karpathy

# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {

awni

hamelsmu

from fastapi import Request, HTTPException
from pydantic import BaseModel, BaseModel, HttpUrl
from modal import Secret, App, web_endpoint, Image
from typing import Optional, List
from example import proposal
import os

app = App(name="circleback", image=Image.debian_slim().pip_install("openai", "pydantic", "fastapi"))

class Attendee(BaseModel):

shreyshahi

# Code inspired from https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
# slerp function is entirely lifted from the above gist.

import torch
from diffusers import DiffusionPipeline
import numpy as np


def interpolate(v1, v2, step, total_steps):
    alpha = step / (total_steps - 1)

thomwolf

""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio
"""

from openai import OpenAI
import time

atiorh

# !pip install transformers sentencepiece

import torch
import torch.nn as nn
torch.set_grad_enabled(False)

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.activations import ReLUSquaredActivation

from collections import defaultdict, OrderedDict

Birch-san

from transformers import (
  AutoConfig,
  AutoTokenizer,
  BitsAndBytesConfig,
  GenerationConfig,
  AutoModelForCausalLM,
  LlamaTokenizerFast,
  PreTrainedModel,
  TextIteratorStreamer,
  StoppingCriteria,

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

jondurbin

Details on how the airoboros-gpt4-2.0 dataset was created.

Script

https://github.com/jondurbin/airoboros

pip install --upgrade airoboros==2.0.13

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin