kouroshHakha

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from pprint import pprint
import time
import gc
import matplotlib.pyplot as plt
import numpy as np


model_base = "gpt2"

kouroshHakha

import torch
import torch.nn.functional as F
import unittest
import xformers.ops as xops
import math
import time

MAX_ITER = 100

kouroshHakha

(ray) kourosh@kourosh-JRFKXJ33VL auto_prompting % python main.py
============== Trial 1 ===============
Current Prompt format:

I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show.

Failed Example:
Input:
pwd
Output:

kouroshHakha

import numpy as np
import pandas as pd
import os

from ray.train.huggingface import HuggingFacePredictor
import pandas as pd

import re

kouroshHakha

# Install the newest triton version with
# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python"
import pickle
import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from einops import rearrange, repeat

kouroshHakha

import torch
import torch.nn.functional as F

# Create random inputs for testing
batch_size = 128
seq_length = 512
embed_dim = 64  
enable_math = False

query = torch.rand(batch_size, seq_length, embed_dim, device="cuda", requires_grad=True)

kouroshHakha

from safetensors import safe_open
import torch
import numpy as np
import matplotlib.pyplot as plt

tensors = {}
model_ckpt = "/home/ray/default/7b-chat-lora-ckpt/adapter_model.safetensors"
with safe_open(model_ckpt, framework="pt") as f:
    for k in f.keys():
        tensors[k] = f.get_tensor(k)

kouroshHakha

import datasets
import re
import json
import tqdm

ds = datasets.load_dataset("glaiveai/glaive-function-calling-v2", split="train")

out_ds_size = 100

class UserAssistantNotFoundError(Exception):

kouroshHakha

import ray
import torch
import time
import numpy as np
from ray.util.collective.types import Backend
from ray.util.collective.const import get_store_name
import ray.util.collective as collective
import os

@ray.remote(num_gpus=1)