sroecker

import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from itertools import permutations
from collections import Counter

# Define the Thomas attractor system
def thomas_derivatives(t, u, b):
    x, y, z = u

sroecker

#!/usr/bin/env bash
# based off of https://gist.github.com/rauchg/c5f0b1dc245ad95c593de8336aa382ac?permalink_comment_id=4842642#gistcomment-4842642
# further adapted from https://gist.github.com/algal/5e815ffd371d57d3a6a37056db1bd281
if [ "$#" -eq 0 ]; then
    echo "Usage: $(basename $0) promt_to_send_to_perplexity"
    echo ""
    echo "  Requirements: PERPLEXITY_API, defined; jq and curl, installed; bash, version 3 or higher."
    exit 1
fi

sroecker

from distilabel.steps import LoadDataFromDicts
from distilabel.steps.tasks import EvolInstruct
from distilabel.pipeline import Pipeline
from distilabel.llms import OllamaLLM
import pprint

# Set up the pipeline
with Pipeline(name="evol-instruct-example") as pipeline:

    # Load initial instructions

sroecker

import modal

app = modal.App(name="moondream-label-datikz_v2")
data_dict = modal.Dict.from_name("HF_DATASET", create_if_missing=True)

def download_dataset():
   from datasets import load_dataset
   
   data_dict["HF_DATASET"] = "nllg/datikz-v2"
   dataset = load_dataset(data_dict["HF_DATASET"])

sroecker

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import xxhash
from tqdm import tqdm

# load moondream model
model_id = "vikhyatk/moondream2"
revision = "2024-05-20"

model = AutoModelForCausalLM.from_pretrained(

sroecker

import modal

app = modal.App(name="pokemon-eval")

moondream_image = modal.Image.micromamba(
    python_version="3.11"
).apt_install(
    "git"
).micromamba_install(
    "cudatoolkit",

sroecker

podman run --rm --device nvidia.com/gpu=all --security-opt=label=disable --mount type=bind,src="${PWD}",target=/workspace/axol  
otl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1 accelerate launch -m axolotl.cli.train examples/openllam  
a-3b/lora.yml

sroecker

#!/bin/bash
git clone https://github.com/continuedev/continue
npm config set prefix ~/.local
sh continue/install-dependencies.sh
cd continue/extensions/vscode
node scripts/prepackage.js
npm install @vscode/vsce
PATH=$PATH:~/.local/bin/
npm install yarn
node scripts/package.js

sroecker

import torch

print("pytorch version: " + torch.__version__)
print("CUDA available: " + str(torch.cuda.is_available()))
print("device count: " + str(torch.cuda.device_count()))
print("current device: " + str(torch.cuda.current_device()))
print("device name: " + torch.cuda.get_device_name(torch.cuda.current_device()))
print("backend:")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

sroecker

import json
import requests
import collections

class BearerAuth(requests.auth.AuthBase):
    def __init__(self, token):
        self.token = token
    def __call__(self, r):
        r.headers['Authorization'] = 'Bearer ' + self.token
        return r