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# 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/scripts/grpo.py/0 | {
"file_path": "trl/trl/scripts/grpo.py",
"repo_id": "trl",
"token_count": 1251
} |
# 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/dpo_trainer.py/0 | {
"file_path": "trl/trl/trainer/dpo_trainer.py",
"repo_id": "trl",
"token_count": 36273
} |
# 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/ppo_config.py/0 | {
"file_path": "trl/trl/trainer/ppo_config.py",
"repo_id": "trl",
"token_count": 1824
} |
// File only needed for VSCode users to have proper Docker based interpreters
{
"name": "accelerate_dev_environment",
"build": {
// ACTION NEEDED: comment/uncomment the relevant line depending on whether you are in a CPU/GPU environment
"dockerfile": "../docker/accelerate-cpu/Dockerfile"
// ... | accelerate/.devcontainer/devcontainer.json/0 | {
"file_path": "accelerate/.devcontainer/devcontainer.json",
"repo_id": "accelerate",
"token_count": 459
} |
<!---
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 ... | accelerate/CONTRIBUTING.md/0 | {
"file_path": "accelerate/CONTRIBUTING.md",
"repo_id": "accelerate",
"token_count": 2693
} |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | accelerate/benchmarks/fp8/transformer_engine/distrib_deepspeed.py/0 | {
"file_path": "accelerate/benchmarks/fp8/transformer_engine/distrib_deepspeed.py",
"repo_id": "accelerate",
"token_count": 2964
} |
<!--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/overview.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/overview.md",
"repo_id": "accelerate",
"token_count": 303
} |
<!--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 applicable law or agreed... | accelerate/docs/source/package_reference/big_modeling.md/0 | {
"file_path": "accelerate/docs/source/package_reference/big_modeling.md",
"repo_id": "accelerate",
"token_count": 712
} |
<!--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/checkpoint.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/checkpoint.md",
"repo_id": "accelerate",
"token_count": 1150
} |
<!--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 applicable law or agreed... | accelerate/docs/source/usage_guides/sagemaker.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/sagemaker.md",
"repo_id": "accelerate",
"token_count": 2220
} |
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"weight_decay": "auto"... | accelerate/examples/deepspeed_config_templates/zero_stage1_config.json/0 | {
"file_path": "accelerate/examples/deepspeed_config_templates/zero_stage1_config.json",
"repo_id": "accelerate",
"token_count": 614
} |
# 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/t5.py/0 | {
"file_path": "accelerate/examples/inference/pippy/t5.py",
"repo_id": "accelerate",
"token_count": 1023
} |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/manim_animations/dataloaders/stage_2.py/0 | {
"file_path": "accelerate/manim_animations/dataloaders/stage_2.py",
"repo_id": "accelerate",
"token_count": 3396
} |
#!/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/config.py/0 | {
"file_path": "accelerate/src/accelerate/commands/config/config.py",
"repo_id": "accelerate",
"token_count": 1067
} |
#!/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/test.py/0 | {
"file_path": "accelerate/src/accelerate/commands/test.py",
"repo_id": "accelerate",
"token_count": 755
} |
# 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/test_utils/testing.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/testing.py",
"repo_id": "accelerate",
"token_count": 8669
} |
# 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/operations.py/0 | {
"file_path": "accelerate/src/accelerate/utils/operations.py",
"repo_id": "accelerate",
"token_count": 12875
} |
# 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_big_modeling.py/0 | {
"file_path": "accelerate/tests/test_big_modeling.py",
"repo_id": "accelerate",
"token_count": 18483
} |
# 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/tests/test_kwargs_handlers.py/0 | {
"file_path": "accelerate/tests/test_kwargs_handlers.py",
"repo_id": "accelerate",
"token_count": 3261
} |
# 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_tracking.py/0 | {
"file_path": "accelerate/tests/test_tracking.py",
"repo_id": "accelerate",
"token_count": 10128
} |
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | candle/LICENSE-APACHE/0 | {
"file_path": "candle/LICENSE-APACHE",
"repo_id": "candle",
"token_count": 3168
} |
# Simplified
## How its works
This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
Basic moments:
1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
2. The inpu... | candle/candle-book/src/training/simplified.md/0 | {
"file_path": "candle/candle-book/src/training/simplified.md",
"repo_id": "candle",
"token_count": 530
} |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let device = Device::new_cuda(0)?;
let x = Tensor::randn(0f32, 1.0, (8 * 4096, 8 * 4096), &device)?
