Olshansk

# A one liner to leverage the GPU on a mac to transcribe audio files
# Inspired by https://simonwillison.net/2024/Aug/13/mlx-whisper/
llm_transcribe_recording () {
	local file_path="$1"
	python3 -c "
import mlx_whisper
result = mlx_whisper.transcribe('$file_path', path_or_hf_repo='mlx-community/distil-whisper-large-v3')
print(result['text'])
"
}

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

oneryalcin

# I couldn't get return generators from chains so I had to do a bit of low level SSE, Hope this is useful
# Probably you'll use another Vector Store instead of OpenSearch, but if you want to mimic what I did here, 
# please use the fork of `OpenSearchVectorSearch` in https://github.com/oneryalcin/langchain


import json
import os
import logging
from typing import List, Generator

python273

import os
os.environ["OPENAI_API_KEY"] = ""

from flask import Flask, Response, request
import threading
import queue

from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import AIMessage, HumanMessage, SystemMessage

wuputah

Hydra Performance Microbenchmark

Important: This microbenchmark is not intended to represent any real workload. Compression ratios, and therefore performance, will depend heavily on the specific workload. This is only for the purpose of illustrating a "columnar friendly" contrived workload that showcases the benefits of columnar.

Schema

Mr0grog

/**
 * Parse Airtable's "ConstantPooledData" format. They recently started using
 * this format to compress some API responses, and it appears to be a
 * home-grown format.
 * 
 * Call `parseData()` if you have an object with data (e.g. a JSON-parsed API
 * response body).
 * 
 * Call `parseString()` if you have a raw string of data (e.g. an API response
 * body).

tabishiqbal

<%= form_with(model: team) do |form| %>
  <div>
    <%= form.label :name %>
    <%= form.text_field :name, class: "input" %>
  </div>

  <div>
    <%= f.select :user_id, {}, {placeholder: "Select user"}, {class: "w-full", data: { controller: "select", select_url_value: users_path }} %>
  </div>

coco98

import requests

#Fetch existing tables
tables = requests.post('http://localhost:8080/v1/query', json={
    "type":"select",
    "args":{
        "table": {"schema": "information_schema", "name": "tables"}, 
        "columns": ["table_name"], 
        "where": {"table_schema": {"$eq": "public"}}
    }

erinkcochran87

Use this as a guide or a template for your tap's documentation. Remove a section if it's not applicable.

---

[tap_name]

Connecting [tap_name]

Requirements