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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
Make a base instruct model into a chat model, WITHOUT RLHF
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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.
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.
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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
Code for a worker that can receive logpush http requests and parse the contents using streams
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You can do a SQL text query by using the LIKE operator. The issue is that using it requires a lot of computation, as a complete string query is done. Also if you want to have more search options (more fields), your query will grow a lot in complexity. To solve this issue, there's a concept of virtual tables for full text search (FTS).
We will build our solution using Room (already set in the project). We're using version 2.2.0-rc01 for that.
Step 1 - Create new Virtual Table
With Room, the only thing we need is to create the new class with @FTS4 notation. By specifying contentEntity to be the Route class, it means that it will reuse the values from the Route table instead of populating this one with copies. The fields in question should match the ones from the Route table. In this example we only need the title.
Generate a vanilla Wireguard config file for Cloudflare's WARP service
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Helpers to control concurrency for one shot requests using Kotlin coroutines.
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