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@taskylizard
taskylizard / fmhy.md
Last active June 17, 2024 03:56
/r/freemediaheckyeah, in one single file (view raw)
@stefandanaita
stefandanaita / cf-logpush-destination-worker.ts
Last active January 7, 2023 19:26
Code for a worker that can receive logpush http requests and parse the contents using streams
// SPDX-License-Identifier: MIT-0
export interface Env {
}
export default {
async fetch(
request: Request,
env: Env,

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

@MarkRoddy
MarkRoddy / parse_s3_access_logs.sql
Last active February 28, 2024 18:42
DuckDB: Query S3 Access Logs
/*
Usage: you'll want to search for the strings <bucket> and <prefix>, and insert the S3 bucket where your access
logs are being delivered. Use (or delete) <prefix> to filter to a subset of your logs.
*/
/*
These commented out configuration settings you can either run yourself in the REPL and source this file using
`.read parse_s3_access_logs.sql`, or you can uncomment them and supply values for yourself.
@veekaybee
veekaybee / normcore-llm.md
Last active June 18, 2024 17:30
Normcore LLM Reads

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

Screenshot 2023-12-18 at 10 40 27 PM

Pre-Transformer Models

@anadim
anadim / gist:344941a7e24e7a2ee7b48ce8f63a16ac
Created October 18, 2023 20:27
Make a base instruct model into a chat model, WITHOUT RLHF
Instructions:
As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:
1. Be friendly and approachable in your responses.
2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
3. Always use inclusive and respectful language that is not offensive.
4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
5. Always be honest in your responses. Do not lie or engage in deceit.
6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.
@thesamesam
thesamesam / xz-backdoor.md
Last active June 18, 2024 23:46
xz-utils backdoor situation (CVE-2024-3094)

FAQ on the xz-utils backdoor (CVE-2024-3094)

This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.

Background

On March 29th, 2024, a backdoor was discovered in xz-utils, a suite of software that