: &candle::backprop::GradStore) -> Result<()>;
... | candle/candle-nn/src/optim.rs/0 | {
"file_path": "candle/candle-nn/src/optim.rs",
"repo_id": "candle",
"token_count": 2798
} |
[package]
name = "candle-onnx"
version = "0.8.2"
edition = "2021"
description = "ONNX support for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
candle = { path = "../candle-core", pac... | candle/candle-onnx/Cargo.toml/0 | {
"file_path": "candle/candle-onnx/Cargo.toml",
"repo_id": "candle",
"token_count": 242
} |
# Generated content DO NOT EDIT
from .. import functional
avg_pool2d = functional.avg_pool2d
gelu = functional.gelu
max_pool2d = functional.max_pool2d
relu = functional.relu
silu = functional.silu
softmax = functional.softmax
tanh = functional.tanh
| candle/candle-pyo3/py_src/candle/functional/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/functional/__init__.py",
"repo_id": "candle",
"token_count": 84
} |
# Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
@staticmethod
def cuda_is_available() -> bool:
"""
Returns true if ... | candle/candle-pyo3/py_src/candle/utils/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/utils/__init__.pyi",
"repo_id": "candle",
"token_count": 654
} |
import candle
from candle import Tensor, QTensor
from candle.utils import load_safetensors, save_gguf, load_gguf, save_safetensors
from pathlib import Path
TEST_DIR = Path(__file__).parent.parent / "_workdir"
TEST_DIR.mkdir(exist_ok=True)
def test_can_roundtrip_safetensors():
tensors = {
"a": candle.rand... | candle/candle-pyo3/tests/native/test_utils.py/0 | {
"file_path": "candle/candle-pyo3/tests/native/test_utils.py",
"repo_id": "candle",
"token_count": 774
} |
//! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - [GH](https://github.com/openai/CLIP)
//! - [Code](https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/s... | candle/candle-transformers/src/models/clip/text_model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/clip/text_model.rs",
"repo_id": "candle",
"token_count": 5660
} |
//! Falcon language model inference implementation
//!
//! See ["Falcon: a new approach to large language models"](https://huggingface.co/blog/falcon)
//!
//! Based on implementation from [Huggingface Transformers](https://github.com/huggingface/transformers/blob/main/src/transformers/models/falcon)
use candle::{DType... | candle/candle-transformers/src/models/falcon.rs/0 | {
"file_path": "candle/candle-transformers/src/models/falcon.rs",
"repo_id": "candle",
"token_count": 8880
} |
//! Llama2 inference implementation.
//!
//! See ["LLaMA 2: Open Foundation and Fine-Tuned Chat Models"](https://arxiv.org/abs/2307.09288)
//!
//! Based on the [llama2.c](https://github.com/karpathy/llama2.c) implementation
use byteorder::{LittleEndian, ReadBytesExt};
use candle::{DType, Device, IndexOp, Result, Shape... | candle/candle-transformers/src/models/llama2_c_weights.rs/0 | {
"file_path": "candle/candle-transformers/src/models/llama2_c_weights.rs",
"repo_id": "candle",
"token_count": 3405
} |
use candle::{Module, Result, Tensor, D};
use candle_nn as nn;
use super::projections::{AttnProjections, Mlp, Qkv, QkvOnlyAttnProjections};
pub struct ModulateIntermediates {
gate_msa: Tensor,
shift_mlp: Tensor,
scale_mlp: Tensor,
gate_mlp: Tensor,
}
pub struct DiTBlock {
norm1: LayerNormNoAffine,... | candle/candle-transformers/src/models/mmdit/blocks.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mmdit/blocks.rs",
"repo_id": "candle",
"token_count": 8057
} |
//! Open Contrastive Language-Image Pre-Training
//!
//! Open Contrastive Language-Image Pre-Training (OpenCLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - 💻 [GH Link](https://github.com/mlfoundations/open_clip)
//! - 📝 [Paper](https://arxiv.org/abs/2212.07143)
//!
//! ## Overview... | candle/candle-transformers/src/models/openclip/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/openclip/mod.rs",
"repo_id": "candle",
"token_count": 154
} |
//! Module containing quantized MixFormer model implementation.
//!
//! MixFormer is an efficient transformer variant for text generation that uses
//! mixture-of-experts and parallel attention/feed-forward blocks.
//! This implementation provides quantization for reduced memory usage.
//!
//! Key features:
//! - Paral... | candle/candle-transformers/src/models/quantized_mixformer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_mixformer.rs",
"repo_id": "candle",
"token_count": 6503
} |
//! RWKV v5 model implementation.
//!
