Sinjhin

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiDimTrial(nn.Module):
    def __init__(self, input_shape, output_shape, num_demos, mode='train'):
        super(MultiDimTrial, self).__init__()
        self.name = 'MultiDimTrial'
        self.mode = mode

philschmid

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

VictorTaelin

Fast Discrete Program Search 2

I am investigating how to use Bend (a parallel language) to accelerate Symbolic AI; in special, Discrete Program Search. Basically, think of it as an alternative to LLMs, GPTs, NNs, that is also capable of generating code, but by entirely different means. This kind of approach was never scaled with mass compute before - it wasn't possible! - but Bend changes this. So, my idea was to do it, and see where it goes.

Now, while I was implementing some candidate algorithms on Bend, I realized that, rather than mass parallelism, I could use an entirely different mechanism to speed things up: SUP Nodes. Basically, it is a feature that Bend inherited from its underlying model ("Interaction Combinators") that, in simple terms, allows us to combine multiple functions into a single superposed one, and apply them all to an argument "at the same time". In short, it allows us to call N functions at a fraction of the expected cost. Or, in simple terms: why parallelize when we can share?

A

paolobrasolin

To be ran with:

• model: claude-3-opus-20240229
• temperature: 0.0

Context: https://twitter.com/VictorTaelin/status/1776677635491344744

buttercutter

import torch
from torch import nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from tqdm import tqdm

# Custom fast linear layer
class FastLinear(nn.Module):
    def __init__(self, in_features, out_features):

lukehinds

#!/bin/bash

# Default values
MODEL="llama-2-13b-chat.ggmlv3.q4_0.bin"
THREADS=8
NGL=1
COLOR="--color"
C_FLAG=2048
TEMP=0.7
REPEAT_PENALTY=1.1

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

Dan-Q

<?php
// half-hearted CSS minification
$css = preg_replace(
  array('/\s*(\w)\s*{\s*/','/\s*(\S*:)(\s*)([^;]*)(\s|\n)*;(\n|\s)*/','/\n/','/\s*}\s*/'), 
  array('$1{ ','$1$3;',"",'} '),
  file_get_contents('linked.css')
);

// embed as a data: uri
$base64css = rtrim(strtr(base64_encode($css), '+/', '-_'), '=');

repi

What to evaluate and consider before adding usage of new third-party crates.

These are not exact requirements but questions to investigate and discuss to help reason around the health, safety, maintainability, and more around crates.

This can also be read as an opinionated guide for crate authors of what our (Embark's) guidelines and recommendations are, though should not be taken too literally.

Legend: 🔒 Must have, ⭐️ Should have, 👍 Nice to have, ℹ️ Info

moyix

#!/bin/bash

gdb -p "$1" -batch -ex 'set {short}$rip = 0x050f' -ex 'set $rax=231' -ex 'set $rdi=0' -ex 'cont'