filename stringlengths 2 69 | filepath stringlengths 39 208 | relative_path stringlengths 13 182 | language stringclasses 11
values | lsl_type stringclasses 3
values | description stringclasses 1
value | content stringlengths 0 71.8M |
|---|---|---|---|---|---|---|
api.py | D:\GitHub\ai_train\notgpl\ai\aipy\api.py | ai\aipy\api.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
from fastapi import FastAPI
from pydantic import BaseModel
import random
random_seed = random.randin... |
api2.py | D:\GitHub\ai_train\notgpl\ai\aipy\api2.py | ai\aipy\api2.py | Python | N/A | Functionality description extraction logic here | import os
import json
from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling
from datasets import Dataset
import torch
app = FastAPI()
# Load model and... |
api3.py | D:\GitHub\ai_train\notgpl\ai\aipy\api3.py | ai\aipy\api3.py | Python | N/A | Functionality description extraction logic here | import os
from transformers import AutoTokenizer, AutoModelForCausalLM
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
# Define the FastAPI app
app = FastAPI()
class InputText(BaseModel):
input_text: str
# Path to the pretrained model and tokenizer
model_path = "./results"
tokenizer = A... |
api_ollama.py | D:\GitHub\ai_train\notgpl\ai\aipy\api_ollama.py | ai\aipy\api_ollama.py | Python | N/A | Functionality description extraction logic here | import os
import json
from fastapi import FastAPI
from pydantic import BaseModel
import requests
# Available models
models = {
"noromaid": "NeverSleep/Noromaid-7b-v0.1.1",
"gpt-medium": "openai-community/gpt2-medium",
"gpt-large": "openai-community/gpt2-large",
"llama3": "meta-llama/Llama-2-7b-chat-hf"... |
auth.py | D:\GitHub\ai_train\notgpl\ai\aipy\auth.py | ai\aipy\auth.py | Python | N/A | Functionality description extraction logic here | from flask import jsonify
from flask_login import current_user
from . import login_manager
from .models import User
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
@login_manager.unauthorized_handler
def unauthorized():
return jsonify({"message": "Unauthorized access"}),... |
bloominator.py | D:\GitHub\ai_train\notgpl\ai\aipy\bloominator.py | ai\aipy\bloominator.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
from transformers import BloomForCausalLM, BloomTokenizerFast, TrainingArguments, Trainer, DataCollatorForLanguageModeling, get_scheduler
from datasets import load_dataset
from accelerate import Accelerator
import deepspeed
import bitsandbytes as bnb
# Set cache directory
os.environ[... |
bot.py | D:\GitHub\ai_train\notgpl\ai\aipy\bot.py | ai\aipy\bot.py | Python | N/A | Functionality description extraction logic here | import discord
from discord.ext import commands
from discord import app_commands
import aiohttp
from config import TOKEN, GUILD_ID, API_URL, BOT_OWNER_ID
MY_GUILD = discord.Object(id=GUILD_ID)
class MyBot(commands.Bot):
def __init__(self):
super().__init__(command_prefix='!', intents=discord.Intents.defau... |
bot_lora.py | D:\GitHub\ai_train\notgpl\ai\aipy\bot_lora.py | ai\aipy\bot_lora.py | Python | N/A | Functionality description extraction logic here | import discord
from discord.ext import commands
from discord import app_commands
import aiohttp
from config import TOKEN, GUILD_ID, API_URL, BOT_OWNER_ID, HF_TOKEN, CACHE_DIR, OLLAMA_HOST
from ollama import AsyncClient
MY_GUILD = discord.Object(id=GUILD_ID)
class MyBot(commands.Bot):
def __init__(self):
s... |
bot_ollama.py | D:\GitHub\ai_train\notgpl\ai\aipy\bot_ollama.py | ai\aipy\bot_ollama.py | Python | N/A | Functionality description extraction logic here | import discord
from discord.ext import commands
from discord import app_commands
import aiohttp
from config import TOKEN, GUILD_ID, API_URL, BOT_OWNER_ID
