repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
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hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/resnet/README.md | # 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/wuerstchen/main.rs | #[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use candle_transformers::models::wuerstchen;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Tensor};
use clap::Parser;
use tokeniz... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/wuerstchen/README.md | # candle-wuerstchen: Efficient Pretraining of Text-to-Image Models
The `wuerstchen` example is a port of the [diffusers
implementation](https://github.com/huggingface/diffusers/tree/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuer... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama_multiprocess/main.rs | // 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama_multiprocess/model.rs | use candle::backend::BackendStorage;
use candle::{CpuStorage, CustomOp1, DType, Device, IndexOp, Layout, Result, Shape, Tensor, D};
use candle_nn::{Embedding, Linear, Module, RmsNorm};
use cudarc::nccl::safe::{Comm, ReduceOp};
use half::f16;
use serde::Deserialize;
use std::rc::Rc;
use std::sync::{Arc, Mutex};
use sup... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama2-c/main.rs | // https://github.com/karpathy/llama2.c
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::llama2_c as model;
use candle_transformers::models::llama2_c_weights as weights;
use candle_transformers::models::quantized_llama2_c... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama2-c/training.rs | use crate::model::{Cache, Config, Llama};
use candle::{DType, Device, Result};
use candle_datasets::nlp::tinystories::{Dataset, DatasetRandomIter};
use candle_nn::Optimizer;
fn valid_loss(
dataset: &Dataset,
model: &Llama,
args: &crate::TrainingCmd,
device: &Device,
) -> Result<f64> {
let iter = Da... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/main.rs | #![allow(unused)]
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::Result;
use clap::{Parser, Subcommand};
mod gym_env;
mod vec_gym_env;
mod ddpg;
mod policy_gradient;
#[derive(Parser)]
struct Args {
#[command(subcommand)]
command:... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/atari_wrappers.py | import gymnasium as gym
import numpy as np
from collections import deque
from PIL import Image
from multiprocessing import Process, Pipe
# atari_wrappers.py
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30):
"""Sample initial states by taking random number of no-ops on reset.
No... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/README.md | # candle-reinforcement-learning
Reinforcement Learning examples for candle.
This has been tested with `gymnasium` version `0.29.1`. You can install the
Python package with:
```bash
pip install "gymnasium[accept-rom-license]"
```
In order to run the examples, use the following commands. Note the additional
`--package... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/gym_env.rs | #![allow(unused)]
//! Wrappers around the Python API of Gymnasium (the new version of OpenAI gym)
use candle::{Device, Result, Tensor};
use pyo3::prelude::*;
use pyo3::types::PyDict;
/// The return value for a step.
#[derive(Debug)]
pub struct Step<A> {
pub state: Tensor,
pub action: A,
pub reward: f64,
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/ddpg.rs | use std::collections::VecDeque;
use std::fmt::Display;
use candle::{DType, Device, Error, Module, Result, Tensor, Var};
use candle_nn::{
func, linear, sequential::seq, Activation, AdamW, Optimizer, ParamsAdamW, Sequential,
VarBuilder, VarMap,
};
use rand::{distributions::Uniform, thread_rng, Rng};
use super::... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/vec_gym_env.rs | #![allow(unused)]
//! Vectorized version of the gym environment.
use candle::{DType, Device, Result, Tensor};
use pyo3::prelude::*;
use pyo3::types::PyDict;
#[derive(Debug)]
pub struct Step {
pub obs: Tensor,
pub reward: Tensor,
pub is_done: Tensor,
}
pub struct VecGymEnv {
env: PyObject,
action_s... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/policy_gradient.rs | use super::gym_env::{GymEnv, Step};
use candle::{DType, Device, Error, Module, Result, Tensor};
use candle_nn::{
linear, ops::log_softmax, ops::softmax, sequential::seq, Activation, AdamW, Optimizer,
ParamsAdamW, VarBuilder, VarMap,
};
use rand::{distributions::Distribution, rngs::ThreadRng, Rng};
fn new_model... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vgg/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{ModuleT, VarBuilder};
use candle_transformers::models::vgg::{Models, Vgg};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Whic... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vgg/README.md | ## VGG Model Implementation
This example demonstrates the implementation of VGG models (VGG13, VGG16, VGG19) using the Candle library.
The VGG models are defined in `candle-transformers/src/models/vgg.rs`. The main function in `candle-examples/examples/vgg/main.rs` loads an image, selects the VGG model based on the p... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mixtral/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mixtral::{Config, Model};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStr... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mixtral/README.md | # candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the Hu... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/onnx/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{IndexOp, D};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
SqueezeNet,
EfficientNet,
}
#[derive(Parser)]
struct Args {
#[arg(long)]
imag... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/onnx/README.md | ## Using ONNX models in Candle
This example demonstrates how to run ONNX based models in Candle, the model
being used here is a small sequeezenet variant.
You can run the example with the following command:
```bash
cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/phi/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
use candle_transformers::models::phi::{Co... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/phi/README.md | # candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) and
[Phi-2](https://huggingface.co/microsoft/phi-2) are language models using
only 1.3 and 2.7 billion parameters but with state of the art performance compared to
models with up to 10... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/repvgg/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::repvgg;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
A0,
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/repvgg/README.md | # candle-repvgg
[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697).
This candle implementation uses a pre-trained RepVGG network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
`... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/readme.md | # candle-trocr
`TrOCR` is a transformer OCR Model. In this example it is used to
transcribe image text. See the associated [model
card](https://huggingface.co/microsoft/trocr-base-printed) for details on
the model itself.
## Running an example
```bash
cargo run --example trocr --release -- --which base --cpu --imag... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/image_processor.rs | use image::{DynamicImage, ImageBuffer};
use serde::Deserialize;
use std::collections::HashMap;
use candle::{DType, Device, Result, Tensor};
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct ProcessorConfig {
do_resize: bool,
height: u32,
width: u32,
do_rescale: bool,
do_normalize: bool,
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/main.rs | // 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};... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/multilingual.rs | use candle::{IndexOp, Result, Tensor, D};
use tokenizers::Tokenizer;
const LANGUAGES: [(&str, &str); 99] = [
("en", "english"),
("zh", "chinese"),
("de", "german"),
("es", "spanish"),
("ru", "russian"),
("ko", "korean"),
("fr", "french"),
("ja", "japanese"),
("pt", "portuguese"),
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/README.md | # candle-whisper: speech recognition
An implementation of [OpenAI Whisper](https://github.com/openai/whisper) using
candle. Whisper is a general purpose speech recognition model, it can be used to
convert audio files (in the `.wav` format) to text. Supported features include
language detection as well as multilingual ... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/extract_weights.py | # Get the checkpoint from
# https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt
import torch
from safetensors.torch import save_file
data = torch.load("tiny.en.pt")
weights = {}
for k, v in data["model_state_dict"].items():
weights[k] ... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yi/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::yi::{Config, Model};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenO... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/dinov2/main.rs | //! 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/dinov2/README.md | # candle-dinov2
[DINOv2](https://github.com/facebookresearch/dinov2) is a computer vision model.
In this example, it is used as an ImageNet classifier: the model returns the
probability for the image to belong to each of the 1000 ImageNet categories.
## Running some example
```bash
cargo run --example dinov2 --relea... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::object_detection::{non_maximum_suppression, Bbox};
mod darknet;
use anyhow::Result;
use candle::{DType, Device, Tensor};
use candle_nn::{Module, VarBuilder};
use clap::Parser;
use ... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/yolo-v3.cfg | [net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=16
width= 416
height = 416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normaliz... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/darknet.rs | use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Func, Module, VarBuilder};
use std::collections::BTreeMap;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
#[derive(Debug)]
struct Block {
block_type: String,
parameters: BTreeMa... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/extract-weights.py | def remove_prefix(text, prefix):
return text[text.startswith(prefix) and len(prefix):]
nps = {}
for k, v in model.state_dict().items():
k = remove_prefix(k, 'module_list.')
nps[k] = v.detach().numpy()
np.savez('yolo-v3.ot', **nps)
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/README.md | # candle-marian-mt
`marian-mt` is a neural machine translation model. In this example it is used to
translate text from French to English. See the associated [model
card](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-fr-en) for details on
the model itself.
