metacollin

# generated by Slic3r 1.37.2-prusa3d on Thu Dec  7 09:48:20 2017
# Figured out by trial and error engineer metacollin
# Released as public domain.	
# USE GLUESTICK FOR PRINT BED ADHESION
avoid_crossing_perimeters = 0
bed_shape = 0x0,250x0,250x210,0x210
bed_temperature = 110
before_layer_gcode = ;BEFORE_LAYER_CHANGE\n;[layer_z]\n\n
bottom_solid_layers = 8
bridge_acceleration = 1000

padeoe

🤗Huggingface Model Downloader

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, this command-line tool smartly utilizes wget or aria2 for LFS files and git clone for the rest.

Features

• ⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
• 🚀 Multi-threaded Download: Utilize multiple threads to speed up the download process.
• 🚫 File Exclusion: Use --exclude or --include to skip or specify files, save time for models with duplicate formats (e.g., *.bin or *.safetensors).
• 🔐 Auth Support: For gated models that require Huggingface login, use --hf_username and --hf_token to authenticate.
• 🪞 Mirror Site Support: Set up with HF_ENDPOINT environment variable.

kalomaze

Simple Llama + SillyTavern Setup Guide

This guide is meant for Windows users who wish to run Facebook's Llama AI language model on their own PC locally. Our focus will be on character chats, reminiscent of platforms like character.ai / c.ai, using Llama architecture models. Most recently, in late 2023 and early 2024, Mistral AI has released high quality models that are based of the Llama architecture, and will work in the same way if you choose to use them.

• Requirements

  • Windows operating system (may make Mac version of the guide later)
  • GPU with at least a few gigabytes of VRAM (NVIDIA graphics cards recommended)

• Sufficient regular RAM for a model (system memory)

davidmezzetti

import re

from txtai import Embeddings, LLM

# Prompt courtesy of the following link: https://github.com/codelion/optillm/blob/main/cot_reflection.py
def cot(system, user):
    system = f"""
        {system}

        You are an AI assistant that uses a Chain of Thought (CoT) approach with reflection to answer queries. Follow these steps: