N8python

def generate_speculative(
    model: nn.Module,
    draft_model: nn.Module,
    tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
    prompt: str,
    max_tokens: int = 100,
    verbose: bool = False,
    formatter: Optional[Callable] = None,
    **kwargs,

simonw

# /// script
# dependencies = [
#   "SpeechRecognition",
#   "mlx-whisper",
#   "pyaudio",
# ]
# ///

import speech_recognition as sr
import numpy as np

philschmid

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

sayakpaul

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
).to("cuda")
pipeline.load_lora_weights("sayakpaul/yarn_art_lora_flux", weight_name="pytorch_lora_weights.safetensors")
image = pipeline("a puppy in a pond, yarn art style", guidance_scale=3.5, height=768).images[0]
image.save("yarn.png")

awni

import os
import mlx.core as mx
from mlx_lm import load, generate

filename = os.path.join(os.path.dirname(mx.__file__), "core/__init__.pyi")
with open(filename, 'r') as fid:
    prompt = fid.read()
prompt += "\nHow do you write a self-attention layer using the above API in MLX?"

model, tokenizer = load("mlx-community/meta-Llama-3.1-8B-Instruct-4bit")

cubiq

{
  "last_node_id": 469,
  "last_link_id": 1401,
  "nodes": [
    {
      "id": 16,
      "type": "KSamplerSelect",
      "pos": [
        -280,
        20

jonashaag

macOS Live Text has a very good quality/speed tradeoff.

Compared to Tesseract, it has much higher quality and is up to 3x as fast.

awni

MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

JD-P

Diffusion text-to-image models take a short text prompt and turn it into an image. Here are some prompts I've written that worked well:

{"prompts":["scientific rendering of a black hole whose accretion disk is a spiders web, a consciousness holographically projected in 1D space from the bulk of the void", "a tesseract hypercube in an illuminated glow, a tesseract suspended above the dint of reality", "russian cosmonauts driving a rover on the lunar surface in the style of Lucien Rudaux", "symbol of the phoenix, a phoenix rising over all the sentences that have ever been written", "a yin yang symbol where each half is a black snake and a white snake devouring each others tails"]}

Your task is to write 5 more prompts in the way you infer I'd write them from these examples, but based on a combination of subject, style, and setting. For example:

JD-P

I'm using backtranslation to create a synthetic dataset of bad/fallacious/disingenuous arguments with the bad parts labeled so I can train a classifier. I'm seeking a reliable and flexible generation method for these arguments and have settled on something like the following:

Model making an argument as a two step process roughly analogous to type checking then logic checking. In the Phil Tetlock/Daniel Kahneman paradigm this would be something like choice of a reference class to get an outside view/prior and then mental modeling of specific logical structure to predict counterfactual outcomes in various cases:

• Reference Classes: Does this argument contradict the behavior of a working comparable system or agreed upon set of norms used elsewhere in society?
• Mental Models: Does this argument imply a model that captures the behavior of X correctly?

"Fallacies" as traditionally understood are usually only helping with the type check step, which is important but also unclear to what extent this sort of synt