probonopd

opkg install luci-lib-json luci rng-tools usbutils avrdude avahi-daemon

# If rng-tools is not installable, then install it by hand from e.g., http://download.linino.org/linino_distro/linino_dev/latest/packages/rng-tools_3-2_ar71xx.ipk

# Edit your /etc/opkg.conf, add
src/gz barrier_breaker http://download.linino.org/dogstick/all-in-one/latest/packages/
# Comment out your earlier src/gz

opkg update
opkg list|grep bridge

jsheedy

import io
import sys

class IteratorFile(io.TextIOBase):
    """ given an iterator which yields strings,
    return a file like object for reading those strings """

    def __init__(self, it):
        self._it = it
        self._f = io.StringIO()

Jan-Zeiseweis

create or replace function f_sk_date (ts timestamp )
  returns integer
  stable as $$
    if not ts:
      return None
    return int(str(ts)[0:10].replace('-',''))
  $$ language plpythonu;

create or replace function f_date (ts timestamp)
  returns date

aparrish

aanari

# -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

hagope

set tabstop=4
set softtabstop=4
set shiftwidth=4
set textwidth=79
set expandtab
set autoindent
set number
set fileformat=unix
set list
set listchars=tab:>-

rain-1

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.