Simon Willison’s Weblog

mistralai/mistral-common. New from Mistral: mistral-common, an open source Python library providing "a set of tools to help you work with Mistral models".

So far that means a tokenizer! This is similar to OpenAI's tiktoken library in that it lets you run tokenization in your own code, which crucially means you can count the number of tokens that you are about to use - useful for cost estimates but also for cramming the maximum allowed tokens in the context window for things like RAG.

Mistral's library is better than tiktoken though, in that it also includes logic for correctly calculating the tokens needed for conversation construction and tool definition. With OpenAI's APIs you're currently left guessing how many tokens are taken up by these advanced features.

Anthropic haven't published any form of tokenizer at all - it's the feature I'd most like to see from them next.

Here's how to explore the vocabulary of the tokenizer:

MistralTokenizer.from_model(
    "open-mixtral-8x22b"
).instruct_tokenizer.tokenizer.vocab()[:12]

['<unk>', '<s>', '</s>', '[INST]', '[/INST]', '[TOOL_CALLS]', '[AVAILABLE_TOOLS]', '[/AVAILABLE_TOOLS]', '[TOOL_RESULTS]', '[/TOOL_RESULTS]'] # 18th April 2024, 12:39 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 Zen of Python, Unix, and LLMs. Here’s the YouTube recording of my 1.5 hour conversation with Hugo Bowne-Anderson yesterday.

I fed a Whisper transcript to Google Gemini Pro 1.5 and asked it for the themes from our conversation, and it said we talked about “Python’s success and versatility, the rise and potential of LLMs, data sharing and ethics in the age of LLMs, Unix philosophy and its influence on software development and the future of programming and human-computer interaction”. # 29th February 2024, 9:04 pm

Getting Started With CUDA for Python Programmers (via) if, like me, you’ve avoided CUDA programming (writing efficient code that runs on NVIGIA GPUs) in the past, Jeremy Howard has a new 1hr17m video tutorial that demystifies the basics. The code is all run using PyTorch in notebooks running on Google Colab, and it starts with a very clear demonstration of how to convert a RGB image to black and white. # 29th January 2024, 9:23 pm

The Random Transformer (via) “Understand how transformers work by demystifying all the math behind them”—Omar Sanseviero from Hugging Face meticulously implements the transformer architecture behind LLMs from scratch using Python and numpy. There’s a lot to take in here but it’s all very clearly explained. # 10th January 2024, 5:09 am