Mikubill

import os
import random
import math, warnings
from copy import deepcopy

import numpy as np
import lightning
import torch
import torch.nn as nn
import torch.nn.functional as F

norabelrose

from argparse import ArgumentParser

from datasets import load_dataset
from peft import LoraConfig
from trl import DPOTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments


if __name__ == "__main__":
    parser = ArgumentParser()

rtkclouds

class Rezero(layers.Layer):
    def __init__(self):
        super().__init__()
        self.alpha1 = tf.Variable(0.0, trainable=True)

    def call(self, inputs, training):
        return self.alpha1*inputs
class CustomRezero(tf.keras.layers.Layer):

titu1994

LiquidityC

VERSION = \"1.0.0\"

PREFIX ?= out
INCDIR  = include
SRCDIR  = src
LANG    = c
OBJDIR  = .obj

MODULE     ?= binary_name
CC         ?= gcc

sekstini

OhadRubin

Instructions

Your task: Combine multiple texts into one detailed document. Include every piece of information from each source. The goal is to avoid repetition while being thorough and exhaustive.

Essential steps:

1. Organize structure carefully.
2. Integrate all details.
3. Avoid redundancy.

Warnings:

• Be precise, not general.

ChrisHayduk

"""

The code below combines approaches published by both @eugene-yh and @jinyongyoo on Github. 

Thanks for the contributions guys!

"""

import torch
import peft

KohakuBlueleaf

import torch
import torch.nn as nn
import torch.nn.functional as F

from einops import rearrange


def parallel_retention(
    q, k, v,            # bsz, heads, seq_len, dim
    decay_mask = None   # heads, seq_len, seq_len

cloneofsimo

import pytest
import torch

import triton
import triton.language as tl


@triton.jit
def _fwd_kernel(
    Q, K, V, sm_scale,