.to_dtyp... | candle/candle-core/examples/cuda_basics.rs/0 | {
"file_path": "candle/candle-core/examples/cuda_basics.rs",
"repo_id": "candle",
"token_count": 477
} |
use crate::WithDType;
use cudarc;
use cudarc::cudnn::safe::{ConvForward, Cudnn};
use cudarc::driver::{CudaSlice, CudaView, DeviceRepr, ValidAsZeroBits};
use std::cell::RefCell;
use std::collections::HashMap;
use std::sync::Arc;
// The cudnn handles are stored per thread here rather than on the CudaDevice as they are n... | candle/candle-core/src/cuda_backend/cudnn.rs/0 | {
"file_path": "candle/candle-core/src/cuda_backend/cudnn.rs",
"repo_id": "candle",
"token_count": 2242
} |
//! Implementation of Backend traits for Metal
//!
use crate::backend::{BackendDevice, BackendStorage};
use crate::conv::{ParamsConv1D, ParamsConv2D, ParamsConvTranspose1D, ParamsConvTranspose2D};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, CpuStorageRef, DType, Layout, Result, Shape}... | candle/candle-core/src/metal_backend/mod.rs/0 | {
"file_path": "candle/candle-core/src/metal_backend/mod.rs",
"repo_id": "candle",
"token_count": 51267
} |
use crate::Result;
pub(super) fn nearest_int(v: f32) -> i32 {
v.round() as i32
}
/// Validates that the input and output are the right size and returns an iterator which maps each
/// input region `xs` to its corresponding output block in `ys`. Each output region is guaranteed
/// to be `T::BLCK_SIZE` long.
pub(s... | candle/candle-core/src/quantized/utils.rs/0 | {
"file_path": "candle/candle-core/src/quantized/utils.rs",
"repo_id": "candle",
"token_count": 5775
} |
[package]
name = "candle-datasets"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
byteorder = { workspace = true }
candle = { workspace = true }... | candle/candle-datasets/Cargo.toml/0 | {
"file_path": "candle/candle-datasets/Cargo.toml",
"repo_id": "candle",
"token_count": 201
} |
//! BEiT: BERT Pre-Training of Image Transformers
//! https://github.com/microsoft/unilm/tree/master/beit
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Module,... | candle/candle-examples/examples/beit/main.rs/0 | {
"file_path": "candle/candle-examples/examples/beit/main.rs",
"repo_id": "candle",
"token_count": 1178
} |
# candle-convnext
[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) and
[ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808).
This candle implementation uses a pre-trained ConvNeXt network for inference. The
classification head has been trained on the I... | candle/candle-examples/examples/convnext/README.md/0 | {
"file_path": "candle/candle-examples/examples/convnext/README.md",
"repo_id": "candle",
"token_count": 293
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::... | candle/candle-examples/examples/distilbert/main.rs/0 | {
"file_path": "candle/candle-examples/examples/distilbert/main.rs",
"repo_id": "candle",
"token_count": 1939
} |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::{clip, flux, t5};
use anyhow::{Error as E, Result};
use candle::{IndexOp, Module, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use tokenizers::Tokenizer;
#[derive... | candle/candle-examples/examples/flux/main.rs/0 | {
"file_path": "candle/candle-examples/examples/flux/main.rs",
"repo_id": "candle",
"token_count": 5445
} |
// An implementation of LLaMA https://github.com/facebookresearch/llama
//
// This is based on nanoGPT in a similar way to:
// https://github.com/Lightning-AI/lit-llama/blob/main/lit_llama/model.py
//
// The tokenizer config can be retrieved from:
// https://huggingface.co/hf-internal-testing/llama-tokenizer/raw/main/t... | candle/candle-examples/examples/llama/main.rs/0 | {
"file_path": "candle/candle-examples/examples/llama/main.rs",
"repo_id": "candle",
"token_count": 4888
} |
# candle-mobileone
[MobileOne: An Improved One millisecond Mobile Backbone](https://arxiv.org/abs/2206.04040).
This candle implementation uses a pre-trained MobileOne network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Runnin... | candle/candle-examples/examples/mobileone/README.md/0 | {
"file_path": "candle/candle-examples/examples/mobileone/README.md",
"repo_id": "candle",
"token_count": 254
} |
# candle-qwen: large language model series from Alibaba Cloud
Qwen 1.5 is a series of large language models that provide strong performances
on English and Chinese.
- [Blog post](https://qwenlm.github.io/blog/qwen1.5/) introducing Qwen1.5.
- [Model card](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the HuggingFace Hu... | candle/candle-examples/examples/qwen/README.md/0 | {
"file_path": "candle/candle-examples/examples/qwen/README.md",
"repo_id": "candle",
"token_count": 327
} |
# candle-resnet
A candle implementation of inference using a pre-trained [ResNet](https://arxiv.org/abs/1512.03385).