## Running an example
```bash
cargo run --example maria... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/convert_slow_tokenizer.py | # coding=utf-8
# Copyright 2018 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-lm/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::quantized_stable_lm::Model as QStableLM;
use candle_transformers::models::stable_lm::{Config, Model as StableLM};
use c... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-lm/README.md | # 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod model;
use model::{Multiples, YoloV8, YoloV8Pose};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use candle_transformers::object_detection::{non_maximum_sup... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/README.md | # candle-yolo-v8: Object Detection and Pose Estimation
This is a port of [Ultralytics
YOLOv8](https://github.com/ultralytics/ultralytics). The implementation is based
on the [tinygrad
version](https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py)
and on the model architecture described in this
[issue](h... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/model.rs | use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Conv2d, Conv2dConfig, Module, VarBuilder};
#[derive(Clone, Copy, PartialEq, Debug)]
pub struct Multiples {
depth: f64,
width: f64,
ratio: f64,
}
impl Multiples {
pub fn n() -> Self {
Self {
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/efficientnet/main.rs | //! EfficientNet implementation.
//!
//! https://arxiv.org/abs/1905.11946
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::efficientnet::{EfficientNet,... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/t5/main.rs | #[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::t5;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::g... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/t5/README.md | # candle-t5
## Encoder-decoder example:
```bash
$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
...
Eine schöne Kerze.
9 tokens generated (2.42 token/s)
```
Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](ht... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mistral/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mistral::{Config, Model as Mistral};
use candle_transformers::models::quantized_mistral::Model as QMistral;
use candle:... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mistral/README.md | # candle-mistral: 7b LLM with Apache 2.0 licensed weights
Mistral-7B-v0.1 is a pretrained generative LLM with 7 billion parameters. It outperforms all the publicly available 13b models
as of 2023-09-28. Weights (and the original Python model code) are released under the permissive Apache 2.0 license.
- [Blog post](ht... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bert/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};
use anyhow::{Error as E, Result};
use candle::Tensor;
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, ... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bert/README.md | # candle-bert
Bert is a general large language model. In this example it can be used for two
different tasks:
- Compute sentence embeddings for a prompt.
- Compute similarities between a set of sentences.
## Sentence embeddings
Bert is used to compute the sentence embeddings for a prompt. The model weights
are down... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-diffusion/main.rs | #[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
use clap::Parser;
use tokenizers::Tokenizer;
#[derive(Parser)]... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-diffusion/README.md | # candle-stable-diffusion: A Diffusers API in Rust/Candle
_A rusty robot holding a fire torch in its hand_, generated by Stable Diffusion
XL using Rust and [candle](https://github.com/huggingface/candle).
The `stable-diffusion` example is a conversion... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/falcon/main.rs | // TODO: Add an offline mode.
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use clap::Parser;
use h... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/falcon/README.md | # candle-falcon
Falcon is a general large language model.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mnist-training/main.rs | // This should reach 91.5% accuracy.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use rand::prelude::*;
use candle::{DType, Result, Tensor, D};
use candle_nn::{loss, ops, Conv2d, Linear, Module, ModuleT, Optimizer, VarB... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/blip/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::Parser;
use candle::{DType, Device, Result, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::model... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/blip/README.md | # candle-blip
The
[blip-image-captioning](https://huggingface.co/Salesforce/blip-image-captioning-base)
model can generate captions for an input image.
