abacaj

import tqdm
import numpy as np
import torch
import torch.distributed as dist
import transformers

def extract_xml_answer(text: str) -> str:
    answer = text.split("<final_answer>")[-1]
    answer = answer.split("</final_answer>")[0]
    return answer.strip()

infoslack

# This implementation is based on the paper: https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf
#
# pip install torch transformers
# python grpo_demo.py

import torch
import torch.nn as nn
import torch.optim as optim
from transformers import BertTokenizer, BertModel

innat

import tensorflow as tf

# credit: https://stackoverflow.com/a/66524901/9215780
class CustomTrainStep(tf.keras.Model):
    def __init__(self, n_gradients, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.n_gradients = tf.constant(n_gradients, dtype=tf.int32)
        self.n_acum_step = tf.Variable(0, dtype=tf.int32, trainable=False)
        self.gradient_accumulation = [tf.Variable(tf.zeros_like(v, dtype=tf.float32), 
                                                  trainable=False) for v in self.trainable_variables]

hereismari

Setting up a MSI laptop with GPU (gtx1060)

Installing Ubuntu 18.04, CUDA, CDNN, Pytorch and TensorFlow

Installing Ubuntu 18.04

Get Image

https://tutorials.ubuntu.com/tutorial/tutorial-create-a-usb-stick-on-ubuntu#0

Install (solving issues)

antorsae

# Keras layer implementation of "Fix your classifier: the marginal value of training the last weight layer"
# by Andres Torrubia, licensed under GPL 3: https://www.gnu.org/licenses/gpl-3.0.en.html
# https://arxiv.org/abs/1801.04540

from keras import backend as K
from keras.engine.topology import Layer
from keras import activations
from keras.initializers import Constant, RandomUniform
import numpy as np
from scipy.linalg import hadamard

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content

mjdietzx

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)