//! The [RWKV model](https://wiki.rwkv.com/) is a recurrent neural network model
//! with performance on par with transformer architectures. Several variants are
//! available, candle implements the v5 and v6 versions and can be used with
//! Eagle 7B([blog post](https://blog.rwkv.... | candle/candle-transformers/src/models/rwkv_v5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/rwkv_v5.rs",
"repo_id": "candle",
"token_count": 8119
} |
//! Ancestral sampling with Euler method steps.
//!
//! Based on the original [`k-diffusion` implementation by Katherine Crowson]( https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72).
//!
use super::{
schedulers::{
betas_for_alpha_bar, BetaSche... | candle/candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs",
"repo_id": "candle",
"token_count": 4097
} |
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url, cacheFile = true) {
if (!cacheFile) return new Uint8Array(await (await fetch(url)).arrayBuffer());
const cacheName = "blip-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
... | candle/candle-wasm-examples/blip/blipWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/blip/blipWorker.js",
"repo_id": "candle",
"token_count": 815
} |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
use candle_transformers::models::quantized_mixformer::MixFormerSequentialForCausalLM as QMixFormer;
use... | candle/candle-wasm-examples/phi/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/phi/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2646
} |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
pub use candle_transformers::models::t5::{Config, T5EncoderModel, T5ForConditionalGeneration};
use candle_wasm_example_t5::console_log;
use tokenizers::Tokenizer;
use wasm_bindgen::prelude::*;
#[wasm_bi... | candle/candle-wasm-examples/t5/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/t5/src/bin/m.rs",
"repo_id": "candle",
"token_count": 3593
} |
use crate::languages::LANGUAGES;
use anyhow::Error as E;
use candle::{safetensors::Load, DType, Device, IndexOp, Tensor, D};
use candle_nn::{ops::softmax, VarBuilder};
pub use candle_transformers::models::whisper::{self as m, Config};
use rand::{distributions::Distribution, rngs::StdRng, SeedableRng};
use serde::{Deser... | candle/candle-wasm-examples/whisper/src/worker.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/worker.rs",
"repo_id": "candle",
"token_count": 8825
} |
[package]
name = "candle-wasm-tests"
version.workspace = true
edition.workspace = true
description = "WASM tests for candle"
keywords.workspace = true
categories.workspace = true
[dependencies]
candle = { workspace = true }
rand = { workspace = true }
getrandom = { version = "0.2", features = ["js"] }
[dev-dependenci... | candle/candle-wasm-tests/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-tests/Cargo.toml",
"repo_id": "candle",
"token_count": 122
} |
# Chat UI
**Find the docs at [hf.co/docs/chat-ui](https://huggingface.co/docs/chat-ui/index).**
A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit a... | chat-ui/README.md/0 | {
"file_path": "chat-ui/README.md",
"repo_id": "chat-ui",
"token_count": 14260
} |
# Text Embedding Models
By default (for backward compatibility), when `TEXT_EMBEDDING_MODELS` environment variable is not defined, [transformers.js](https://huggingface.co/docs/transformers.js) embedding models will be used for embedding tasks, specifically, the [Xenova/gte-small](https://huggingface.co/Xenova/gte-sma... | chat-ui/docs/source/configuration/embeddings.md/0 | {
"file_path": "chat-ui/docs/source/configuration/embeddings.md",
"repo_id": "chat-ui",
"token_count": 1568
} |
# Configuration Overview
Chat UI handles configuration with environment variables. The default config for Chat UI is stored in the `.env` file, which you may use as a reference. You will need to override some values to get Chat UI to run locally. This can be done in `.env.local` or via your environment. The bare minim... | chat-ui/docs/source/configuration/overview.md/0 | {
"file_path": "chat-ui/docs/source/configuration/overview.md",
"repo_id": "chat-ui",
"token_count": 130
} |
import fs from "fs";
import yaml from "js-yaml";
const file = fs.readFileSync("chart/env/prod.yaml", "utf8");
// have to do a weird stringify/parse because of some node error
const prod = JSON.parse(JSON.stringify(yaml.load(file)));
const vars = prod.envVars as Record<string, string>;
let PUBLIC_CONFIG = "";
Object.... | chat-ui/scripts/updateLocalEnv.ts/0 | {
"file_path": "chat-ui/scripts/updateLocalEnv.ts",
"repo_id": "chat-ui",
"token_count": 288
} |
<script lang="ts">
interface Props {
label?: string;
position?: "top" | "bottom" | "left" | "right";
TooltipClassNames?: string;
