pszemraj

sudo apt-get update && sudo apt upgrade -y
sudo apt-get install -y poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools

git clone https://github.com/allenai/olmocr.git --depth 1
cd olmocr
pip install -q ninja
pip install -e .[gpu] --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/

# clean up
pip cache purge && apt autoremove -y

pszemraj

import math
import torch
import torch.nn as nn
import torch.nn.functional as F


class LayerNormScaling(nn.Module):
    """
    LayerNorm Scaling implementation to mitigate the Curse of Depth in LLMs.
    

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 = "./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: