AI · alternatives · 2026
24 alternatives to less than 650 LOC trainable GPT only using NumPy
Absolute minimalistic implementation of a GPT-like transformer using only numpy (<650 lines). - joennlae/tensorli
This is a trainable GPT implementation written in fewer than 650 lines of code using only NumPy. It is designed for developers and machine learning enthusiasts who want to understand or build GPT models from scratch… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. less than 650 LOC trainable GPT only using NumPy launched in 2023; newer entries below may have overtaken it.
- 1
Announcing GPT-4.1, GPT-4.1 mini, & GPT-4.1 nano in the API
2025 · openai.com · its alternatives →
- 2
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 →
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Fast and efficient models optimized for coding and subagents
Mar 2026 · openai.com · its alternatives →
- 5
A tiny version of GPT fully implemented in Python with zero dependencies - isaacperez/tinygpt
2024 · github.com · its alternatives →
- 6
Train a tiny GPT in under a minute (CUDA only). Contribute to lostmsu/TurboGPT development by creating an account on GitHub.
22d ago · github.com · its alternatives →
- 7
Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data. - volotat/mini-AGI
Sep 2026 · github.com · its alternatives →
- 8
:satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow
2018 · github.com · its alternatives →
- 9
Back in the old days, people used to do general-purpose GPU programming by using shaders like GLSL. This is what inspired NVIDIA (and other companies) to eventually create CUDA (and friends). This is an implementation of GPT-2 using WebGL and shaders. Enjoy!
2025 · github.com · its alternatives →
- 10TT
For the last few weeks, I have been working on training an LLM from scratch and deploying it in production on Google Cloud Platform. Finally, I trained a 30 million parameter model on 1 billion tokens and deployed it as a web service. You can access the LLM using this site - https://kunalmishra.info The following steps were taken to build Tiny Tales GPT 1. Downloaded and preprocessed 8GB of dataset using multiprocessing library. 2. Tokenized the data using byte pair encoding to create 1 billion tokens sharded in different bin files. 3. Defined a training setup and trained the model…
2024 · kunalmishra.info · its alternatives →
- 11
GPT4All▲107A chatbot trained on a massive collection of clean data
2023 · github.com · its alternatives →
- 12
Pure Rust implementation of a minimal Generative Pretrained Transformer - keyvank/femtoGPT
2023 · github.com · its alternatives →
- 13
I previously posted this a month and a half ago: https://news.ycombinator.com/item?id=45876742 I have rewritten it to be much more simple, from 951 lines of code to just 364, with no loss in core functionality or generation quality. I also added a tiny GPT implementation as a comparison (inspired by Andrej Karpathy's code). The two model implementations are ~80% identical, with the core differences being the generate and get_batch functions. The model architecture, training loop, etc, differ in only 19 lines of code. Trained weights are included, so you can just clone and run…
Nov 2025 · github.com · its alternatives →
- 14
Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai · its alternatives →
- 15
A robust Python tool for text-based AI training and generation using GPT-2. - minimaxir/aitextgen
2020 · github.com · its alternatives →
- 16
A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
- 17
A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq
2017 · github.com · its alternatives →
- 18
2018 · lambdaway.free.fr · its alternatives →
- 19
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
Here's a small demonstration of the fundamental aspects of the word-to-vec algorithm. It's implemented in a single python script and depends only on a single text file for training. It's not meant to be blazingly fast or anything, just a toy example to aid my understanding of how word vectors might be learnt from a corpus.
2023 · github.com · its alternatives →
- 21
I saw a tweet that tinygrad is so good that you could make a graphics library that wraps tg. So I’ve been hacking on a gtinygrad, and honestly it convinced me it could be used for legit research. The JIT + tensor model ends up being a really nice way to express light transport all in simple python, so I reimplemented some new research papers from SIGGRAPH like REstir PG and SZ and it just works. instead of complicated cpp its just a 200 LOC of python.
Jan 2026 · github.com · its alternatives →
- 22MI
The release of microgpt by Andrej Karpathy is a foundational moment for AI transparency. In exactly 243 lines of pure, dependency-free Python, Karpathy has implemented the complete GPT algorithm from scratch. As a PhD scholar investigating AI and Blockchain, I see this as the ultimate tool for moving beyond the "black box" narrative of Large Language Models (LLMs). The Architecture of Simplicity Unlike modern frameworks that hide complexity behind optimized CUDA kernels, microgpt exposes the raw mathematical machinery. The code implements: The Autograd Engine: A custom Value class that…
Feb 2026 · its alternatives →
- 23
An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow/core/distributed_runtime at master · tensorflow/tensorflow
2016 · github.com · its alternatives →
- 24L2
Hi all, today we're excited to launch LoraLand: 25 fine-tuned mistral-7b models that outperform #gpt4 on task-specific applications ranging from sentiment detection to question answering. All 25 fine-tuned models… - Outperform GPT-4, GPT-3.5-turbo, and mistral-7b-instruct for specific tasks - Are cost-effectively served from a single GPU through LoRAX - Were trained for less than $8 each on average You can prompt all of the fine-tuned models today and compare their results to mistral-7b-instruct in real time! We'd love to hear comments and feedback from the community
2024 · predibase.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →