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import torch | |
import os | |
import json | |
from safetensors.torch import load_file, save_file | |
def replicate_lora_a(name: str, weight: "torch.Tensor") -> dict[str, "torch.Tensor"]: | |
prefix, suffix = name.split('qkv_proj') | |
res = {} | |
for t in ['q_proj', 'k_proj', 'v_proj']: | |
name = f"{prefix}{t}{suffix}" |
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import os | |
import time | |
import requests | |
from requests.adapters import HTTPAdapter, Retry | |
from huggingface_hub import configure_http_backend | |
import argparse | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--model', type=str, help='name of huggingface model to download', default='sshleifer/tiny-gpt2') |
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import openai | |
import asyncio | |
from typing import Any | |
async def dispatch_openai_requests( | |
messages_list: list[list[dict[str,Any]]], | |
model: str, | |
temperature: float, | |
max_tokens: int, | |
top_p: float, |
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channels: | |
- anaconda | |
- pytorch | |
- nvidia | |
- conda-forge | |
dependencies: | |
- python=3.8 | |
- pip | |
- pytorch=1.13.1 | |
- torchvision |
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# note this is a copy from https://paste.ubuntu.com/p/Nx5CcSmhHn/ for convenience | |
import torch | |
import torch.nn.functional as F | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
def SimCSE_loss(pred, tau=0.05): | |
ids = torch.arange(0, pred.shape[0], device=device) | |
y_true = ids + 1 - ids % 2 * 2 | |
similarities = F.cosine_similarity(pred.unsqueeze(1), pred.unsqueeze(0), dim=2) |