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matthewolfe
searching Neon…
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6 ms
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Sessiongrep: Local-first memory layer for CLI agents
(github.com)
2 points
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matthewolfe
2mo ago
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0 comments
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Timeline of Computer History
(computerhistory.org)
3 points
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matthewolfe
5mo ago
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1 comments
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matthewolfe
1y ago
What exactly doesn't "just work" anymore?
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Hammock Driven Development – Rich Hickey [video]
(youtube.com)
3 points
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matthewolfe
1y ago
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LLM-Generated Rules Engines for LLM Explainability
(brain.co)
2 points
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matthewolfe
1y ago
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0 comments
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Wormhole for Perplexity Comet
(blog.gingerbeardman.com)
3 points
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matthewolfe
1y ago
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1 comments
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Assign Tickets to Claude Code
(twitter.com)
1 points
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matthewolfe
1y ago
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0 comments
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Richard Feynman: What I cannot create, I do not understand
(freshspectrum.com)
1 points
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matthewolfe
1y ago
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0 comments
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The Factorio Mindset
(thediff.co)
2 points
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matthewolfe
1y ago
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0 comments
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matthewolfe
1y ago
I believe SchemeFlow [0] is working on solving some of these problem, particularly with the insane reporting requirements. But of course, that still leaves the unions... [0] https://www.schemeflow.com/
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matthewolfe
1y ago
For argument's sake, suppose we live in a world where many high-quality models can be run on-device. Is there any concern from companies/model developers about exposing their proprietary weights to the end user? It's generall
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matthewolfe
1y ago
To echo the other replies, the tokenizer is definitely not the bottleneck. It just happens to be the first step in inference, so it's what I did first.
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matthewolfe
1y ago
haha, I thought about it.
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matthewolfe
1y ago
Fair chance I'm remembering it wrong :D
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matthewolfe
1y ago
Alright, 0.1.1 should now be a true drop-in replacement. I'll write up some examples soon.
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matthewolfe
1y ago
Should be the same. Both use Byte-Pair Encoding (BPE) as underlying algo.
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matthewolfe
1y ago
Ah good catch. Updating this right now.
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matthewolfe
1y ago
Modal's GPU glossary is a good overview about how GPUs work [0]. Karpathy's LLM overview is a good high level overview on LLMs [1]. 3b1b's video (and subsequent videos) on transformers was excellent at helping me understand t
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matthewolfe
1y ago
I'm working on incremental re-tokenizing next. Then I'll run some benchmarks against this crate too.
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matthewolfe
1y ago
Agreed. A former mentor of mine told me a nice way of viewing software development: 1. Make it work. 2. Make it fast. 3. Make it pretty. Transformers & LLMs have been developed to a point where they work quite well. I feel as though we&
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matthewolfe
1y ago
Cool! I've reached out to the guy who maintains Tiktoken to talk about this.
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matthewolfe
1y ago
A lot of model-specific tokenizers have reference implementations ([0], [1]). Underlying them is a core algorithm like SentencePiece or Byte-pair encoding (BPE). Tiktoken and TokenDagger are BPE implementations. The wrapping "tokenizer
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matthewolfe
1y ago
I probably will. Was hesitant initially, because adding PCRE2 as a dependency might cause issues to existing projects. I believe this was discussed briefly in a closed PR with other performance improvements.
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matthewolfe
1y ago
The output should be identical, assuming no bugs. The Tiktoken implementation takes a collection of all special tokens upon initialization and compiles them into a regex by joining them with `|` [0]. Then the actual encoding process checks
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matthewolfe
1y ago
Thanks, I clarified it.
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matthewolfe
1y ago
Agreed. I figured nobody would use it otherwise.
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Show HN: TokenDagger – A tokenizer faster than OpenAI's Tiktoken
(github.com)
281 points
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matthewolfe
1y ago
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73 comments
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matthewolfe
1y ago
I'm working on TokenDagger [0] a high performance implementation of OpenAI's Tiktoken. My benchmarks are showing 2-3x higher throughput, as well as ~4x faster tokenization for code samples on a single thread. [0] https:/
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From Punched Cards to Flat Screens – A Technical Autobiography
(drive.google.com)
1 points
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matthewolfe
1y ago
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0 comments
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Idea to Revenue in 4 Days
(sigurg.com)
1 points
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matthewolfe
1y ago
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0 comments
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