This uses a classification head trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --example resnet --release -- --image tiger.j... | candle/candle-examples/examples/resnet/README.md/0 | {
"file_path": "candle/candle-examples/examples/resnet/README.md",
"repo_id": "candle",
"token_count": 204
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use clap::Parser;
use candle::{DType, Tensor};
#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
enum Which {
#[value(name = "silero")]
Silero,
}
#[derive(Clone, D... | candle/candle-examples/examples/silero-vad/main.rs/0 | {
"file_path": "candle/candle-examples/examples/silero-vad/main.rs",
"repo_id": "candle",
"token_count": 2894
} |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::path::Path;
use anyhow::{anyhow, Error as E, Result};
use clap::Parser;
use candle_transformers::models::stella_en_v5::{
Config, EmbedDim as StellaEmbedDim, EmbeddingModel,
};
use candle::{D... | candle/candle-examples/examples/stella-en-v5/main.rs/0 | {
"file_path": "candle/candle-examples/examples/stella-en-v5/main.rs",
"repo_id": "candle",
"token_count": 6512
} |
// https://github.com/openai/whisper/blob/main/whisper/model.py/rgs
// TODO:
// - Batch size greater than 1.
// - More token filters (SuppressBlanks, ApplyTimestampRules).
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};... | candle/candle-examples/examples/whisper/main.rs/0 | {
"file_path": "candle/candle-examples/examples/whisper/main.rs",
"repo_id": "candle",
"token_count": 10862
} |
// Build script to run nvcc and generate the C glue code for launching the flash-attention kernel.
// The cuda build time is very long so one can set the CANDLE_FLASH_ATTN_BUILD_DIR environment
// variable in order to cache the compiled artifacts and avoid recompiling too often.
use anyhow::{Context, Result};
use std::... | candle/candle-flash-attn/build.rs/0 | {
"file_path": "candle/candle-flash-attn/build.rs",
"repo_id": "candle",
"token_count": 2163
} |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <cute/tensor.hpp>
#include "utils.h"
////////////////////////////////////////////////////////////////... | candle/candle-flash-attn/kernels/rotary.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/rotary.h",
"repo_id": "candle",
"token_count": 5052
} |
#include "compatibility.cuh"
#include<stdint.h>
#include<cmath>
// TODO: This is often used to check that the data is contiguous so that
// kernels can be easily mapped. However this only returns true for row
// major, if all the inputs are column major, we could apply the fast path
// too (but we wouldn't if some of ... | candle/candle-kernels/src/cuda_utils.cuh/0 | {
"file_path": "candle/candle-kernels/src/cuda_utils.cuh",
"repo_id": "candle",
"token_count": 3947
} |
#include <metal_stdlib>
using namespace metal;
template<typename T> METAL_FUNC void fill_with(
device T *out,
constant float &value,
constant size_t &numel,
uint tid [[thread_position_in_grid]]
) {
if (tid >= numel) {
return;
}
out[tid] = static_cast<T>(value);
}
#define FILL_OP(N... | candle/candle-metal-kernels/src/fill.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/fill.metal",
"repo_id": "candle",
"token_count": 638
} |
use metal::{Buffer, ComputeCommandEncoderRef, ComputePipelineState, MTLSize};
use std::ffi::c_void;
/// Most kernels apply similarly across the tensors
/// This creates a strategy that uses the maximum amount of threads per threadgroup (capped at the
/// actual total buffer length).
/// Then kernels can just do their ... | candle/candle-metal-kernels/src/utils.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/utils.rs",
"repo_id": "candle",
"token_count": 2819
} |
//! Convolution Layers.
use crate::BatchNorm;
use candle::{Result, Tensor};
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv1dConfig {
pub padding: usize,
pub stride: usize,
pub dilation: usize,
pub groups: usize,
}
impl Default for Conv1dConfig {
fn default() -> Self {
Self {
... | candle/candle-nn/src/conv.rs/0 | {
"file_path": "candle/candle-nn/src/conv.rs",
"repo_id": "candle",
"token_count": 5891
} |
//! A `VarBuilder` for variable retrieval from models
//!
//! A `VarBuilder` is used to retrieve variables used by a model. These variables can either come
//! from a pre-trained checkpoint, e.g. using `VarBuilder::from_mmaped_safetensors`, or initialized
//! for training, e.g. using `VarBuilder::from_varmap`.
