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23 items tagged “anthropic”

2024

Building files-to-prompt entirely using Claude 3 Opus

files-to-prompt is a new tool I built to help me pipe several files at once into prompts to LLMs such as Claude and GPT-4.

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The lifecycle of a code AI completion (via) Philipp Spiess provides a deep dive into how Sourcegraph’s Cody code completion assistant works. Lots of fascinating details in here:

“One interesting learning was that if a user is willing to wait longer for a multi-line request, it usually is worth it to increase latency slightly in favor of quality. For our production setup this means we use a more complex language model for multi-line completions than we do for single-line completions.”

This article is from October 2023 and talks about Claude Instant. The code for Cody is open source so I checked to see if they have switched to Haiku yet and found a commit from March 25th that adds Haiku as an A/B test. # 7th April 2024, 7:37 pm

The cost of AI reasoning over time (via) Karina Nguyen from Anthropic provides a fascinating visualization illustrating the cost of different levels of LLM over the past few years, plotting their cost-per-token against their scores on the MMLU benchmark.

Claude 3 Haiku currently occupies the lowest cost to score ratio, over on the lower right hand side of the chart. # 4th April 2024, 12:51 pm

“The king is dead”—Claude 3 surpasses GPT-4 on Chatbot Arena for the first time. I’m quoted in this piece by Benj Edwards for Ars Technica:

“For the first time, the best available models—Opus for advanced tasks, Haiku for cost and efficiency—are from a vendor that isn’t OpenAI. That’s reassuring—we all benefit from a diversity of top vendors in this space. But GPT-4 is over a year old at this point, and it took that year for anyone else to catch up.” # 27th March 2024, 4:58 pm

Claude and ChatGPT for ad-hoc sidequests

Here is a short, illustrative example of one of the ways in which I use Claude and ChatGPT on a daily basis.

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llm-claude-3 0.3. Anthropic released Claude 3 Haiku today, their least expensive model: $0.25/million tokens of input, $1.25/million of output (GPT-3.5 Turbo is $0.50/$1.50). Unlike GPT-3.5 Haiku also supports image inputs.

I just released a minor update to my llm-claude-3 LLM plugin adding support for the new model. # 13th March 2024, 9:18 pm

The GPT-4 barrier has finally been broken

Four weeks ago, GPT-4 remained the undisputed champion: consistently at the top of every key benchmark, but more importantly the clear winner in terms of “vibes”. Almost everyone investing serious time exploring LLMs agreed that it was the most capable default model for the majority of tasks—and had been for more than a year.

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The Claude 3 system prompt, explained. Anthropic research scientist Amanda Askell provides a detailed breakdown of the Claude 3 system prompt in a Twitter thread.

This is some fascinating prompt engineering. It’s also great to see an LLM provider proudly documenting their system prompt, rather than treating it as a hidden implementation detail.

The prompt is pretty succinct. The three most interesting paragraphs:

“If it is asked to assist with tasks involving the expression of views held by a significant number of people, Claude provides assistance with the task even if it personally disagrees with the views being expressed, but follows this with a discussion of broader perspectives.

Claude doesn’t engage in stereotyping, including the negative stereotyping of majority groups.

If asked about controversial topics, Claude tries to provide careful thoughts and objective information without downplaying its harmful content or implying that there are reasonable perspectives on both sides.” # 7th March 2024, 1:16 am

llm-claude-3. I built a new plugin for LLM—my command-line tool and Python library for interacting with Large Language Models—which adds support for the new Claude 3 models from Anthropic. # 4th March 2024, 6:46 pm

The new Claude 3 model family from Anthropic. Claude 3 is out, and comes in three sizes: Opus (the largest), Sonnet and Haiku.

Claude 3 Opus has self-reported benchmark scores that consistently beat GPT-4. This is a really big deal: in the 12+ months since the GPT-4 release no other model has consistently beat it in this way. It’s exciting to finally see that milestone reached by another research group.

The pricing model here is also really interesting. Prices here are per-million-input-tokens / per-million-output-tokens:

Claude 3 Opus: $15 / $75
Claude 3 Sonnet: $3 / $15
Claude 3 Haiku: $0.25 / $1.25

All three models have a 200,000 length context window and support image input in addition to text.

Compare with today’s OpenAI prices:

GPT-4 Turbo (128K): $10 / $30
GPT-4 8K: $30 / $60
GPT-4 32K: $60 / $120
GPT-3.5 Turbo: $0.50 / $1.50

So Opus pricing is comparable with GPT-4, more than GPT-4 Turbo and significantly cheaper than GPT-4 32K... Sonnet is cheaper than all of the GPT-4 models (including GPT-4 Turbo), and Haiku (which has not yet been released to the Claude API) will be cheaper even than GPT-3.5 Turbo.

It will be interesting to see if OpenAI respond with their own price reductions. # 4th March 2024, 6:34 pm

Talking about Open Source LLMs on Oxide and Friends

I recorded an episode of the Oxide and Friends podcast on Monday, talking with Bryan Cantrill and Adam Leventhal about Open Source LLMs.

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2023

Long context prompting for Claude 2.1. Claude 2.1 has a 200,000 token context, enough for around 500 pages of text. Convincing it to answer a question based on a single sentence buried deep within that content can be difficult, but Anthropic found that adding “Assistant: Here is the most relevant sentence in the context:” to the end of the prompt was enough to raise Claude 2.1’s score from 27% to 98% on their evaluation. # 6th December 2023, 11:44 pm

Claude: How to use system prompts. Documentation for the new system prompt support added in Claude 2.1. The design surprises me a little: the system prompt is just the text that comes before the first instance of the text “Human: ...”—but Anthropic promise that instructions in that section of the prompt will be treated differently and followed more closely than any instructions that follow.

This whole page of documentation is giving me some pretty serious prompt injection red flags to be honest. Anthropic’s recommended way of using their models is entirely based around concatenating together strings of text using special delimiter phrases.

I’ll give it points for honesty though. OpenAI use JSON to field different parts of the prompt, but under the hood they’re all concatenated together with special tokens into a single token stream. # 22nd November 2023, 4:31 am

Introducing Claude 2.1. Anthropic’s Claude used to have the longest token context of any of the major models: 100,000 tokens, which is about 300 pages. Then GPT-4 Turbo came out with 128,000 tokens and Claude lost one of its key differentiators.

Claude is back! Version 2.1, announced today, bumps the token limit up to 200,000—and also adds support for OpenAI-style system prompts, a feature I’ve been really missing.

They also announced tool use, but that’s only available for a very limited set of partners to preview at the moment. # 22nd November 2023, 4:28 am

Deciphering clues in a news article to understand how it was reported

Written journalism is full of conventions that hint at the underlying reporting process, many of which are not entirely obvious. Learning how to read and interpret these can help you get a lot more out of the news.

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Before Altman’s Ouster, OpenAI’s Board Was Divided and Feuding. This is the first piece of reporting I’ve seen on the OpenAI situation which has offered a glimmer of an explanation as to what happened.

It sounds like the board had been fighting about things for over a year—notably including who should replace departed members, which is how they’d shrunk down to just six people.

There’s also an interesting detail in here about the formation of Anthropic:

“Mr. Sutskever’s frustration with Mr. Altman echoed what had happened in 2021 when another senior A.I. scientist left OpenAI to form the company Anthropic. That scientist and other researchers went to the board to try to push Mr. Altman out. After they failed, they gave up and departed, according to three people familiar with the attempt to push Mr. Altman out.” # 22nd November 2023, 12:31 am

Claude was trained on data up until December 2022, but may know some events into early 2023.

How up-to-date is Claude's training data? # 9th October 2023, 1:25 am

Decomposing Language Models Into Understandable Components. Anthropic appear to have made a major breakthrough with respect to the interpretability of Large Language Models:

“[...] we outline evidence that there are better units of analysis than individual neurons, and we have built machinery that lets us find these units in small transformer models. These units, called features, correspond to patterns (linear combinations) of neuron activations. This provides a path to breaking down complex neural networks into parts we can understand” # 8th October 2023, 3:43 pm

How I make annotated presentations

Giving a talk is a lot of work. I go by a rule of thumb I learned from Damian Conway: a minimum of ten hours of preparation for every one hour spent on stage.

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Catching up on the weird world of LLMs

I gave a talk on Sunday at North Bay Python where I attempted to summarize the last few years of development in the space of LLMs—Large Language Models, the technology behind tools like ChatGPT, Google Bard and Llama 2.

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Not every conversation I had at Anthropic revolved around existential risk. But dread was a dominant theme. At times, I felt like a food writer who was assigned to cover a trendy new restaurant, only to discover that the kitchen staff wanted to talk about nothing but food poisoning.

Kevin Roose # 13th July 2023, 10:23 pm

claude.ai. Anthropic’s new Claude 2 model is available to use online, and it has a 100k token context window and the ability to upload files to it—I tried uploading a text file with 34,000 tokens in it (according to my ttok CLI tool, counting using the GPT-3.5 tokenizer) and it gave me a workable summary. # 12th July 2023, 4:39 pm

It’s infuriatingly hard to understand how closed models train on their input

One of the most common concerns I see about large language models regards their training data. People are worried that anything they say to ChatGPT could be memorized by it and spat out to other users. People are concerned that anything they store in a private repository on GitHub might be used as training data for future versions of Copilot.

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