children?: import("svelte").Snippet;
}
let { label = "", position = "bottom", TooltipClassNames = "", children }: Props = $props();
const positionClasses = {
top: "bottom-full... | chat-ui/src/lib/components/HoverTooltip.svelte/0 | {
"file_path": "chat-ui/src/lib/components/HoverTooltip.svelte",
"repo_id": "chat-ui",
"token_count": 380
} |
<script lang="ts">
interface Props {
checked: boolean;
name: string;
}
let { checked = $bindable(), name }: Props = $props();
</script>
<input bind:checked type="checkbox" {name} class="peer pointer-events-none absolute opacity-0" />
<div
aria-checked={checked}
aria-roledescription="switch"
aria-label="swit... | chat-ui/src/lib/components/Switch.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Switch.svelte",
"repo_id": "chat-ui",
"token_count": 267
} |
<script lang="ts">
import { createBubbler } from "svelte/legacy";
const bubble = createBubbler();
import { useSettingsStore } from "$lib/stores/settings";
import { documentParserToolId } from "$lib/utils/toolIds";
import CarbonImage from "~icons/carbon/image";
interface Props {
// import EosIconsLoading from ... | chat-ui/src/lib/components/chat/FileDropzone.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/FileDropzone.svelte",
"repo_id": "chat-ui",
"token_count": 1191
} |
import type { Migration } from ".";
import { collections } from "$lib/server/database";
import { ObjectId, type WithId } from "mongodb";
import type { Conversation } from "$lib/types/Conversation";
import {
MessageUpdateType,
MessageWebSearchUpdateType,
type MessageUpdate,
} from "$lib/types/MessageUpdate";
import t... | chat-ui/src/lib/migrations/routines/06-trim-message-updates.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/routines/06-trim-message-updates.ts",
"repo_id": "chat-ui",
"token_count": 703
} |
import { z } from "zod";
import type { Endpoint } from "../endpoints";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import { createImageProcessorOptionsValidator } from "../images";
import { endpointMessagesToAnthropicMessages } from "./utils";
import type { MessageParam } from "@anthropic-... | chat-ui/src/lib/server/endpoints/anthropic/endpointAnthropicVertex.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/anthropic/endpointAnthropicVertex.ts",
"repo_id": "chat-ui",
"token_count": 1193
} |
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import type OpenAI from "openai";
import type { Stream } from "openai/streaming";
/**
* Transform a stream of OpenAI.Completions.Completion into a stream of TextGenerationStreamOutput
*/
export async function* openAICompletionToTextGenerationS... | chat-ui/src/lib/server/endpoints/openai/openAICompletionToTextGenerationStream.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/openai/openAICompletionToTextGenerationStream.ts",
"repo_id": "chat-ui",
"token_count": 325
} |
import { isURLLocal } from "./isURLLocal";
import { describe, expect, it } from "vitest";
describe("isURLLocal", async () => {
it("should return true for localhost", async () => {
expect(await isURLLocal(new URL("http://localhost"))).toBe(true);
});
it("should return true for 127.0.0.1", async () => {
expect(aw... | chat-ui/src/lib/server/isURLLocal.spec.ts/0 | {
"file_path": "chat-ui/src/lib/server/isURLLocal.spec.ts",
"repo_id": "chat-ui",
"token_count": 492
} |
import { MessageUpdateType } from "$lib/types/MessageUpdate";
import {
ToolColor,
ToolIcon,
ToolOutputComponents,
type BackendCall,
type BaseTool,
type ConfigTool,
type ToolInput,
} from "$lib/types/Tool";
import type { TextGenerationContext } from "../textGeneration/types";
import { z } from "zod";
import JSON... | chat-ui/src/lib/server/tools/index.ts/0 | {
"file_path": "chat-ui/src/lib/server/tools/index.ts",
"repo_id": "chat-ui",
"token_count": 3696
} |
import type { SerializedHTMLElement } from "./types";
interface DBSCANOptions<T> {
dataset: T[];
epsilon?: number;
epsilonCompare?: (distance: number, epsilon: number) => boolean;
minimumPoints?: number;
distanceFunction: (a: T, b: T) => number;
}
export function spatialParser() {
/**
* Implementation for dbs... | chat-ui/src/lib/server/websearch/scrape/parser.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/scrape/parser.ts",
"repo_id": "chat-ui",
"token_count": 6106
} |
import { base } from "$app/paths";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { share } from "./utils/share";
import { page } from "$app/stores";
import { get } from "svelte/store";
import { getShareUrl } from "./utils/getShareUrl";
export async function shareConversation(id: string, title: stri... | chat-ui/src/lib/shareConversation.ts/0 | {
"file_path": "chat-ui/src/lib/shareConversation.ts",
"repo_id": "chat-ui",
"token_count": 363
} |
import type { ObjectId } from "mongodb";
import type { Conversation } from "./Conversation";
import type { Timestamps } from "./Timestamps";
import type { HeaderElement } from "$lib/server/websearch/markdown/types";
export interface WebSearch extends Timestamps {
_id?: ObjectId;
convId?: Conversation["_id"];
promp... | chat-ui/src/lib/types/WebSearch.ts/0 | {
"file_path": "chat-ui/src/lib/types/WebSearch.ts",
"repo_id": "chat-ui",
"token_count": 348
} |
type Gen<T, TReturn> = AsyncGenerator<T, TReturn, undefined>;
type GenPromiseMap<T, TReturn> = Map<
Gen<T, TReturn>,
Promise<{ gen: Gen<T, TReturn> } & IteratorResult<T, TReturn>>
>;
/** Merges multiple async generators into a single async generator that yields values from all of them in parallel. */
export async f... | chat-ui/src/lib/utils/mergeAsyncGenerators.ts/0 | {
"file_path": "chat-ui/src/lib/utils/mergeAsyncGenerators.ts",
"repo_id": "chat-ui",
"token_count": 407
} |
import type { Conversation } from "$lib/types/Conversation";
import type { Message } from "$lib/types/Message";
import { v4 } from "uuid";
export function addChildren(
conv: Pick<Conversation, "messages" | "rootMessageId">,
message: Omit<Message, "id">,
parentId?: Message["id"]
): Message["id"] {
// if this is the... | chat-ui/src/lib/utils/tree/addChildren.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/addChildren.ts",
"repo_id": "chat-ui",
"token_count": 501
} |
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
export async function GET({ params }) {
const id = params.id;
const assistantId = new ObjectId(id);
const assistant = await collections.assistants.findOne({
_id: assistantId,
});
if (assistant) {
return Response.json(ass... | chat-ui/src/routes/api/assistant/[id]/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/assistant/[id]/+server.ts",
"repo_id": "chat-ui",
"token_count": 133
} |
import type { RequestHandler } from "./$types";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { error, redirect } from "@sveltejs/kit";
import { base } from "$app/paths";
import { z } from "zod";
import type { Message } from "$lib/types/Message";
import { models, validat... | chat-ui/src/routes/conversation/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/+server.ts",
"repo_id": "chat-ui",
"token_count": 1252
} |
<script lang="ts">
import type { PageData } from "./$types";
import { env as envPublic } from "$env/dynamic/public";
import { isHuggingChat } from "$lib/utils/isHuggingChat";
import { base } from "$app/paths";
import { page } from "$app/state";
import CarbonHelpFilled from "~icons/carbon/help-filled";
import ... | chat-ui/src/routes/models/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/models/+page.svelte",
"repo_id": "chat-ui",
"token_count": 2588
} |
import { collections } from "$lib/server/database";
import { error, type RequestHandler } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export const GET: RequestHandler = async ({ params }) => {
const assistant = await collections.assistants.findOne({
_id: new ObjectId(params.assistantId),
});
if (!a... | chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/avatar.jpg/+server.ts/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/avatar.jpg/+server.ts",
"repo_id": "chat-ui",
"token_count": 420
} |
import { base } from "$app/paths";
import { requiresUser } from "$lib/server/auth.js";
import { collections } from "$lib/server/database.js";
import { editableToolSchema } from "$lib/server/tools/index.js";
import { generateSearchTokens } from "$lib/utils/searchTokens.js";
import { error, fail, redirect } from "@svelte... | chat-ui/src/routes/tools/[toolId]/edit/+page.server.ts/0 | {
"file_path": "chat-ui/src/routes/tools/[toolId]/edit/+page.server.ts",
"repo_id": "chat-ui",
"token_count": 650
} |
import json
import os
from dataclasses import dataclass
import numpy as np
import pyarrow as pa
import datasets
from utils import get_duration
SPEED_TEST_N_EXAMPLES = 100_000_000_000
SPEED_TEST_CHUNK_SIZE = 10_000
RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__)
RESULTS_FILE_PATH = os.path.join(RESULTS... | datasets/benchmarks/benchmark_getitem_100B.py/0 | {
"file_path": "datasets/benchmarks/benchmark_getitem_100B.py",
"repo_id": "datasets",
"token_count": 867
} |
# Datasets 🤝 Arrow
## What is Arrow?
[Arrow](https://arrow.apache.org/) enables large amounts of data to be processed and moved quickly. It is a specific data format that stores data in a columnar memory layout. This provides several significant advantages:
* Arrow's standard format allows [zero-copy reads](https:/... | datasets/docs/source/about_arrow.md/0 | {
"file_path": "datasets/docs/source/about_arrow.md",
"repo_id": "datasets",
"token_count": 682
} |
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