import json # Add this import
MY_GUILD = discord.Object(id=GUILD_ID)
class MyBot(commands.Bot):
def __init__(self):
super().__init__(command_prefix='!... |
chat.py | D:\GitHub\ai_train\notgpl\ai\aipy\chat.py | ai\aipy\chat.py | Python | N/A | Functionality description extraction logic here | from transformers import BloomTokenizerFast, BloomForCausalLM
import torch
# Specify the model name
model_name_or_path = "mia4o-bloom"
# Load the tokenizer and the model
tokenizer = BloomTokenizerFast.from_pretrained(model_name_or_path,cache_dir="D:\.cache")
model = BloomForCausalLM.from_pretrained(model_name_or_path... |
chaten.py | D:\GitHub\ai_train\notgpl\ai\aipy\chaten.py | ai\aipy\chaten.py | Python | N/A | Functionality description extraction logic here | from transformers import BloomTokenizerFast, BloomForCausalLM, MarianMTModel, MarianTokenizer
import torch
import langdetect
# Specify the model names
chat_model_name = "WangZeJun/bloom-820m-chat"
translation_model_name = 'Helsinki-NLP/opus-mt-zh-en'
# Load the tokenizer and the model for chat
chat_tokenizer = BloomT... |
client.py | D:\GitHub\ai_train\notgpl\ai\aipy\client.py | ai\aipy\client.py | Python | N/A | Functionality description extraction logic here | import requests
BASE_URL = 'http://127.0.0.1:5000'
def register(username, password):
url = f"{BASE_URL}/register"
payload = {'username': username, 'password': password}
response = requests.post(url, json=payload)
return response.json()
def login(username, password):
url = f"{BASE_URL}/login"
... |
codeinator.py | D:\GitHub\ai_train\notgpl\ai\aipy\codeinator.py | ai\aipy\codeinator.py | Python | N/A | Functionality description extraction logic here | import requests
import logging
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
from torch.utils.data import IterableDataset, DataLoader
import torch
import os
import wandb
from huggingface_hub import login
# Set up your Hugging Face token
hf_to... |
codeset.py | D:\GitHub\ai_train\notgpl\ai\aipy\codeset.py | ai\aipy\codeset.py | Python | N/A | Functionality description extraction logic here | import os
import csv
import chardet
# Define the directory containing your source code files
source_code_directory = input("Enter the path to the directory containing your source code files: ")
# Define the output CSV file path
output_csv_file = input("Enter the path to the output CSV file: ")
# A dictionary to map f... |
combined.py | D:\GitHub\ai_train\notgpl\ai\aipy\combined.py | ai\aipy\combined.py | Python | N/A | Functionality description extraction logic here | import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
def combine_models(base_model_name, lora_path, cache_dir, output_path):
# Load the tokenizer and the base model
tokenizer = AutoTokenizer.from_pretrained(base_model_name, cache_dir=cache_d... |
config.py | D:\GitHub\ai_train\notgpl\ai\aipy\config.py | ai\aipy\config.py | Python | N/A | Functionality description extraction logic here | # config.py
# Replace with your actual bot token
TOKEN = 'MTI0ODkxMTYyNjc5NTYxNDIwOA.G-RnFi.LbXz-1GVhNSCJZEeYHl6Vb0BG7xIovpS0bvRkk'
# Replace with your actual guild ID
GUILD_ID = 187966607451095041
# REST API URL
API_URL = 'http://localhost:11434/api'
BOT_OWNER_ID = 1199767018837127178 # Replace with your actual Di... |
convertconvert-bloom-hf-to-gguf.py | D:\GitHub\ai_train\notgpl\ai\aipy\convertconvert-bloom-hf-to-gguf.py | ai\aipy\convertconvert-bloom-hf-to-gguf.py | Python | N/A | Functionality description extraction logic here | #!/usr/bin/env python3
# HF bloom --> gguf conversion
from __future__ import annotations
import argparse
import json
import os
import re
import struct
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
from transformers import AutoTokenizer # type: ignore[import]