use crat... | candle/candle-nn/src/var_builder.rs/0 | {
"file_path": "candle/candle-nn/src/var_builder.rs",
"repo_id": "candle",
"token_count": 11054
} |
use candle::Result;
use prost::Message;
pub mod onnx {
include!(concat!(env!("OUT_DIR"), "/onnx.rs"));
}
pub mod eval;
pub use eval::{dtype, simple_eval};
pub fn read_file<P: AsRef<std::path::Path>>(p: P) -> Result<onnx::ModelProto> {
let buf = std::fs::read(p)?;
onnx::ModelProto::decode(buf.as_slice()).... | candle/candle-onnx/src/lib.rs/0 | {
"file_path": "candle/candle-onnx/src/lib.rs",
"repo_id": "candle",
"token_count": 154
} |
use std::collections::HashMap;
use crate::utils::wrap_err;
use crate::{PyDType, PyTensor};
use candle_onnx::eval::{dtype, get_tensor, simple_eval};
use candle_onnx::onnx::tensor_proto::DataType;
use candle_onnx::onnx::tensor_shape_proto::dimension::Value;
use candle_onnx::onnx::type_proto::{Tensor as ONNXTensor, Value... | candle/candle-pyo3/src/onnx.rs/0 | {
"file_path": "candle/candle-pyo3/src/onnx.rs",
"repo_id": "candle",
"token_count": 3268
} |
//! ConvMixer implementation.
//!
//! See "Patches Are All You Need?" by Trockman et al. 2022
//!
//! - 📝 [Arxiv](https://arxiv.org/abs/2201.09792)
//! - 💻 [Github](https://github.com/locuslab/convmixer)
//!
use candle::Result;
use candle_nn::{batch_norm, Conv2dConfig, Module, VarBuilder};
#[allow(clippy::many_singl... | candle/candle-transformers/src/models/convmixer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/convmixer.rs",
"repo_id": "candle",
"token_count": 1504
} |
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{LayerNorm, Linear, RmsNorm, VarBuilder};
// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/model.py#L12
#[derive(Debug, Clone)]
pub struct Config {
pub in_channels: usize,
pub vec_in_dim: usize,
... | candle/candle-transformers/src/models/flux/model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/flux/model.rs",
"repo_id": "candle",
"token_count": 10740
} |
//! Mamba inference implementation.
//!
//! See ["Mamba: Linear-Time Sequence Modeling with Selective State Spaces"](https://arxiv.org/abs/2312.00752)
//!
//! Based on reference implementation from the AlbertMamba project
//! A fast implementation of mamba for inference only.
//! Based on Laurent Mazare's rust implemen... | candle/candle-transformers/src/models/mamba.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mamba.rs",
"repo_id": "candle",
"token_count": 3923
} |
use candle::{Module, Result, Tensor};
use candle_nn as nn;
pub struct Qkv {
pub q: Tensor,
pub k: Tensor,
pub v: Tensor,
}
pub struct Mlp {
fc1: nn::Linear,
act: nn::Activation,
fc2: nn::Linear,
}
impl Mlp {
pub fn new(
in_features: usize,
hidden_features: usize,
v... | candle/candle-transformers/src/models/mmdit/projections.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mmdit/projections.rs",
"repo_id": "candle",
"token_count": 1917
} |
//! Persimmon Model
//!
//! A transformer language model for efficient inference and general-purpose tasks. The model uses a standard transformer architecture with:
//! - Layer normalization for Q/K attention
//! - RoPE embeddings with partial rotary factor
//! - ReLU activation
//! - Separate number of attention heads... | candle/candle-transformers/src/models/persimmon.rs/0 | {
"file_path": "candle/candle-transformers/src/models/persimmon.rs",
"repo_id": "candle",
"token_count": 1045
} |
//! Phi3 model implementation with quantization support.
//!
//! Phi3 is a language model intended for research purposes.
//! This implementation provides quantization for reduced memory usage.
//!
//! Key characteristics:
//! - Multi-head attention
//! - RMSNorm for layer normalization
//! - Rotary positional embeddin... | candle/candle-transformers/src/models/quantized_phi3.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_phi3.rs",
"repo_id": "candle",
"token_count": 6108
} |
use candle::{IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::transformer::TwoWayTransformer;
#[derive(Debug)]
struct MlpMaskDecoder {
layers: Vec<super::Linear>,
sigmoid_output: bool,
span: tracing::Span,
}
impl MlpMaskDecoder {
fn new(
input_dim: usize,
hidd... | candle/candle-transformers/src/models/segment_anything/mask_decoder.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/mask_decoder.rs",
"repo_id": "candle",
"token_count": 4213
} |
//! 2D UNet Denoising Models
//!
