Simon Willison’s Weblog

Understanding Immortal Objects in Python 3.12. Abhinav Upadhyay provides a clear and detailed explanation of immortal objects coming in Python 3.12, which ensure Python no longer updates reference counts for immutable objects such as True, False, None and low-values integers. The trick (which maintains ABI compatibility) is pretty simple: a reference count value of 4294967295 now means an object is immortal, and the existing Py_INCREF and Py_DECREF macros have been updated to take that into account.

# 12:08 pm / python

Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper. Anyscale offer (cheap, fast) API access to Llama 2, so they’re not an unbiased source of information—but I really hope their claim here that Llama 2 70B provides almost equivalent summarization quality to GPT-4 holds up. Summarization is one of my favourite applications of LLMs, partly because it’s key to being able to implement Retrieval Augmented Generation against your own documents—where snippets of relevant documents are fed to the model and used to answer a user’s question. Having a really high performance openly licensed summarization model is a very big deal.

# 2:37 pm / ai, generative-ai, llama, gpt-4, llms, rag, gpt

WebLLM supports Llama 2 70B now. The WebLLM project from MLC uses WebGPU to run large language models entirely in the browser. They recently added support for Llama 2, including Llama 2 70B, the largest and most powerful model in that family.

To my astonishment, this worked! I used a M2 Mac with 64GB of RAM and Chrome Canary and it downloaded many GBs of data... but it worked, and spat out tokens at a slow but respectable rate of 3.25 tokens/second.

# 2:41 pm / ai, webassembly, generative-ai, llama, llms, mlc, webgpu

excalidraw.com (via) Really nice browser-based editor for simple diagrams using a pleasing hand-sketched style, with the ability to export them as SVG or PNG.

# 4:30 pm / svg