if 'NO_LOCA... |
crossbreeder.py | D:\GitHub\ai_train\notgpl\ai\aipy\crossbreeder.py | ai\aipy\crossbreeder.py | Python | N/A | Functionality description extraction logic here | import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset
class MultiTaskModel(nn.Module):
def __init__(self, model1, model2):
super(MultiTaskModel, self).__init__()
self.model1 = model1
self.model2 = m... |
dataset2data.py | D:\GitHub\ai_train\notgpl\ai\aipy\dataset2data.py | ai\aipy\dataset2data.py | Python | N/A | Functionality description extraction logic here | import json
import random
import os
def split_dataset(dataset_path, train_ratio=0.8, val_ratio=0.1, test_ratio=0.1, seed=None):
if seed is not None:
random.seed(seed)
# Load the dataset
with open(dataset_path, 'r', encoding='utf-8') as f:
data = json.load(f)
# Shuffle the data
ran... |
discordtrainer.py | D:\GitHub\ai_train\notgpl\ai\aipy\discordtrainer.py | ai\aipy\discordtrainer.py | Python | N/A | Functionality description extraction logic here | import os
import torch
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
# Login to Hugging Face
login(token="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")
# User inputs... |
discordtrainer_lora.py | D:\GitHub\ai_train\notgpl\ai\aipy\discordtrainer_lora.py | ai\aipy\discordtrainer_lora.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
from peft import LoraConfig, get_peft_model, PeftModel
# Login to Hugging Face
... |
ggufmk.py | D:\GitHub\ai_train\notgpl\ai\aipy\ggufmk.py | ai\aipy\ggufmk.py | Python | N/A | Functionality description extraction logic here | import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def convert_to_gguf(model, tokenizer, output_path):
model_dict = {
"model_state_dict": model.state_dict(),
"config": model.config.to_dict(),
"tokenizer": tokenizer.get_vocab()
}
# Saving the model dictionary ... |
hackllama.py | D:\GitHub\ai_train\notgpl\ai\aipy\hackllama.py | ai\aipy\hackllama.py | Python | N/A | Functionality description extraction logic here | import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
# Load the dataset
dataset = load_dataset('cognitivecomputations/dolphin-2.9.3')
# Load the model and tokenizer
model_name = 'meta-llama/C... |
hackpilot.py | D:\GitHub\ai_train\notgpl\ai\aipy\hackpilot.py | ai\aipy\hackpilot.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
import deepspeed
from transformers import AutoModelForCausalLM, AutoTokenizer
# DeepSpeed Configuration
deepspeed_config = {
"train_batch_size": 8,
"gradient_accumulation_steps": 2,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.00015,
... |
hybrid.py | D:\GitHub\ai_train\notgpl\ai\aipy\hybrid.py | ai\aipy\hybrid.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
from fastapi import FastAPI
from pydantic import BaseModel
import random
# Login to Hugging Face Hu... |
json2cvs.py | D:\GitHub\ai_train\notgpl\ai\aipy\json2cvs.py | ai\aipy\json2cvs.py | Python | N/A | Functionality description extraction logic here | import json
import pandas as pd
import re
def remove_non_ascii(text):
return re.sub(r'[^\x00-\x7F]+', '', text)
def json_to_csv(json_file, csv_file):
# Load JSON data with UTF-8 encoding
try:
with open(json_file, 'r', encoding='utf-8') as f:
data = json.load(f)
except UnicodeDecode... |
llama3-thestack2_trainer.py | D:\GitHub\ai_train\notgpl\ai\aipy\llama3-thestack2_trainer.py | ai\aipy\llama3-thestack2_trainer.py | Python | N/A | Functionality description extraction logic here | import os
import logging
import torch
from transformers import LlamaForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset
from torch.cuda.amp import autocast
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Define model and tokenizer pat... |