//! The 2D Unet models take as input a noisy sample and the current diffusion
//! timestep and return a denoised version of the input.
use super::embeddings::{TimestepEmbedding, Timesteps};
use super::unet_2d_blocks::*;
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Res... | candle/candle-transformers/src/models/stable_diffusion/unet_2d.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/unet_2d.rs",
"repo_id": "candle",
"token_count": 8419
} |
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug, Clone)]
pub struct Embedding {
inner: candle_nn::Embedding,
span: tracing::Span,
}
impl Embedding {
pub fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
let inner = candle_nn::embedding(d1, d2, vb)?;
... | candle/candle-transformers/src/models/with_tracing.rs/0 | {
"file_path": "candle/candle-transformers/src/models/with_tracing.rs",
"repo_id": "candle",
"token_count": 2381
} |
use candle::{Device, Result, Tensor};
use candle_transformers::generation::LogitsProcessor;
#[test]
fn sample_with_zero_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(1337, None, None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.s... | candle/candle-transformers/tests/generation_tests.rs/0 | {
"file_path": "candle/candle-transformers/tests/generation_tests.rs",
"repo_id": "candle",
"token_count": 806
} |
use wasm_bindgen::prelude::*;
pub mod token_output_stream;
#[wasm_bindgen]
extern "C" {
// Use `js_namespace` here to bind `console.log(..)` instead of just
// `log(..)`
#[wasm_bindgen(js_namespace = console)]
pub fn log(s: &str);
}
#[macro_export]
macro_rules! console_log {
// Note that this is u... | candle/candle-wasm-examples/blip/src/lib.rs/0 | {
"file_path": "candle/candle-wasm-examples/blip/src/lib.rs",
"repo_id": "candle",
"token_count": 192
} |
## Running [Moondream 2](https://huggingface.co/vikhyatk/moondream2) Model Example
### Vanilla JS and WebWorkers
To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
```bash
sh build-lib.sh
```
This will bundle the library under `./build` and we can import it inside o... | candle/candle-wasm-examples/moondream/README.md/0 | {
"file_path": "candle/candle-wasm-examples/moondream/README.md",
"repo_id": "candle",
"token_count": 217
} |
## Running Whisper Examples
Here, we provide two examples of how to run Whisper using a Candle-compiled WASM binary and runtimes.
### Pure Rust UI
To build and test the UI made in Rust you will need [Trunk](https://trunkrs.dev/#install)
From the `candle-wasm-examples/whisper` directory run:
Download assets:
```bas... | candle/candle-wasm-examples/whisper/README.md/0 | {
"file_path": "candle/candle-wasm-examples/whisper/README.md",
"repo_id": "candle",
"token_count": 1023
} |
{
"moz:firefoxOptions": {
"prefs": {
"media.navigator.streams.fake": true,
"media.navigator.permission.disabled": true
},
"args": []
},
"goog:chromeOptions": {
"args": [
"--use-fake-device-for-media-stream",
"--use-fake-ui-for-media-stream"
]
}
}
| candle/candle-wasm-tests/webdriver.json/0 | {
"file_path": "candle/candle-wasm-tests/webdriver.json",
"repo_id": "candle",
"token_count": 143
} |
apiVersion: v1
kind: ConfigMap
metadata:
labels: {{ include "labels.standard" . | nindent 4 }}
name: {{ include "name" . }}
namespace: {{ .Release.Namespace }}
data:
{{- range $key, $value := $.Values.envVars }}
{{ $key }}: {{ $value | quote }}
{{- end }}
| chat-ui/chart/templates/config.yaml/0 | {
"file_path": "chat-ui/chart/templates/config.yaml",
"repo_id": "chat-ui",
"token_count": 96
} |
# Anthropic
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | No |
| [Multimodal](../multimodal) | Yes |
We also support Anthropic models (including multimodal ones via `multmodal: true`) through the official SDK. You may provide your ... | chat-ui/docs/source/configuration/models/providers/anthropic.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/anthropic.md",
"repo_id": "chat-ui",
"token_count": 1541
} |
# Copy HuggingChat
The config file for HuggingChat is stored in the `chart/env/prod.yaml` file. It is the source of truth for the environment variables used for our CI/CD pipeline. For HuggingChat, as we need to customize the app color, as well as the base path, we build a custom docker image. You can find the workflo... | chat-ui/docs/source/developing/copy-huggingchat.md/0 | {
"file_path": "chat-ui/docs/source/developing/copy-huggingchat.md",
"repo_id": "chat-ui",
"token_count": 870
} |
import { env } from "$env/dynamic/private";
import { env as envPublic } from "$env/dynamic/public";
import type { Handle, HandleServerError } from "@sveltejs/kit";
import { collections } from "$lib/server/database";
import { base } from "$app/paths";
import { findUser, refreshSessionCookie, requiresUser } from "$lib/se... | chat-ui/src/hooks.server.ts/0 | {
"file_path": "chat-ui/src/hooks.server.ts",
"repo_id": "chat-ui",
"token_count": 3211
} |
<script lang="ts">
import { createEventDispatcher, onDestroy, onMount } from "svelte";
import { cubicOut } from "svelte/easing";
import { fade, fly } from "svelte/transition";
import Portal from "./Portal.svelte";
import { browser } from "$app/environment";
interface Props {
width?: string;
children?: import... | chat-ui/src/lib/components/Modal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Modal.svelte",
"repo_id": "chat-ui",
"token_count": 734
} |
<script lang="ts">
import ToolLogo from "./ToolLogo.svelte";
import { base } from "$app/paths";
import { browser } from "$app/environment";
interface Props {
toolId: string;
}
let { toolId }: Props = $props();
</script>
<div
class="relative flex items-center justify-center space-x-2 rounded border border-gr... | chat-ui/src/lib/components/ToolBadge.svelte/0 | {
"file_path": "chat-ui/src/lib/components/ToolBadge.svelte",
"repo_id": "chat-ui",
"token_count": 523
} |
<script lang="ts">
import { MessageToolUpdateType, type MessageToolUpdate } from "$lib/types/MessageUpdate";
import {
isMessageToolCallUpdate,
isMessageToolErrorUpdate,
isMessageToolResultUpdate,
} from "$lib/utils/messageUpdates";
import CarbonTools from "~icons/carbon/tools";
import { ToolResultStatus, ty... | chat-ui/src/lib/components/chat/ToolUpdate.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/ToolUpdate.svelte",
"repo_id": "chat-ui",
"token_count": 2368
} |
import type { Migration } from ".";
import { collections } from "$lib/server/database";
import { Collection, FindCursor, ObjectId } from "mongodb";
import { logger } from "$lib/server/logger";
import type { Conversation } from "$lib/types/Conversation";
const BATCH_SIZE = 1000;
const DELETE_THRESHOLD_MS = 60 * 60 * 10... | chat-ui/src/lib/migrations/routines/09-delete-empty-conversations.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/routines/09-delete-empty-conversations.ts",
"repo_id": "chat-ui",
"token_count": 950
} |
import { z } from "zod";
import type { Endpoint } from "../endpoints";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import { env } from "$env/dynamic/private";
import { logger } from "$lib/server/logger";
export const endpointCloudflareParametersSchema = z.object({
weight: z.number().int(... | chat-ui/src/lib/server/endpoints/cloudflare/endpointCloudflare.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/cloudflare/endpointCloudflare.ts",
"repo_id": "chat-ui",
"token_count": 1621
} |
import { error } from "@sveltejs/kit";
import { collections } from "$lib/server/database";
import type { Conversation } from "$lib/types/Conversation";
import type { SharedConversation } from "$lib/types/SharedConversation";
import type { MessageFile } from "$lib/types/Message";
export async function downloadFile(
sh... | chat-ui/src/lib/server/files/downloadFile.ts/0 | {
"file_path": "chat-ui/src/lib/server/files/downloadFile.ts",
"repo_id": "chat-ui",
"token_count": 397
} |
import { env } from "$env/dynamic/private";
import type { ChatTemplateInput } from "$lib/types/Template";
import { compileTemplate } from "$lib/utils/template";
import { z } from "zod";
import endpoints, { endpointSchema, type Endpoint } from "./endpoints/endpoints";
import { endpointTgi } from "./endpoints/tgi/endpoin... | chat-ui/src/lib/server/models.ts/0 | {
"file_path": "chat-ui/src/lib/server/models.ts",
"repo_id": "chat-ui",
"token_count": 4853
} |
import { stringifyMarkdownElementTree } from "$lib/server/websearch/markdown/utils/stringify";
import { scrapeUrl } from "$lib/server/websearch/scrape/scrape";
import type { ConfigTool } from "$lib/types/Tool";
import { ObjectId } from "mongodb";
const fetchUrl: ConfigTool = {
_id: new ObjectId("000000000000000000000... | chat-ui/src/lib/server/tools/web/url.ts/0 | {
"file_path": "chat-ui/src/lib/server/tools/web/url.ts",
"repo_id": "chat-ui",
"token_count": 357
} |
import { WebSearchProvider, type WebSearchSource } from "$lib/types/WebSearch";
import { env } from "$env/dynamic/private";
import searchSerper from "./endpoints/serper";
import searchSerpApi from "./endpoints/serpApi";
import searchSerpStack from "./endpoints/serpStack";
import searchYouApi from "./endpoints/youApi";
... | chat-ui/src/lib/server/websearch/search/endpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/endpoints.ts",
"repo_id": "chat-ui",
"token_count": 543
} |
import type { ObjectId } from "mongodb";
import type { User } from "./User";
import type { Assistant } from "./Assistant";
import type { Timestamps } from "./Timestamps";
export interface Report extends Timestamps {
_id: ObjectId;
createdBy: User["_id"] | string;
object: "assistant" | "tool";
contentId: Assistant[... | chat-ui/src/lib/types/Report.ts/0 | {
"file_path": "chat-ui/src/lib/types/Report.ts",
"repo_id": "chat-ui",
