stelterlab

# Derived SM121 (GB10) vLLM image with the Aleph Alpha Kolibri proprietary plugin.
#
# Base: eugr/spark-vllm:latest — a native GB10 (SM121/12.1a) vLLM build produced
# by eugr/spark-vllm-docker (FlashInfer + vLLM compiled for the Blackwell 40-bit SoC).
#
# The Aleph Alpha plugin (aleph-alpha-inference 1.0.0) is a pure-Python
# vllm.general_plugins entry point that adds Kolibri1ForCausalLM plus the
# kolibri1 reasoning/tool parsers. It is version-pinned to vllm>=0.29,<0.30, so
# we install it with --no-deps to keep the SM121-compiled vLLM/torch intact and
# only --override the declared torch/transformers bounds instead of letting pip

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

davidberenstein1957

# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
#     "duckdb", sentence-transformers, huggingface_hub
# ]
# ///
from sentence_transformers import SentenceTransformer
import duckdb
from huggingface_hub import get_token

ficstamas

from transformers import ElectraForMaskedLM, ElectraForPreTraining
from transformers.models.electra.modeling_electra import ElectraForPreTrainingOutput
from torch import Tensor
import torch.nn.functional as F
import torch.nn as nn
import torch


class ElectraModel(nn.Module):
    def __init__(self,

BramVanroy

# See https://gist.github.com/BramVanroy/f78530673b1437ed0d6be7c61cdbdd7c
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments, HyperOptArguments))

try:
    # Assumes that the first .json file is the config file (if any)
    config_file = next(iter(arg for arg in sys.argv if arg.endswith(".json")))
except StopIteration:
    config_file = None

run_name_specified = False

MidSpike

CVE-2022-23812

---

RIAEvangelist/node-ipc is malware / protestware

The RIAEvangelist/node-ipc module contains protestware peacenotwar.

Excerpt from RIAEvangelist/node-ipc:

as of v11.0.0 & v9.2.2 this module uses the peacenotwar module.

LilithWittmann

Betreff: Berechtigungszertifikat für NPA An: VERTRIEB@bdr.de

Text: Sehr geehrte Damen und Herren, ich bin am Erwerb eines Berechtigungszertifikats für den neuen Personalausweis interessiert. Können Sie mir bitte ihre aktuelle Preisliste zukommen lassen?

Vielen Dank & viele Grüße,

[NAME]

Fluepke

; <<>> DiG 9.16.21 <<>> axfr 219.185.195.in-addr.arpa @91.189.192.6
;; global options: +cmd
; Transfer failed.

yoavg

A criticism of "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big"

Yoav Goldberg, Jan 23, 2021.

The FAccT paper "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big" by Bender, Gebru, McMillan-Major and Shmitchell has been the center of a controversary recently. The final version is now out, and, owing a lot to this controversary, would undoubtly become very widely read. I read an earlier draft of the paper, and I think that the new and updated final version is much improved in many ways: kudos for the authors for this upgrade. I also agree with and endorse most of the content. This is important stuff, you should read it.

However, I do find some aspects of the paper (and the resulting discourse around it and around technology) to be problematic. These weren't clear to me when initially reading the first draft several months ago, but they became very clear to me now. These points are for the most part

stephenroller

#!/usr/bin/env python3

"""
Example of a generic Mixout implementation. (Lee et al., 2019).
https://arxiv.org/abs/1909.11299

Implementation by Stephen Roller (https://stephenroller.com).

Updated 2020-02-10 to include 1/(1 - p) correction term. Thanks to
Cheolhyoung Lee for making this correction.