## Running on an example
```bash
cargo run --example blip --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
```
```
Running on CPU, to run on GP... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use std::io::Write;
use tokenizers::Tokenizer;
use candle::quantized::{ggml_file, gguf_file};
use candle::Tensor;
use candle_transformers::generation::LogitsProcessor;
use c... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized/README.md | # candle-quantized-llama: Fast Inference of quantized LLaMA models
This example provides a quantized LLaMA model similar to
[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
built-in quantization methods. Supported features include:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quan... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/custom-ops/main.rs | // This example illustrates how to implement custom operations. These operations can provide their
// own forward pass (CPU and GPU versions) as well as their backward pass.
//
// In this example we add the RMS normalization operation and implement it for f32.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[rus... | 0 |
hf_public_repos/candle/candle-examples/examples/custom-ops | hf_public_repos/candle/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu | #include <stdint.h>
#include "reduction_utils.cuh"
template <typename scalar_t>
__device__ void
rms_norm_kernel(scalar_t *__restrict__ out, // [num_tokens, hidden_size]
const scalar_t *__restrict__ input, // [num_tokens, hidden_size]
const float epsilon, const uint32_t num_token... | 0 |
hf_public_repos/candle/candle-examples/examples/custom-ops | hf_public_repos/candle/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh | /*
* Adapted from
* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/reduce_kernel_utils.cuh
* Copyright (c) 2023, The vLLM team.
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mobileone/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::mobileone;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
S... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mobileone/README.md | # 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama/main.rs | // 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized-t5/main.rs | #[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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized-t5/README.md | # candle-quantized-t5
## Seq2Seq example
This example uses a quantized version of the t5 model.
```bash
$ cargo run --example quantized-t5 --release -- --prompt "translate to German: A beautiful candle."
...
Eine schöne Kerze.
```
## Generating Quantized weight files
The weight file is automatically retrieved fro... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/jina-bert/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::jina_bert::{BertModel, Config};
use anyhow::Error as E;
use candle::{DType, Module, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
#[derive(Parser, Debug)]
#[comman... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/jina-bert/README.md | # candle-jina-bert
Jina-Bert is a general large language model with a context size of 8192, [model
card](https://huggingface.co/jinaai/jina-embeddings-v2-base-en). In this example
it can be used for two different tasks:
- Compute sentence embeddings for a prompt.
- Compute similarities between a set of sentences.
##... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bigcode/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::bigcode::{Config, GPTBigCode};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers:... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bigcode/README.md | # candle-starcoder: code generation model
[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
model specialized to code generation. The initial model was trained on 80
programming languages.
## Running some example
```bash
cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/replit-code/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mpt::{Config, Model as M};
use candle_transformers::models::quantized_mpt::Model as Q;
use candle::{DType, Device, Tens... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/replit-code/README.md | # 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... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
mod model;
use model::{Config, Model};
use candle::{DType, Device, Module, Tensor};
use candle_examples::token_output_stream::TokenOutputSt... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/README.md | # candle-mamba-minimal: minimal implementation of Mamba
This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
## Running the example
```bash
$ cargo run --example mamba-minimal --release -- --prompt "Mamba is the"
Mamba is the most popular and best-selling game in the world. It has been down... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/model.rs | /// 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_... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vit/main.rs | #[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::VarBuilder;
use candle_transformers::models::vit;
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(l... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vit/README.md | # candle-vit
Vision Transformer (ViT) model implementation following the lines of
[vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
This uses a classification head trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --exa... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/main.rs | #![allow(dead_code)]
// https://huggingface.co/facebook/musicgen-small/tree/main
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/modeling_musicgen.py
// TODO: Add an offline mode.
// TODO: Add a KV cache.
#[cfg(feature = "mkl")]
extern crate... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/musicgen_model.rs | use crate::encodec_model;
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{
embedding, layer_norm, linear_no_bias, Activation, Embedding, LayerNorm, Linear, Module,
VarBuilder,
};
use candle_transformers::models::t5;
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe3... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/nn.rs | use candle::Result;
use candle_nn::{Conv1d, Conv1dConfig, VarBuilder};
// Applies weight norm for inference by recomputing the weight tensor. This
// does not apply to training.
// https://pytorch.org/docs/stable/generated/torch.nn.utils.weight_norm.html
pub fn conv1d_weight_norm(
in_c: usize,
out_c: usize,
... | 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/encodec_model.rs | use crate::nn::conv1d_weight_norm;
use candle::{DType, IndexOp, Module, Result, Tensor};
use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};
// Encodec Model
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py
#[derive(Debug, Clone, PartialEq)]
enum Norm... | 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/token_output_stream.rs | use candle::Result;
/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
/// streaming way rather than having to wait for the full decoding.
pub struct TokenOutputStream {
tokenizer: tokenizers::Tokenizer,
tokens: Vec<u32>,
prev_index: usize,
current_index: us... | 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/lib.rs | pub mod coco_classes;
pub mod imagenet;
pub mod token_output_stream;
use candle::utils::{cuda_is_available, metal_is_available};
use candle::{Device, Result, Tensor};
pub fn device(cpu: bool) -> Result<Device> {
if cpu {
Ok(Device::Cpu)
} else if cuda_is_available() {
Ok(Device::new_cuda(0)?)
... | 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/coco_classes.rs | pub const NAMES: [&str; 80] = [
"person",
"bicycle",
"car",
"motorbike",
"aeroplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
... | 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/imagenet.rs | use candle::{Device, Result, Tensor};
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 224, 224). imagenet normalization is applied.
pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
... | 0 |
hf_public_repos/candle | hf_public_repos/candle/.cargo/config.toml | [build]
rustflags = ["-C", "target-cpu=native"]
[target.wasm32-unknown-unknown]
rustflags = ["-C", "target-feature=+simd128"]
[target.x86_64-apple-darwin]
rustflags = ["-C", "target-feature=-avx,-avx2"] | 0 |
hf_public_repos/candle | hf_public_repos/candle/.vscode/settings.json | {
"[python]": {
"editor.defaultFormatter": "ms-python.black-formatter"
},
"python.formatting.provider": "none",
"python.testing.pytestArgs": [
"candle-pyo3"
],
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true
} | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-book/book.toml | [book]
authors = ["Nicolas Patry"]
language = "en"
multilingual = false
src = "src"
title = "Candle Documentation"
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-book/Cargo.toml | [package]
name = "candle-book"
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]
accelerate-src = { workspace = true, optional = true }
candle = { ... | 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/lib.rs | #[cfg(test)]
pub mod simplified;
#[cfg(test)]
mod tests {
use anyhow::Result;
use candle::{DType, Device, Tensor};
use parquet::file::reader::SerializedFileReader;
// NOTE: Waiting on https://github.com/rust-lang/mdBook/pull/1856
#[rustfmt::skip]
#[tokio::test]
async fn book_hub_1() {
// A... | 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/README.md | # Introduction
{{#include ../../README.md:features}}
This book will introduce step by step how to use `candle`.
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/chapter_1.md | # Chapter 1
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/simplified.rs | //! #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... | 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/SUMMARY.md | # Summary
[Introduction](README.md)
# User Guide
- [Installation](guide/installation.md)
- [Hello World - MNIST](guide/hello_world.md)
- [PyTorch cheatsheet](guide/cheatsheet.md)
# Reference Guide
- [Running a model](inference/inference.md)
- [Using the hub](inference/hub.md)
- [Error management](error_manage.... | 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/error_manage.md | # Error management
You might have seen in the code base a lot of `.unwrap()` or `?`.
If you're unfamiliar with Rust check out the [Rust book](https://doc.rust-lang.org/book/ch09-02-recoverable-errors-with-result.html)
for more information.
What's important to know though, is that if you want to know *where* a particu... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/advanced/mkl.md | # Using MKL
| 0 |
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