"token_count": 112
} |
type UUID = ReturnType<typeof crypto.randomUUID>;
export function randomUUID(): UUID {
// Only on old safari / ios
if (!("randomUUID" in crypto)) {
return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) =>
(
Number(c) ^
(crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4))... | chat-ui/src/lib/utils/randomUuid.ts/0 | {
"file_path": "chat-ui/src/lib/utils/randomUuid.ts",
"repo_id": "chat-ui",
"token_count": 166
} |
import type { Conversation } from "$lib/types/Conversation";
import type { Message } from "$lib/types/Message";
export function buildSubtree(
conv: Pick<Conversation, "messages" | "rootMessageId">,
id: Message["id"]
): Message[] {
if (!conv.rootMessageId) {
if (conv.messages.length === 0) return [];
// legacy c... | chat-ui/src/lib/utils/tree/buildSubtree.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/buildSubtree.ts",
"repo_id": "chat-ui",
"token_count": 329
} |
import { models } from "$lib/server/models";
export async function GET() {
const res = models
.filter((m) => m.unlisted == false)
.map((model) => ({
id: model.id,
name: model.name,
websiteUrl: model.websiteUrl ?? "https://huggingface.co",
modelUrl: model.modelUrl ?? "https://huggingface.co",
tokeni... | chat-ui/src/routes/api/models/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/models/+server.ts",
"repo_id": "chat-ui",
"token_count": 311
} |
import { buildPrompt } from "$lib/buildPrompt";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { models } from "$lib/server/models";
import { buildSubtree } from "$lib/utils/tree/buildSubtree";
import { isMessageId } from "$lib/utils/tree/isMessageId";
impor... | chat-ui/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts",
"repo_id": "chat-ui",
"token_count": 654
} |
<script lang="ts">
import { env as envPublic } from "$env/dynamic/public";
import { isHuggingChat } from "$lib/utils/isHuggingChat";
import logo from "../../../../../static/huggingchat/logo.svg?raw";
interface Props {
name: string;
logoUrl: string | undefined;
}
let { name, logoUrl }: Props = $props();
</sc... | chat-ui/src/routes/models/[...model]/thumbnail.png/ModelThumbnail.svelte/0 | {
"file_path": "chat-ui/src/routes/models/[...model]/thumbnail.png/ModelThumbnail.svelte",
"repo_id": "chat-ui",
"token_count": 503
} |
<script lang="ts">
import type { ActionData, PageData } from "./$types";
import AssistantSettings from "$lib/components/AssistantSettings.svelte";
interface Props {
data: PageData;
form: ActionData;
}
let { data, form = $bindable() }: Props = $props();
</script>
<AssistantSettings bind:form models={data.mod... | chat-ui/src/routes/settings/(nav)/assistants/new/+page@settings.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/assistants/new/+page@settings.svelte",
"repo_id": "chat-ui",
"token_count": 108
} |
import { sveltekit } from "@sveltejs/kit/vite";
import Icons from "unplugin-icons/vite";
import { promises } from "fs";
import { defineConfig } from "vitest/config";
// used to load fonts server side for thumbnail generation
function loadTTFAsArrayBuffer() {
return {
name: "load-ttf-as-array-buffer",
async transf... | chat-ui/vite.config.ts/0 | {
"file_path": "chat-ui/vite.config.ts",
"repo_id": "chat-ui",
"token_count": 393
} |
import json
import sys
def format_json_to_md(input_json_file, output_md_file):
with open(input_json_file, encoding="utf-8") as f:
results = json.load(f)
output_md = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(results):
benchmark_res = re... | datasets/benchmarks/format.py/0 | {
"file_path": "datasets/benchmarks/format.py",
"repo_id": "datasets",
"token_count": 746
} |
# Create an image dataset
There are two methods for creating and sharing an image dataset. This guide will show you how to:
* Create an image dataset from local files in python with [`Dataset.push_to_hub`]. This is an easy way that requires only a few steps in python.
* Create an image dataset with `ImageFolder` and... | datasets/docs/source/image_dataset.mdx/0 | {
"file_path": "datasets/docs/source/image_dataset.mdx",
"repo_id": "datasets",
"token_count": 5689
} |
# Utilities
## Configure logging
🤗 Datasets strives to be transparent and explicit about how it works, but this can be quite verbose at times. We have included a series of logging methods which allow you to easily adjust the level of verbosity of the entire library. Currently the default verbosity of the library is ... | datasets/docs/source/package_reference/utilities.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/utilities.mdx",
"repo_id": "datasets",
"token_count": 725
} |
# Use with PyArrow
This document is a quick introduction to using `datasets` with PyArrow, with a particular focus on how to process
datasets using Arrow compute functions, and how to convert a dataset to PyArrow or from PyArrow.
