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Minesh A. Jethva minesh1291
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minesh1291
/ VNC_Server_On_Ubuntu_22.04LTS.md
Created
March 8, 2025 17:20
— forked from indyfromoz/VNC_Server_On_Ubuntu_22.04LTS.md
VNC Server setup on Ubuntu 22.04 LTS
minesh1291
/ grpo_demo.py
Created
February 1, 2025 03:28
— forked from willccbb/grpo_demo.py
GRPO Llama-1B
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| # train_grpo.py | |
| import re | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import LoraConfig | |
| from trl import GRPOConfig, GRPOTrainer | |
| # Load and prep dataset |
minesh1291
/ MoE.py
Created
May 4, 2024 11:55
— forked from ruvnet/MoE.py
A PyTorch implementation of a Mixture of Experts (MoE) model resembling the Mixtral 8x7B architecture, with detailed inline comments. This model combines transformer layers with an MoE layer consisting of 8 experts, aiming for high efficiency by activating only 2 experts per token. It's configured with dimensions reflecting the operational effic…
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| """ | |
| This model integrates the MoE concept within a Transformer architecture. Each token's | |
| representation is processed by a subset of experts, determined by the gating mechanism. | |
| This architecture allows for efficient and specialized handling of different aspects of the | |
| data, aiming for the adaptability and efficiency noted in the Mixtral 8x7B model's design | |
| philosophy. The model activates only a fraction of the available experts for each token, | |
| significantly reducing the computational resources needed compared to activating all experts | |
| for all tokens. | |
| """ |
minesh1291
/ load_m5_gluonts.py
Created
April 24, 2024 05:47
— forked from lostella/load_m5_gluonts.py
Loading M5 competition data into a gluonts PandasDataset
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| # Works on gluonts dev branch as of May 30th, 2023 | |
| # Assumes "m5-forecasting-accuracy" folder with data next to the script | |
| # Data is obtained from https://www.kaggle.com/c/m5-forecasting-accuracy | |
| import pandas as pd | |
| from pathlib import Path | |
| from gluonts.dataset.pandas import PandasDataset | |
| # Load data from csv files |
minesh1291
/ bokeh_subprocess_test.py
Created
November 29, 2023 15:26
— forked from ruoyu0088/bokeh_subprocess_test.py
a demo for zmq process with bokeh server
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| from os import path | |
| from bokeh.models import Button, Div | |
| from bokeh.layouts import column | |
| from bokeh.document import without_document_lock | |
| from bokeh.io import curdoc | |
| from zmq_subprocess import ZmqSubProcessClient | |
| ok_button = Button(label="ok") | |
| div = Div() |
minesh1291
/ cuda_install.md
Created
October 29, 2023 02:46
— forked from denguir/cuda_install.md
Installation procedure for CUDA & cuDNN
minesh1291
/ train.py
Created
August 14, 2023 17:23
— forked from kenqgu/train.py
Tutorial for multimodal_transformers
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| from transformers import Trainer, TrainingArguments | |
| training_args = TrainingArguments( | |
| output_dir="./logs/model_name", | |
| logging_dir="./logs/runs", | |
| overwrite_output_dir=True, | |
| do_train=True, | |
| per_device_train_batch_size=32, | |
| num_train_epochs=1, | |
| evaluate_during_training=True, |
minesh1291
/ model_loading.py
Created
August 14, 2023 17:22
— forked from kenqgu/model_loading.py
Tutorial for multimodal_transformers
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| from multimodal_transformers.model import AutoModelWithTabular, TabularConfig | |
| from transformers import AutoConfig | |
| num_labels = len(np.unique(torch_dataset, labels)) | |
| config = AutoConfig.from_pretrained('bert-base-uncased') | |
| tabular_config = TabularConfig( | |
| num_labels=num_labels, | |
| cat_feat_dim=torch_dataset.cat_feats.shape[1], | |
| numerical_feat_dim=torch_dataset.numerical_feats.shape[1], | |
| combine_feat_method='weighted_feature_sum_on_transformer_cat_and_numerical_feats', |
minesh1291
/ data_loading.py
Created
August 14, 2023 17:22
— forked from kenqgu/data_loading.py
Tutorial for multimodal_transformers
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| import pandas as pd | |
| from multimodal_transformers.data import load_data | |
| from transformers import AutoTokenizer | |
| data_df = pd.read_csv('Womens Clothing E-Commerce Reviews.csv') | |
| text_cols = ['Title', 'Review Text'] | |
| # The label col is expected to contain integers from 0 to N_classes - 1 | |
| label_col = 'Recommended IND' | |
| categorical_cols = ['Clothing ID', 'Division Name', 'Department Name', 'Class Name'] | |
| numerical_cols = ['Rating', 'Age', 'Positive Feedback Count'] |
minesh1291
/ fitter-interface-cartpole.ipynb
Created
May 14, 2023 12:13
— forked from jamartinh/fitter-interface-cartpole.ipynb
Fitter interface cartpole.ipynb
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