千橙 iqiancheng

1. 常用药物

感冒/发烧类

布洛芬（退烧、止痛）
对乙酰氨基酚（泰诺）

抗病毒/流感类

奥司他韦（达菲，需处方）

消炎类

阿莫西林（需处方）
红霉素软膏（外用）

说明

如何让命令使用代理服务器

前提

# 1. 代理类型
* http ------ 远程域名解析 -- 建议
* socks  ----
* socks4 ---- 本地域名解析
* socks4a --- 远程域名解析 -- 建议

Are multi-LLM-agent systems a thing? Yes they are. But.

Yoav Goldberg, Nov 24, 2024

This piece started with a pair of twitter and bluesky posts:

let's talk about "agents" (in the LLM sense). there's a lot of buzz around "multi-agent" systems where agents collaborate but... i don't really get how it differs from a thinking of a single agent with multiple modes of operation. what are the benefits of modeling as multi-agent?
— (((ل()(ل() 'yoav))))👾 (@yoavgo) November 23, 2024

When loading the LoRA params (that were obtained on a quantized base model) and merging them into the base model, it is recommended to first dequantize the base model, merge the LoRA params into it, and then quantize the model again. This is because merging into 4bit quantized models can lead to some rounding errors. Below, we provide an end-to-end example:

First, load the original model and merge the LoRA params into it:

from diffusers import FluxPipeline 
import torch 

ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline = FluxPipeline.from_pretrained(

the sneakyf1shy apple intelligence tutorial v2.0

Warning

This is patched as of iOS/iPadOS 18.1 DevBeta 5. If you want to follow this, stay on Beta 4.

This actually downloads the models, and is NOT just new SiriUI. Hence, this process is complex and probably not worth it.

⚠️ Prepare to be disappointed and annoyed, and have your time wasted! ⚠️

What does not work: Writing Tools, Memories, Reduce Interruptions, Image Eraser and other tools that are within official Apple Intelligence on supported devices.

Is telling a model to "not hallucinate" absurd?

Can you tell an LLM "don't hallucinate" and expect it to work? my gut reaction was "oh this is so silly" but upon some reflection, it really isn't. There is actually no reason why it shouldn't work, especially if it was preference-fine-tuned on instructions with "don't hallucinate" in them, and if it a recent commercial model, it likely was.

What does an LLM need in order to follow an instruction? It needs two things:

an ability to perform then task. Something in its parameters/mechanism should be indicative of the task objective, in a way that can be influenced. (In our case, it should "know" when it hallucinates, and/or should be able to change or adapt its behavior to reduce the chance of hallucinations.)
an ability to ground the instruction: the model should be able to associate the requested behavior with its parameters/mechanisms. (In our case, the model should associate "don't hallucinate" with the behavior related to 1).

	// ==UserScript==
	// @name URL Tagger
	// @namespace http://tampermonkey.net/
	// @version 1.5
	// @description Tag URLs based on predefined patterns and data
	// @author Your Name
	// @match :///*
	// @grant GM_xmlhttpRequest
	// @grant GM_addStyle
	// @connect 192.168.x.x

	import inspect
	from typing import Any, Callable, Dict, List, Optional, Union

	import numpy as np
	import torch
	from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import FluxLoraLoaderMixin
	from diffusers.models.autoencoders import AutoencoderKL


	def groupedSimilarFilenames(filenames):
	'''
	将相似文件名分组\n
	输出类似格式\n
	filenames preffix suffix size \n
	0 cover.jpg NaN NaN 0 \n
	1 top.png NaN NaN 0 \n
	2 9.jpg NaN .jpg 0 \n
	3 015a.jpg 0 a.jpg 1 \n

	from diffusers import StableDiffusion3Pipeline
	from transformers import T5EncoderModel
	import torch
	import time
	import gc

	def flush():
	gc.collect()
	torch.cuda.empty_cache()