This is particularly useful as it allows fast zero-copy operations, since `datasets` use... | datasets/docs/source/use_with_pyarrow.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pyarrow.mdx",
"repo_id": "datasets",
"token_count": 1257
} |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
HIGHLIGHT_MESSAGE_PRE = """<<<<<<< This should probably be modified because it mentions: """
HIGHLIGHT_MESSAGE_POST = """=======
>>>>>>>... | datasets/src/datasets/commands/convert.py/0 | {
"file_path": "datasets/src/datasets/commands/convert.py",
"repo_id": "datasets",
"token_count": 3811
} |
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/audio.py/0 | {
"file_path": "datasets/src/datasets/features/audio.py",
"repo_id": "datasets",
"token_count": 5332
} |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | datasets/src/datasets/info.py/0 | {
"file_path": "datasets/src/datasets/info.py",
"repo_id": "datasets",
"token_count": 8410
} |
import itertools
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class XmlConfig(datasets.BuilderConf... | datasets/src/datasets/packaged_modules/xml/xml.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/xml/xml.py",
"repo_id": "datasets",
"token_count": 1167
} |
# deprecated, please use the `filelock` package instead
from filelock import ( # noqa: F401 # imported for backward compatibility TODO: remove in 3.0.0
BaseFileLock,
SoftFileLock,
Timeout,
UnixFileLock,
WindowsFileLock,
)
from ._filelock import FileLock # noqa: F401 # imported for backward compa... | datasets/src/datasets/utils/filelock.py/0 | {
"file_path": "datasets/src/datasets/utils/filelock.py",
"repo_id": "datasets",
"token_count": 115
} |
"""Utility helpers to handle progress bars in `datasets`.
Example:
1. Use `datasets.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`.
2. To disable progress bars, either use `disable_progress_bars()` helper or set the
environment variable `HF_DATASETS_DISABLE_PROGRESS_BARS` to 1.
3. To r... | datasets/src/datasets/utils/tqdm.py/0 | {
"file_path": "datasets/src/datasets/utils/tqdm.py",
"repo_id": "datasets",
"token_count": 1662
} |
import os
import time
import uuid
from contextlib import contextmanager
from typing import Optional
import pytest
import requests
from huggingface_hub.hf_api import HfApi, RepositoryNotFoundError
from huggingface_hub.utils import hf_raise_for_status
CI_HUB_USER = "__DUMMY_TRANSFORMERS_USER__"
CI_HUB_USER_FULL_NAME =... | datasets/tests/fixtures/hub.py/0 | {
"file_path": "datasets/tests/fixtures/hub.py",
"repo_id": "datasets",
"token_count": 2900
} |
import shutil
import textwrap
import numpy as np
import pytest
from datasets import ClassLabel, Features, Image, Value
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesDict, DataFilesList, get_data_patterns
from datasets.download.streaming_download_manager import StreamingDownlo... | datasets/tests/packaged_modules/test_imagefolder.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_imagefolder.py",
"repo_id": "datasets",
"token_count": 8893
} |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.download.streaming_download_manager import StreamingDownloadManager
from datasets.utils.file_utils import hash_url_to_f... | datasets/tests/test_download_manager.py/0 | {
"file_path": "datasets/tests/test_download_manager.py",
"repo_id": "datasets",
"token_count": 2945
} |
from tempfile import NamedTemporaryFile
import pytest
import requests
from datasets.utils.file_utils import fsspec_get, fsspec_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline, require_not_windows
@pytest.mark.integration
@require_not_windows # fsspec get keeps a file hand... | datasets/tests/test_offline_util.py/0 | {
"file_path": "datasets/tests/test_offline_util.py",
"repo_id": "datasets",
"token_count": 641
} |
.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
export PYTHONPATH = src
check_dirs := examples scripts src tests utils benchmarks
modified_only_fixup:... | diffusers/Makefile/0 | {
"file_path": "diffusers/Makefile",
"repo_id": "diffusers",
"token_count": 929
} |
FROM ubuntu:20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -y update \
&& apt-get install -y software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa
RUN apt install -y bash \
build-essential \
git \
git-l... | diffusers/docker/diffusers-flax-cpu/Dockerfile/0 | {
"file_path": "diffusers/docker/diffusers-flax-cpu/Dockerfile",
"repo_id": "diffusers",
"token_count": 652
} |
<!-- 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 agree... | diffusers/docs/source/en/api/cache.md/0 | {
"file_path": "diffusers/docs/source/en/api/cache.md",
"repo_id": "diffusers",
"token_count": 674
} |
<!-- 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 agree... | diffusers/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md",
"repo_id": "diffusers",
"token_count": 383
} |
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