pszemraj

Model	Average CR⬆️	AGIEval Mean (Min, Max)	AGIEval CR	MMLU-Pro Mean (Min, Max)	MMLU-Pro CR	Math Mean (Min, Max)	Math CR	#Params (B)
meta-llama/Llama-3.1-70B-Instruct	72.39	72.43, (65.34, 74.66)	81.79	66.63, (55.16, 70.68)	73.19	65.88, (64.58, 67.86)	62.18	0
mistralai/Mistral-Large-Instruct-2407	71.93	68.78, (61.41, 74.49)	75.77	65.1, (50.28, 69.23)	72.31	71.04, (69.66, 72.72)	67.71	0
meta-llama/Meta-Llama-3-70B-Instruct	69.11	69.71, (60.77, 71.2)	83.13	58.75, (49.3, 63.16)	75.24	51.29, (49.66, 54.2)	48.96	0
01-ai/Yi-1.5-34B-Chat	58.43	63.89

pszemraj

#!/usr/bin/env python
"""
CLI utility to quickly inspect the latest scalar values from TensorBoard logs.

Dependencies:
  pip install tbparse pandas fire tqdm

Usage:
  python tensorboard_inspect.py --logdir ./path/to/logs
"""

pszemraj

raspberry pi amd gpu

https://www.jeffgeerling.com/blog/2024/amd-radeon-pro-w7700-running-on-raspberry-pi

install prereqs

Install all needed dependencies for kernel compilation:

sudo apt install git bc bison flex libssl-dev make libc6-dev libncurses5-dev crossbuild-essential-arm64 libelf-dev

pszemraj

bash/zsh util for readerlm-v2

Warning

Using this will send the data of your (public URL) to jina ai, and the license for readerlm-v2 is cc-by-nc

Here's the improved version of your smart_curl function that better handles filename extraction and works in Zsh:

function smart_curl() {

pszemraj

import logging
from typing import Dict, List

import faiss
import numpy as np
from datasets import load_dataset
from sentence_transformers import CrossEncoder, SentenceTransformer

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

pszemraj

"""
util script for loading, basic processing, converting reddit posts -> hf dataset

https://arctic-shift.photon-reddit.com/download-tool
"""
import pandas as pd
from datasets import Dataset, load_dataset

src = "./data/r_LocalLLaMA_posts.jsonl" # update with relevant path
df = pd.read_json(src, lines=True).convert_dtypes()

pszemraj

import math
import torch
from torch.optim.adamw import AdamW


class CautiousAdamW(AdamW):
    """
    Implements Cautious AdamW optimizer by subclassing AdamW.

    All hyperparameters remain identical to AdamW.

pszemraj

def chibihash64__load64le(p, offset=0):
    """Load 8 bytes from the input bytes object as a little-endian 64-bit integer."""
    return int.from_bytes(p[offset : offset + 8], "little", signed=False)


def chibihash64(key, seed=0):
    """
    Compute the 64-bit hash of the input bytes object using the chibihash64 algorithm.

    Parameters:

pszemraj

"""
based on the megalodon implementation

https://github.com/XuezheMax/megalodon/blob/53cbaa3a3b3e05ea439564bd67cb352012ba6b97/megalodon/modules/complex_exponential_moving_average.py
"""

import math
import torch
from torch import nn
from typing import Optional, Tuple

pszemraj

import base64
import tempfile
from pathlib import Path

import fire
import mdformat
from joblib import Memory
from openai import OpenAI
from pdf2image import convert_from_path
from tqdm.auto import tqdm