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#
# Unless required by applicable... | transformers/utils/update_metadata.py/0 | {
"file_path": "transformers/utils/update_metadata.py",
"repo_id": "transformers",
"token_count": 5916
} | 440 |
# hello world experiment
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmar... | trl/benchmark/benchmark_level1.sh/0 | {
"file_path": "trl/benchmark/benchmark_level1.sh",
"repo_id": "trl",
"token_count": 906
} | 441 |
# Best of N sampling: Alternative ways to get better model output without RL based fine-tuning
Within the extras module is the `best-of-n` sampler class that serves as an alternative method of generating better model output.
As to how it fares against the RL based fine-tuning, please look in the `examples` directory ... | trl/docs/source/best_of_n.mdx/0 | {
"file_path": "trl/docs/source/best_of_n.mdx",
"repo_id": "trl",
"token_count": 840
} | 442 |
# Multi Adapter RL (MARL) - a single base model for everything
Here we present an approach that uses a single base model for the entire PPO algorithm - which includes retrieving the reference logits, computing the active logits and the rewards. This feature is experimental as we did not test the convergence of the app... | trl/docs/source/multi_adapter_rl.mdx/0 | {
"file_path": "trl/docs/source/multi_adapter_rl.mdx",
"repo_id": "trl",
"token_count": 1207
} | 443 |
# 0. imports
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import torch
from accelerate import Accelerator
from datasets import Dataset, load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, TrainingArguments, set... | trl/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py/0 | {
"file_path": "trl/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py",
"repo_id": "trl",
"token_count": 3863
} | 444 |
# Copyright 2023 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... | trl/examples/scripts/reward_modeling.py/0 | {
"file_path": "trl/examples/scripts/reward_modeling.py",
"repo_id": "trl",
"token_count": 1819
} | 445 |
# 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... | trl/tests/test_core.py/0 | {
"file_path": "trl/tests/test_core.py",
"repo_id": "trl",
"token_count": 568
} | 446 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/tests/testing_utils.py/0 | {
"file_path": "trl/tests/testing_utils.py",
"repo_id": "trl",
"token_count": 1062
} | 447 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/models/sd_utils.py/0 | {
"file_path": "trl/trl/models/sd_utils.py",
"repo_id": "trl",
"token_count": 2507
} | 448 |
# 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... | trl/trl/trainer/utils.py/0 | {
"file_path": "trl/trl/trainer/utils.py",
"repo_id": "trl",
"token_count": 15913
} | 449 |
# Builds CPU-only Docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
FROM python:3.8-slim as compile-image
ARG DEBIAN_FRONTEND=noninteractive
RUN apt update
RUN apt-get install -y --no-install-recommends \
build-essential \
git \
gcc
# Setup virtual environment for Docker
ENV V... | accelerate/docker/accelerate-cpu/Dockerfile/0 | {
"file_path": "accelerate/docker/accelerate-cpu/Dockerfile",
"repo_id": "accelerate",
"token_count": 380
} | 0 |
<!--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/concept_guides/internal_mechanism.md/0 | {
"file_path": "accelerate/docs/source/concept_guides/internal_mechanism.md",
"repo_id": "accelerate",
"token_count": 1096
} | 1 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | accelerate/docs/source/usage_guides/megatron_lm.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/megatron_lm.md",
"repo_id": "accelerate",
"token_count": 9557
} | 2 |
# 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/llama.py/0 | {
"file_path": "accelerate/examples/inference/llama.py",
"repo_id": "accelerate",
"token_count": 695
} | 3 |
# 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/accelerator.py/0 | {
"file_path": "accelerate/src/accelerate/accelerator.py",
"repo_id": "accelerate",
"token_count": 63109
} | 4 |
# 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/optimizer.py/0 | {
"file_path": "accelerate/src/accelerate/optimizer.py",
"repo_id": "accelerate",
"token_count": 3103
} | 5 |
#!/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/test_utils/scripts/test_script.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_script.py",
"repo_id": "accelerate",
"token_count": 12418
} | 6 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/utils/modeling.py/0 | {
"file_path": "accelerate/src/accelerate/utils/modeling.py",
"repo_id": "accelerate",
"token_count": 34335
} | 7 |
# 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_cli.py/0 | {
"file_path": "accelerate/tests/test_cli.py",
"repo_id": "accelerate",
"token_count": 9037
} | 8 |
# 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_multigpu.py/0 | {
"file_path": "accelerate/tests/test_multigpu.py",
"repo_id": "accelerate",
"token_count": 2091
} | 9 |
#!/bin/bash
#SBATCH --ntasks-per-node=1
#SBATCH --exclusive
#SBATCH --gres=gpu:8
#SBATCH --partition=hopper-prod # Adjust this for your cluster
#SBATCH --output=/fsx/h4/logs/%x-%j.out # Adjust this for your cluster
#SBATCH --err=/fsx/h4/logs/%x-%j.err # Adjust this for your cluster
set -x -e
source ~/.bashrc
cond... | alignment-handbook/recipes/launch.slurm/0 | {
"file_path": "alignment-handbook/recipes/launch.slurm",
"repo_id": "alignment-handbook",
"token_count": 1135
} | 10 |
# Scripts to Train and Evaluate Chat Models
## Fine-tuning
In the handbook, we provide three main ways to align LLMs for chat:
- Full fine-tuning on a multi-GPU machine with DeepSpeed ZeRO-3 (tested on an 8 x A100 (80GB) node).
- LoRA or QLoRA fine-tuning on a single consumer 24GB GPU (tested on an RTX 4090).
- LoR... | alignment-handbook/scripts/README.md/0 | {
"file_path": "alignment-handbook/scripts/README.md",
"repo_id": "alignment-handbook",
"token_count": 2967
} | 11 |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | alignment-handbook/tests/test_data.py/0 | {
"file_path": "alignment-handbook/tests/test_data.py",
"repo_id": "alignment-handbook",
"token_count": 4201
} | 12 |
[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 = { ... | candle/candle-book/Cargo.toml/0 | {
"file_path": "candle/candle-book/Cargo.toml",
"repo_id": "candle",
"token_count": 467
} | 13 |
# Installation
**With Cuda support**:
1. First, make sure that Cuda is correctly installed.
- `nvcc --version` should print information about your Cuda compiler driver.
- `nvidia-smi --query-gpu=compute_cap --format=csv` should print your GPUs compute capability, e.g. something
like:
```bash
compute_cap
8.9
```
You... | candle/candle-book/src/guide/installation.md/0 | {
"file_path": "candle/candle-book/src/guide/installation.md",
"repo_id": "candle",
"token_count": 487
} | 14 |
mod benchmarks;
use criterion::criterion_main;
criterion_main!(
benchmarks::affine::benches,
benchmarks::matmul::benches,
benchmarks::random::benches,
benchmarks::where_cond::benches
);
| candle/candle-core/benches/bench_main.rs/0 | {
"file_path": "candle/candle-core/benches/bench_main.rs",
"repo_id": "candle",
"token_count": 71
} | 15 |
#![allow(clippy::excessive_precision)]
// Code taken from https://github.com/statrs-dev/statrs
//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
//! related functions
mod evaluate {
//! Provides functions that don't have a numerical solution and must
//! be solved computationally (e.g.... | candle/candle-core/src/cpu/erf.rs/0 | {
"file_path": "candle/candle-core/src/cpu/erf.rs",
"repo_id": "candle",
"token_count": 11974
} | 16 |
//! ML framework for Rust
//!
//! ```rust
//! use candle_core::{Tensor, DType, Device};
//! # use candle_core::Error;
//! # fn main() -> Result<(), Error>{
//!
//! let a = Tensor::arange(0f32, 6f32, &Device::Cpu)?.reshape((2, 3))?;
//! let b = Tensor::arange(0f32, 12f32, &Device::Cpu)?.reshape((3, 4))?;
//!
//! let c =... | candle/candle-core/src/lib.rs/0 | {
"file_path": "candle/candle-core/src/lib.rs",
"repo_id": "candle",
"token_count": 1521
} | 17 |
use super::k_quants::{BlockQ2K, BlockQ4K, BlockQ4_0, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
use core::arch::wasm32::*;
#[inline(always)]
pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> Result<f32> {
... | candle/candle-core/src/quantized/simd128.rs/0 | {
"file_path": "candle/candle-core/src/quantized/simd128.rs",
"repo_id": "candle",
"token_count": 11617
} | 18 |
#![allow(clippy::approx_constant)]
use anyhow::{Context, Result};
use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
fn simple_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4.], device)?;
let x = x.as_tensor();
let y = (((x * x)? + x * 5f64)? + 4f64)?;
let grad... | candle/candle-core/tests/grad_tests.rs/0 | {
"file_path": "candle/candle-core/tests/grad_tests.rs",
"repo_id": "candle",
"token_count": 9105
} | 19 |
use candle::{Result, Tensor};
pub struct Batcher<I> {
inner: I,
batch_size: usize,
return_last_incomplete_batch: bool,
}
impl<I> Batcher<I> {
fn new(inner: I) -> Self {
Self {
inner,
batch_size: 16,
return_last_incomplete_batch: false,
}
}
p... | candle/candle-datasets/src/batcher.rs/0 | {
"file_path": "candle/candle-datasets/src/batcher.rs",
"repo_id": "candle",
"token_count": 2660
} | 20 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::Parser;
use candle::{DType, Device, Result, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::model... | candle/candle-examples/examples/blip/main.rs/0 | {
"file_path": "candle/candle-examples/examples/blip/main.rs",
"repo_id": "candle",
"token_count": 2437
} | 21 |
# candle-endocec
[EnCodec](https://huggingface.co/facebook/encodec_24khz) is a high-quality audio
compression model using an encoder/decoder architecture with residual vector
quantization.
## Running one example
```bash
cargo run --example encodec --features symphonia --release -- code-to-audio \
candle-examples... | candle/candle-examples/examples/encodec/README.md/0 | {
"file_path": "candle/candle-examples/examples/encodec/README.md",
"repo_id": "candle",
"token_count": 305
} | 22 |
#[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... | candle/candle-examples/examples/mamba-minimal/main.rs/0 | {
"file_path": "candle/candle-examples/examples/mamba-minimal/main.rs",
"repo_id": "candle",
"token_count": 4087
} | 23 |
#![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... | candle/candle-examples/examples/musicgen/main.rs/0 | {
"file_path": "candle/candle-examples/examples/musicgen/main.rs",
"repo_id": "candle",
"token_count": 1151
} | 24 |
use std::collections::VecDeque;
use rand::distributions::Uniform;
use rand::{thread_rng, Rng};
use candle::{DType, Device, Module, Result, Tensor};
use candle_nn::loss::mse;
use candle_nn::{linear, seq, Activation, AdamW, Optimizer, VarBuilder, VarMap};
use crate::gym_env::GymEnv;
const DEVICE: Device = Device::Cpu... | candle/candle-examples/examples/reinforcement-learning/dqn.rs/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/dqn.rs",
"repo_id": "candle",
"token_count": 2032
} | 25 |
use candle::Device;
use candle::Module;
use candle_nn::VarBuilder;
use candle_transformers::models::segformer::{
Config, ImageClassificationModel, SemanticSegmentationModel,
};
use clap::{Args, Parser, Subcommand};
use image::Rgb;
use imageproc::integral_image::ArrayData;
use std::collections::HashMap;
use std::pat... | candle/candle-examples/examples/segformer/main.rs/0 | {
"file_path": "candle/candle-examples/examples/segformer/main.rs",
"repo_id": "candle",
"token_count": 2226
} | 26 |
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,
... | candle/candle-examples/examples/trocr/image_processor.rs/0 | {
"file_path": "candle/candle-examples/examples/trocr/image_processor.rs",
"repo_id": "candle",
"token_count": 2274
} | 27 |
# 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... | candle/candle-examples/examples/wuerstchen/README.md/0 | {
"file_path": "candle/candle-examples/examples/wuerstchen/README.md",
"repo_id": "candle",
"token_count": 358
} | 28 |
// Copied from https://github.com/ruuda/bs1770/blob/master/src/lib.rs
// BS1770 -- Loudness analysis library conforming to ITU-R BS.1770
// Copyright 2020 Ruud van Asseldonk
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// A copy ... | candle/candle-examples/src/bs1770.rs/0 | {
"file_path": "candle/candle-examples/src/bs1770.rs",
"repo_id": "candle",
"token_count": 7223
} | 29 |
/******************************************************************************
* 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
} | 30 |
#include "cuda_utils.cuh"
#include<stdint.h>
// Naive implementation of conv1d.
template <typename T, typename A>
__device__ void conv1d(
const size_t src_numel,
const size_t l_out,
const size_t stride,
const size_t padding,
const size_t dilation,
const size_t *info,
const T *src,
const... | candle/candle-kernels/src/conv.cu/0 | {
"file_path": "candle/candle-kernels/src/conv.cu",
"repo_id": "candle",
"token_count": 10786
} | 31 |
use metal::{
Buffer, CommandBufferRef, CompileOptions, ComputeCommandEncoderRef, ComputePipelineState,
Device, Function, FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
};
use std::collections::HashMap;
use std::ffi::c_void;
use std::sync::RwLock;
const AFFINE: &str = include_str!("affine.me... | candle/candle-metal-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 26995
} | 32 |
use candle::{Result, Tensor};
use serde::Deserialize;
#[derive(Debug, Clone, Copy, PartialEq, Deserialize, Default)]
#[serde(rename_all = "lowercase")]
pub enum Activation {
#[default]
#[serde(alias = "gelu")]
Gelu,
#[serde(alias = "gelu_new")]
NewGelu,
Relu,
Relu2,
Relu6,
Silu,
... | candle/candle-nn/src/activation.rs/0 | {
"file_path": "candle/candle-nn/src/activation.rs",
"repo_id": "candle",
"token_count": 1650
} | 33 |
//! 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 crate::VarMap;
use candle::{safetensors::Load, DType, Device, ... | candle/candle-nn/src/var_builder.rs/0 | {
"file_path": "candle/candle-nn/src/var_builder.rs",
"repo_id": "candle",
"token_count": 8632
} | 34 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{Device, Result, Tensor};
use candle_onnx::onnx::{AttributeProto, GraphProto, ModelProto, NodeProto, ValueInfoProto};
use std::collections::HashMap;
const INPUT_X: &str = "x";
const INPUT_Y: &s... | candle/candle-onnx/tests/ops.rs/0 | {
"file_path": "candle/candle-onnx/tests/ops.rs",
"repo_id": "candle",
"token_count": 20694
} | 35 |
import math
from typing import Any
import candle
from candle import Tensor
from .module import Module
# See https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/linear.py
class Identity(Module):
r"""A placeholder identity operator that is argument-insensitive.
Args:
args: any argument (unu... | candle/candle-pyo3/py_src/candle/nn/linear.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/linear.py",
"repo_id": "candle",
"token_count": 1947
} | 36 |
# See: https://raw.githubusercontent.com/huggingface/tokenizers/main/bindings/python/stub.py
import argparse
import inspect
import os
from typing import Optional
import black
from pathlib import Path
import re
INDENT = " " * 4
GENERATED_COMMENT = "# Generated content DO NOT EDIT\n"
TYPING = """from typing import Any,... | candle/candle-pyo3/stub.py/0 | {
"file_path": "candle/candle-pyo3/stub.py",
"repo_id": "candle",
"token_count": 3852
} | 37 |
#![allow(unused)]
/// A fast implementation of mamba for inference only.
/// This is based on: https://github.com/LaurentMazare/mamba.rs
use crate::models::with_tracing::{linear, linear_no_bias, Linear};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{RmsNorm, VarBuilder};
const D_CONV... | candle/candle-transformers/src/models/mamba.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mamba.rs",
"repo_id": "candle",
"token_count": 3792
} | 38 |
use crate::quantized_nn::{linear_no_bias, Embedding, Linear, RmsNorm};
pub use crate::quantized_var_builder::VarBuilder;
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::Activation;
use std::sync::Arc;
pub use crate::models::mistral::Config;
#[derive(Debug, Clone)]
struct RotaryEmbedding {
s... | candle/candle-transformers/src/models/quantized_mistral.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_mistral.rs",
"repo_id": "candle",
"token_count": 6082
} | 39 |
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
struct PositionEmbeddingRandom {
positional_encoding_gaussian_matrix: Tensor,
}
impl PositionEmbeddingRandom {
fn new(num_pos_feats: usize, vb: VarBuilder) -> Result<Self> {
let positional_encoding_gaussian_ma... | candle/candle-transformers/src/models/segment_anything/prompt_encoder.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/prompt_encoder.rs",
"repo_id": "candle",
"token_count": 4719
} | 40 |
#![allow(dead_code)]
//! # Variational Auto-Encoder (VAE) Models.
//!
//! Auto-encoder models compress their input to a usually smaller latent space
//! before expanding it back to its original shape. This results in the latent values
//! compressing the original information.
use super::unet_2d_blocks::{
DownEncode... | candle/candle-transformers/src/models/stable_diffusion/vae.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/vae.rs",
"repo_id": "candle",
"token_count": 6006
} | 41 |
pub mod attention_processor;
pub mod common;
pub mod ddpm;
pub mod diffnext;
pub mod paella_vq;
pub mod prior;
| candle/candle-transformers/src/models/wuerstchen/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/mod.rs",
"repo_id": "candle",
"token_count": 38
} | 42 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::models::bert::{BertModel, Config};
use candle_wasm_example_bert::console_log;
use tokenizers::{PaddingParams, Tokenizer};
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub struct Model {
bert: BertModel,
tokenizer: Tokeniz... | candle/candle-wasm-examples/bert/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/bert/src/bin/m.rs",
"repo_id": "candle",
"token_count": 1534
} | 43 |
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url) {
const cacheName = "llama2c-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await cachedResponse.arrayBuffer();
return new Uint8... | candle/candle-wasm-examples/llama2-c/llama2cWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/llama2cWorker.js",
"repo_id": "candle",
"token_count": 1223
} | 44 |
## Running Segment Anything Example
Here, we provide an example showing how to run the Segment Anything model in the
browser.
### 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 u... | candle/candle-wasm-examples/segment-anything/README.md/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/README.md",
"repo_id": "candle",
"token_count": 220
} | 45 |
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