cooperleong00

# %%
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import random
random.seed(42)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset

# %%

sayakpaul

import torch
from diffusers import FluxTransformer2DModel
import torch.utils.benchmark as benchmark
from torchao.quantization import quantize_, int8_weight_only
from torchao.utils import unwrap_tensor_subclass
import torch._inductor

torch._inductor.config.mixed_mm_choice = "triton"

ariG23498

# Taken from: https://gist.github.com/sayakpaul/23862a2e7f5ab73dfdcc513751289bea

from diffusers import FluxPipeline, FluxTransformer2DModel
from transformers import T5EncoderModel
import torch
import gc


def flush():
    gc.collect()

ariG23498

philipturner

//
//  main.swift
//  UnifiedGEMMKernel
//
//  Created by Philip Turner on 5/29/24.
//

import Metal
#if os(macOS)
import IOKit

bartowski1182

In addition to a significant decrease in hepatic lipid accumulation in the IOE group, which inhibited energy intake by propionate enrichment, hepatic lipids were also significantly reduced in the mice in the IOP group, which was largely enriched with butyrate. Compared with the IOE group, IOP had a stronger regulatory effect on hepatic metabolism and triglyceride metabolism and higher levels of TCA cycle in the host. In addition, butyrate has the ability to promote browning of white adipose tissue (WAT) to brown adipose tissue (BAT).^[@ref39],[@ref40]^ WAT stores energy, whereas BAT uses energy for heating and consequently host energy expenditure increases.^[@ref41],[@ref42]^ However, adipose tissue weight does not change after WAT browning.^[@ref43]^ Therefore, the weight of adipose tissue of mice in the IOP group dominated by butyrate was greater than that of the mice in the IOE group dominated by propionate.

In conclusion ([Figure [7](#fig7){ref-type="fig"}](#fig7){ref-type="fig"}C), the improvement of ob

JD-P

Alignment

I did a podcast with Zvi after seeing that Shane Legg couldn't answer a straightforward question about deceptive alignment on a podcast. Demis Hassabis was recently interviewed on the same podcast and also doesn't seem able to answer a straightforward question about alignment. OpenAI's "Superalignment" plan is literally to build AGI and have it solve alignment for us. The public consensus seems to be that "alignment" is a mysterious pre-paradigmatic field with a lot of [open problems](https://www.great

seanchatmangpt

import ast

import dspy

from dspygen.utils.dspy_tools import init_dspy


def get_current_weather(location: str, temperature_unit: str) -> str:
    """
    Get the current weather for a given location in the specified format.

seanchatmangpt

import ast
import inspect
import dspy
from typing import Type

from pydantic import BaseModel


class User(BaseModel):
    name: str

seanchatmangpt

import ijson
import tiktoken

# Define the path to your large JSON file
json_file_path = "data/conversations.json"


from pydantic import BaseModel, Field, ValidationError
from typing import List, Optional, Any