bigsnarfdude

https://web.stanford.edu/class/cs25/

https://www.youtube.com/watch?v=mcep6W8oB1I https://www.youtube.com/watch?v=fz8wf9hN20c https://www.youtube.com/watch?v=wwQ1LQA3RCU https://www.youtube.com/watch?v=1GbDTTK3aR4 https://www.youtube.com/watch?v=ylEk1TE1uBo https://www.youtube.com/watch?v=mE7IDf2SmJg https://www.youtube.com/watch?v=ckNMsUuLryM

bigsnarfdude

bigsnarfdude

what is rope important in llm

Rope is important in Large Language Models (LLMs) like ChatGPT in a metaphorical sense. The term "rope" isn't literally used in the technology or structure of LLMs, but understanding its metaphorical application can be insightful.

1. Complexity and Interconnectedness: Just as a rope is made of many intertwined fibers, an LLM is composed of multiple layers and connections within its neural network. Each thread or fiber in a rope contributes to its overall strength and functionality, much like how each neuron and layer in an LLM contributes to its ability to process and generate language.

2. Guidance and Direction: Ropes are often used to guide, lead, or secure things. In a similar way, the training and design of an LLM guide how it processes information and generates responses. The training data, algorithms, and parameters act like ropes, directing the LLM towards accurate and relevant outputs.

bigsnarfdude

<start_of_turn>user

summarize the following text:

 Thank you very much to the organizers for inviting me to speak, which I guess came a
  long time ago.
  But I'm glad that we all made it here eventually.
  Yeah, so I'm sorry about two things, the first is to break a long streak of very nice chalk
  talk.
  I'm afraid during COVID I got very lazy, so I just give talks on my iPad now.

bigsnarfdude

Hardware Requirements


Method	Bits	7B	13B	30B	65B	8x7B
Full	16	160GB	320GB	600GB	1200GB	1000GB
Freeze	16	20GB	40GB	120GB	240GB	200GB
LoRA	16	16GB	32GB	80GB	160GB	120GB
QLoRA	8	10GB	16GB	40GB	80GB	80GB
QLoRA	4	6GB	12GB	24GB	48GB	32GB

bigsnarfdude

import os
from openai import OpenAI
import prompt



def truncate_words(input_string, max_words):
    words = input_string.split()
    truncated_words = words[:max_words]
    return ' '.join(truncated_words)

bigsnarfdude

This text appears to be a summary of a lecture or discussion focused on algebraic geometry, specifically dealing with complex varieties, schemes, and toric degenerations. Here's a breakdown of the key points:

1. Complex Varieties: The discussion primarily deals with algebraic varieties over complex numbers. However, it acknowledges that extending beyond the complex case is possible for those interested.

2. General Schemes and Spec of a Ring: The lecture isn't focused on general schemes but rather on specific cases, like the spectrum (Spec) of some ring in variables defined by an ideal. This is related to the concept of vanishing sets of ideals, i.e., sets of points where functions in the ideal vanish.

3. Vanishing Sets in (\mathbb{C}^n) and (\mathbb{C}^{n+1}): The speaker describes constructing varieties as vanishing sets of ideals within (\mathbb{C}^n) and (\mathbb{C}^{n+1}). They mention removing the zero vector from these sets and dividing by scaling, referring to a projective space

bigsnarfdude

import os
import sys

def clean_duplicates(file_name):
    duplicates = []
    cleaned = []
    with open(file_name, 'r') as f:
        sentences = f.readlines()
    for s in sentences:
        if s in cleaned:

bigsnarfdude

from llama_cpp import Llama
llm = Llama(
      model_path="/Users/vincent/development/llama.cpp/models/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
      n_gpu_layers=-1, # Uncomment to use GPU acceleration
      seed=1337,
      n_ctx=2048)
output = llm(
      "[INST] Question: Write a paper on the industrial revolution. Answer: [/INST]", # Prompt
      max_tokens=None, # Generate up to 32 tokens, set to None to generate up to the end of the context window
      #stop=["Q:", "\n"], # Stop generating just before the model would generate a new question

bigsnarfdude

import os
import sys

def clean_duplicates(file_name):
    duplicates = []
    cleaned = []
    with open(file_name, 'r') as f:
        sentences = f.readlines()
    for s in sentences:
        if s in cleaned: