zackangelo

<|begin_of_text|><|start_header_id|>system<|end_header_id|>Environment: ipython
Cutting Knowledge Date: December 2023
Today Date: 13 Dec 2024


# Tool Instructions
You may optionally call functions that you have been given access to. You DO NOT have 
to call a function if you do not require it. ONLY call functions if you need them. Do NOT call
functions that you have not been given access to.

michabbb

<?php

namespace App\Services\google;

use Exception;
use Illuminate\Http\Client\ConnectionException;
use Illuminate\Support\Facades\Http;

class Gemini
{

cugu

Webhooks for PocketBase

A simple webhook plugin for PocketBase.

Adds a new collection "webhooks" to the admin interface, to manage webhooks.

Example

The webhook record in the following example send create, update, and delete events in the tickets collection to http://localhost:8080/webhook.

oplanre

<?php
class Pipeline {
    public function __construct(
        private mixed $data
    ) {}
    public function pipe(callable ...$callbacks): static {
        foreach ($callbacks as $callback) {
            $this->data = $callback($this->data);
        }
        return $this;

roychri

Stream Ollama (openai) chat completion API on CLI with HTTPie and jq

Explanation

This command sends a request to the Chat Completion API to generate high-level documentation for the file @src/arch.js. The API is configured to use the llama3-gradient model and to respond in Markdown format.

The messages array contains two elements:

• The first element is a system message that provides the prompt for the API.
• The second element is a user message that specifies the file for which to generate documentation.

e-p-armstrong

# Machine Intelligence Made to Impersonate Characteristics: MIMIC

# NOTE run this $ conda install -c conda-forge mpi4py mpich to get mpi working
# accelerate launch --use_deepspeed -m axolotl.cli.train ./config_name_here
base_model: alpindale/Mistral-7B-v0.2-hf
base_model_config: alpindale/Mistral-7B-v0.2-hf
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

Artefact2

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

garyblankenship

name: Deploy to DigitalOcean

on:
  push:
    branches:
      - main

jobs:
  deploy:
    runs-on: ubuntu-latest

garyblankenship

<?php

/**
 * Graceful cache retrieval and updating macro.
 *
 * First, tries to retrieve the cached value. If not found, uses a grace value.
 * Schedules a background job for updating the cache if grace value is found.
 *
 * @param  string  $key      The cache key to retrieve.
 * @param  int  $ttl      Time to live for the cache, in seconds.

garyblankenship

<?php

$response = Http::post(
    "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=" .
        config("services.googleai.key"),
    [
        "contents" => [
            [
                "parts" => [
                    [