odellus

SearXNG setup

Step 1 - Install SearXNG

Open a terminal in MacOS or Linux [if you are using Windows consider your life choices up to this point and download a real operating system please] and do the following

They have detailed instructions [elsewhere][] on how to do this but I am trying to keep this self-contained.

mkdir -p ~/some/path/for/searxng/searxng

ChrisHayduk

"""

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

Thanks for the contributions guys!

"""

import torch
import peft

moyix

MONGODB_URL=mongodb://localhost:27017
HF_ACCESS_TOKEN=<REDACTED>

# 'name', 'userMessageToken', 'assistantMessageToken' are required
MODELS=`[
  {
    "endpoints": [{"url": "http://localhost:8081"}],
    "name": "WizardLM/WizardLM-70B-V1.0",
    "description": "WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions",
    "websiteUrl": "https://huggingface.co/WizardLM/WizardLM-70B-V1.0",

Birch-san

from transformers import (
  AutoConfig,
  AutoTokenizer,
  BitsAndBytesConfig,
  GenerationConfig,
  AutoModelForCausalLM,
  LlamaTokenizerFast,
  PreTrainedModel,
  TextIteratorStreamer,
  StoppingCriteria,

odellus

# coding=utf-8
# 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 applicable law or agreed to in writing, software

younesbelkada

# coding=utf-8
# 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 applicable law or agreed to in writing, software

eusip

import transformers
import argparse
import numpy as np
import pandas as pd

from huggingface_hub import HfFolder

import evaluate
from datasets import load_dataset, Dataset, load_metric, concatenate_datasets, DatasetDict
from transformers import AutoModelForCausalLM, AutoTokenizer

moyix

Using Linear Algebra to Convert a Large Code Model

Background

The SalesForce CodeGen models are a family of large language models trained on a large amount of natural language data and then fine-tuned on specialized datasets of code. Models of size 350M, 2B, 6B, and 16B parameters are provided in three flavors:

• nl, the base model trained on The Pile, a large natural language dataset compiled by EleutherAI
• multi, which is fine-tuned from the nl model on a dataset of code in multiple languages, scraped from GitHub, and
• mono, which is fine-tuned from the multi model on Python code only.

moyix

#!/usr/bin/env python

import argparse
import torch
from transformers import GPTJForCausalLM, GPTJConfig
# Note: these need the git version of Transformers as of 7/22/2022
from transformers import CodeGenTokenizer, CodeGenForCausalLM
from transformers import CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST

parser = argparse.ArgumentParser('Convert SalesForce CodeGen model to GPT-J')

dvschultz

def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
    '''
    Spherical linear interpolation
    Args:
        t (float/np.ndarray): Float value between 0.0 and 1.0
        v0 (np.ndarray): Starting vector
        v1 (np.ndarray): Final vector
        DOT_THRESHOLD (float): Threshold for considering the two vectors as
                               colineal. Not recommended to alter this.
    Returns: