Life & fun · alternatives · 2026

24 alternatives to Byte-Pair Encoding tokenizer for training LLMs on large datasets

Byte-Pair Encoding tokenizer for training large language models on huge datasets - jmaczan/bpe-tokenizer

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Byte-Pair Encoding tokenizer for training LLMs on large datasets launched in 2024; newer entries below may have overtaken it.

1
I built a tiny LLM to demystify how language models work▲915
Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
Apr 2026 · github.com · its alternatives →
2
Wordllama – Things you can do with the token embeddings of an LLM▲370
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com · its alternatives →
3
Explore large language models with 512MB of RAM▲138
Explore large language models in 512MB of RAM. Contribute to jncraton/languagemodels development by creating an account on GitHub.
2023 · github.com · its alternatives →
4
HuggingFace – Fast tokenization library for deep-learning NLP pipelines▲168
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - huggingface/tokenizers
2020 · github.com · its alternatives →
5
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
6PT
Predictive text using only 13kb of JavaScript. no LLM▲207
2024 · adamgrant.info · its alternatives →
7
Fully client-side GPT2 prediction visualizer▲153
Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
8AS
A surprisingly effective way to predict token importance in LLM prompts▲14
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io · its alternatives →
9
Tiny-vLLM – high performance LLM inference engine in C++ and CUDA▲205
Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm
May 2026 · github.com · its alternatives →
10
A general-purpose encoder-decoder framework for Tensorflow▲54
A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq
2017 · github.com · its alternatives →
11
a16z - When Language Meets Big Data▲58
It's not what you say, it's how you say it
2016 · soundcloud.com · its alternatives →
12
An all-in-one blog for learning Large Language Models (LLMs)▲5
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app · its alternatives →
13
LLaMA▲118
A foundational, 65-billion-parameter large language model
2023 · research.facebook.com · its alternatives →
14
LLMs following grammar for general-purpose PLs▲11
Efficient and general syntactical decoding for Large Language Models - structuredllm/syncode
2024 · github.com · its alternatives →
15WD
Web demo of 13B Alpaca-LLaMA trained on improved Stanford dataset▲42
2023 · lama.nbnl.uk · its alternatives →
16
Improve Text-to-SQL Accuracy with LLM▲17
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
17LT
LLM Token Visualizer – How big is 128k token input▲6
2025 · v0-llm-token-visualizer.vercel.app · its alternatives →
18UN
Use natural language to query and visualize 400M tweets▲11
We've been working hard for the last year on building HeavyIQ, an LLM-powered plugin to the GPU-accelerated HeavyDB database, that allows users to ask natural language questions and get SQL, visualizations, and natural language answers back. The LLM itself is fine-tuned on tens of thousands of question and answer pairs, with a major focus around building the model’s proficiency at performing spatial and temporal joins. You can try it for yourself on this live demo of 400M tweets and a handful of other datasets of interest:…
2024 · demo-heavyiq.heavy.ai · its alternatives →
19
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
20
A Command-Line Sentence Tokenizer Written in Golang▲6
A multilingual command line sentence tokenizer in Golang - neurosnap/sentences
2015 · github.com · its alternatives →
21AE
An encoding/decoding tool for Martin David's theoretical S language▲24
2016 · ramadis.github.com · its alternatives →
22
Tokensift, an open-sourced token-efficiency linter for LLM prompts▲6
Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift
Aug 2026 · github.com · its alternatives →
23
I trained a language model that thinks the capital of Japan is Paris▲15
DIMBA II: a 288M-parameter masked-diffusion language model on a bidirectional Mamba-2 backbone. What worked, what failed, and what we
Jul 2026 · hamiltonianresearch.xyz · its alternatives →
24
ESM C, setting a new state of the art for protein language models▲11
Hey HN! We're releasing ESM C, a series of protein language models that set a new state of the art. By training on metagenomics and fine-tuning on UniRef, we're able to match ESM2 650M with ESM C 300M, and ESM C 6B has incredible unsupervised contact precision performance. These models are meant to be used as base models and representation learning models for protein sequences, and we'll release a preprint describing exact details soon.
2024 · evolutionaryscale.ai · its alternatives →

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