mccrory

Anybody have a non-paywall link?

Verizon is a telco, a regulated industry. They are protected by a tariff, you cannot sue them. So there cannot be a class action lawsuit against a company that can’t be sued.

This looks interesting, I would suggest adding some screenshots in the docs of what a final output looks like (counter app for example).

Well done!

What’s up with the license? It doesn’t look friendly to do anything except build a personal project. It says you can’t redistribute it, unless I’ve misread it somehow?

Love this, would love to see this run locally and allow me to add notes and associate docs with it.

Also, a simple local API would be fantastic.

My understanding is that it is a large collection of traits and that a person might have no traits or possibly even 1 or 2 that are very minor and NOT be on the spectrum. Someone else might have 1 severe trait and be on the spectrum and someone else might need 5 traits to be diagnosed as on the spectrum. Don’t think of it as a continuous spectrum but more of a collection of traits that are of little or no severity to high severity where as was mentioned before, the person has little or no quality of life.

MCNE/MCSE here from the mid-90’s, agree it was obvious that Netware would likely fade. Windows NT 3.51 (even though it wasn’t as good at the time), was more user friendly and had the Windows interface, compatibility, etc etc However, it NT wasn’t remotely as stable as Netware at that time either. Then we got to watch as Novell slowly went insane competing and buying WordPerfect and doing all sorts of other crazy moves. I still wish that Novell had taken all of its money and bought VMware when they were just working on GSX/ESX (now vSphere). This would have changed the trajectory of both Novell and the market.

That could be signs of something else too.

Now if we could solve this for the rest of the lawn equipment, especially mowers and string trimmers.

Or go for 33% of your salary or even better, live on 1% of your salary… at some point you simply can’t make it work unless you are making 7 or more figures. Which then it really doesn’t matter anyway. :-/

100% they do

What happens when the “few bad apples” get all of the traction and all of their news spreads vs good journalists who get very small readership? You end up with all perceived journalism being bad and the good being so diluted by the bad that their impact isn’t ever felt. I believe this is where we are headed if we aren’t already there now.

It’s not society, it’s a capitalism problem. The harder you work, the richer and more powerful those above become, they will resist anything that goes against this cycle, such as you working less.

TLDR: vMotion for schema changes in PostgreSQL

AGPLv3 - No Thanks

Sign me up for that, I made my own out of legos as a kid. I managed to make two of them that looked like robots and vehicles. Using just hinges and the small squares with rotating centers.

No, just was thinking OG Transformers. All though some of the very new stuff isn’t my thing.

Does this apply to both types of transformers? Decepticons and Autobots?

This exempts almost everyone and everything. lol

Then they can say goodbye to most online gaming. I wonder how people will feel about this when they realize their kids can’t play games anymore?

Lots of interesting information in this and so far the model has worked well on my toy scenarios.

DuckDB-NSQL-7B

Model Description

NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.

In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of DuckDB text-to-SQL pairs.

Training Data

200k DuckDB text-to-SQL pairs, synthetically generated using Mixtral-8x7B-Instruct-v0.1, guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from NSText2SQL that were transpiled to DuckDB SQL using sqlglot.

Evaluation Data

We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available here.

Training Procedure

DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We fine-tuned for 10 epochs.

Intended Use and Limitations

The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs. In contrast to existing text-to-SQL models, the SQL generation is not contrained to SELECT statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions.

How to Use

Example 1:

import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)

text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question.

### Input:

### Question: create a new table called tmp from test.csv

### Response (use duckdb shorthand if possible): """

input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=500) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

Example 2:

import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)

text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.

### Input: Here is the database schema that the SQL query will run on: CREATE TABLE taxi ( VendorID bigint, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count double, trip_distance double, fare_amount double, extra double, tip_amount double, tolls_amount double, improvement_surcharge double, total_amount double, );

### Question: get all columns ending with _amount from taxi table

### Response (use duckdb shorthand if possible):"""

input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=500) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

This majority of discussions/arguments/comments are summed up by the plot from this Rick and Morty episode: https://en.m.wikipedia.org/wiki/Rattlestar_Ricklactica IMO

Where is my M3 Ultra MacBook Pro? I’m okay with reduced battery if I can have 256GB of ram and the processing power.

Please show me the candidates that aren’t coin operated by big business. Either party is entirely beholden to the giant corporations and lobbyists. So voting literally does nothing.

Too bad it has tons of memory leaks if you run even simple VBA using excel.

Centipede https://g.co/kgs/zUznwnH

Better to distract people with other things than face the important work.