main_script.py | D:\GitHub\ai_train\notgpl\ai\aipy\main_script.py | ai\aipy\main_script.py | Python | N/A | Functionality description extraction logic here | import os
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
from fastapi import FastAPI
from pydantic import BaseModel
# Login to Hugging Face Hub
login(token=... |
modelconv.py | D:\GitHub\ai_train\notgpl\ai\aipy\modelconv.py | ai\aipy\modelconv.py | Python | N/A | Functionality description extraction logic here | import torch
from safetensors.torch import load_file, save_file
# Load the safetensors model
safetensors_path = 'F:\AI\mia4o-bloom\model.safetensors'
state_dict = load_file(safetensors_path)
# Save the state_dict as a PyTorch model
pytorch_model_path = 'F:\AI\mia4o-bloom\pytorch_model.bin'
torch.save(state_dict, pyto... |
models.py | D:\GitHub\ai_train\notgpl\ai\aipy\models.py | ai\aipy\models.py | Python | N/A | Functionality description extraction logic here | from . import db
from flask_login import UserMixin
from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
from flask import current_app
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(150), unique=True, nullable=False)
password = ... |
routes.py | D:\GitHub\ai_train\notgpl\ai\aipy\routes.py | ai\aipy\routes.py | Python | N/A | Functionality description extraction logic here | from flask import Blueprint, request, jsonify, current_app
from flask_jwt_extended import create_access_token, jwt_required, get_jwt_identity
from werkzeug.security import generate_password_hash, check_password_hash
from .models import User
from . import db
import requests
routes = Blueprint('routes', __name__)
# Def... |
sddbllmtrainer.py | D:\GitHub\ai_train\notgpl\ai\aipy\sddbllmtrainer.py | ai\aipy\sddbllmtrainer.py | Python | N/A | Functionality description extraction logic here | import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
from datasets import load_dataset
# Prompt the user for the model directory and output directory
model_name = input("Enter the name or path of the pre-trained model: ")
output_dir = input("Enter the pat... |
start.py | D:\GitHub\ai_train\notgpl\ai\aipy\start.py | ai\aipy\start.py | Python | N/A | Functionality description extraction logic here | import os
from transformers import AutoTokenizer, TrainingArguments
from datasets import load_from_disk
from unsloth import FastLanguageModel
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
# Get user inputs for paths
MODEL_ID = input("Enter the model location (e.g., 'unsloth/gemma-7b-bnb-4bit'): ")
TRAINI... |
startj.py | D:\GitHub\ai_train\notgpl\ai\aipy\startj.py | ai\aipy\startj.py | Python | N/A | Functionality description extraction logic here | import os
import json
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
from datasets import Dataset
from huggingface_hub import login
# Log in to Hugging Face Hub
login(token="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")
# Get the model and JSON file... |
test.py | D:\GitHub\ai_train\notgpl\ai\aipy\test.py | ai\aipy\test.py | Python | N/A | Functionality description extraction logic here | |
train.py | D:\GitHub\ai_train\notgpl\ai\aipy\train.py | ai\aipy\train.py | Python | N/A | Functionality description extraction logic here | import os
import random
from datasets import load_dataset, DatasetDict
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from huggingface_hub import HfApi, Repository
# Step 1: Load the dataset
def load_data(data_dir):
data_files = {
"train": os.path.join(data_dir, "t... |
tweet2dataset.py | D:\GitHub\ai_train\notgpl\ai\aipy\tweet2dataset.py | ai\aipy\tweet2dataset.py | Python | N/A | Functionality description extraction logic here | import json
import os
def extract_relevant_info(tweet):
"""Extract relevant information from a tweet."""
tweet_id = tweet["id_str"]
prompt = tweet["full_text"]
response = "" # For tweets, we might not have an explicit response
metadata = {
"created_at": tweet["created_at"],
"user_m... |
tweets2miadataset.py | D:\GitHub\ai_train\notgpl\ai\aipy\tweets2miadataset.py | ai\aipy\tweets2miadataset.py | Python | N/A | Functionality description extraction logic here | import json
import os
def load_tweets(tweets_path):
with open(tweets_path, 'r', encoding='utf-8') as f:
tweets = json.load(f)
return tweets
def convert_tweets_to_character(tweets):
character_data = {
"name": "Nya GPT",
"description": "An inquisitive and helpful AI designed to provi... |
utils.py | D:\GitHub\ai_train\notgpl\ai\aipy\utils.py | ai\aipy\utils.py | Python | N/A | Functionality description extraction logic here | # Utility functions can be added here
|
__init__.py | D:\GitHub\ai_train\notgpl\ai\aipy\__init__.py | ai\aipy\__init__.py | Python | N/A | Functionality description extraction logic here | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager
import os
from dotenv import load_dotenv
from flask_jwt_extended import JWTManager
jwt = JWTManager()
db = SQLAlchemy()
login_manager = LoginManager()
def create_app():
load_dotenv() # Load environment variables f... |
.eslintignore | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\.eslintignore | discord\activities\embedded-app-sdk\.eslintignore | unknown | N/A | Functionality description extraction logic here | node_modules
output
# Need to upgrade eslint to support es modules
rollup.config.mjs
scripts/syncRPCSchema.mjs
|
.eslintrc.json | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\.eslintrc.json | discord\activities\embedded-app-sdk\.eslintrc.json | unknown | N/A | Functionality description extraction logic here | {
"root": true,
"plugins": ["promise", "import", "@typescript-eslint", "prettier"],
"env": {
"es6": true,
"browser": true,
"node": true
},
"parserOptions": {
"sourceType": "module"
},
"extends": ["plugin:import/typescript", "prettier"],
"rules": {
"prettier/prettier": "error",
"c... |
.gitignore | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\.gitignore | discord\activities\embedded-app-sdk\.gitignore | unknown | N/A | Functionality description extraction logic here | node_modules
output
*.log
.DS_Store
tmp
*.tsbuildinfo
|
.npmrc | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\.npmrc | discord\activities\embedded-app-sdk\.npmrc | unknown | N/A | Functionality description extraction logic here | include-workspace-root=true
|
.prettierrc | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\.prettierrc | discord\activities\embedded-app-sdk\.prettierrc | unknown | N/A | Functionality description extraction logic here | {
"printWidth": 120,
"bracketSpacing": false,
"singleQuote": true,
"jsxBracketSameLine": true,
"overrides": [
{
"files": ["*.ts", "*.tsx"],
"options": {
"parser": "typescript"
}
}
]
}
|
jest.config.ts | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\jest.config.ts | discord\activities\embedded-app-sdk\jest.config.ts | TypeScript | N/A | Functionality description extraction logic here | import type {Config} from '@jest/types';
export default (): Config.InitialOptions => {
return {
globals: {
'ts-jest': {
tsconfig: 'tsconfig.json',
},
},
preset: 'ts-jest',
testEnvironment: 'jsdom',
moduleFileExtensions: ['ts', 'js'],
transform: {
'^.+\\.(ts|tsx)$': '... |
LICENSE.md | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\LICENSE.md | discord\activities\embedded-app-sdk\LICENSE.md | unknown | N/A | Functionality description extraction logic here | MIT License
Copyright (c) 2024 Discord Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, dist... |
package.json | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\package.json | discord\activities\embedded-app-sdk\package.json | unknown | N/A | Functionality description extraction logic here | {
"name": "@discord/embedded-app-sdk",
"version": "1.0.0",
"description": "@discord/embedded-app-sdk enables you to build rich, multiplayer experiences inside Discord.",
"author": "Discord",
"license": "MIT",
"bugs": {
"url": "https://github.com/discord/embedded-app-sdk/issues"
},
"homepage": "https... |
patch-url-mappings.md | D:\GitHub\ai_train\notgpl\discord\activities\embedded-app-sdk\patch-url-mappings.md | discord\activities\embedded-app-sdk\patch-url-mappings.md | unknown | N/A | Functionality description extraction logic here | ## patchUrlMappings
Activities in the Discord ecosystem are “sandboxed” via a discord proxy. This is done to hide the users’ IP addresses as well as block urls from known malicious endpoints. To achieve this, the developer portal has a section for embedded applications called "URL Mappings". One edge-case of URL mappi... |
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