--- title: "femtoGPT" type: "tool" slug: "keyvank-femtogpt" canonical_url: "https://www.graphcanon.com/tools/keyvank-femtogpt" github_url: "https://github.com/keyvank/femtoGPT" homepage_url: "https://discord.gg/wTJFaDVn45" stars: 934 forks: 66 primary_language: "Rust" license: "MIT" archived: false categories: ["model-training", "llm-frameworks", "inference-serving"] tags: ["gpu", "llm", "machine-learning", "neural-network", "hacktoberfest", "opencl", "from-scratch", "gpt"] updated_at: "2026-07-11T10:42:19.907673+00:00" --- # femtoGPT > Pure Rust implementation of a minimal Generative Pretrained Transformer Pure Rust implementation of a minimal Generative Pretrained Transformer ## Facts - Repository: https://github.com/keyvank/femtoGPT - Homepage: https://discord.gg/wTJFaDVn45 - Stars: 934 · Forks: 66 · Open issues: 10 · Watchers: 11 - Primary language: Rust - License: MIT - Last pushed: 2025-10-21T11:13:42+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Slowing (computed 2026-07-11T10:42:12.629Z) - Security scan: No lockfile (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T10:42:13.967Z - Full report: [trust report](/tools/keyvank-femtogpt/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/keyvank-femtogpt/trust) ## Categories - [Model Training](/categories/model-training.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Inference & Serving](/categories/inference-serving.md) ## Tags gpu, llm, machine-learning, neural-network, hacktoberfest, opencl, from-scratch, gpt ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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Check it out here: [The Super Programmer](https://github.com/keyvank/tsp)) ## Usage Training: `cargo run --release -- train` Inference: `cargo run --release -- infer` (Note: Add `--features gpu` in order to leverage GPU speedups!) ## Intro Everything is implemented from scratch, including the tensor processing logic along with training/inference code of a minimal GPT architecture. The architecture is very similar/almost identical with Andrej Karpathy's [nanoGPT video lecture](https://github.com/karpathy/ng-video-lecture). femtoGPT is a great start for those who are fascinated by LLMs and would like to understand how these models work in very deep levels. femtoGPT uses nothing but random generation libraries (`rand`/`rand-distr`), data-serialization libraries (`serde`/`bincode` for saving/loading already trained models) and a parallel computing library (`rayon`). femtoGPT is ~~EXTREMELY SLOW~~ ***relatively fast on CPU 😉***, and most of the primitive operations (E.g Matrix multiplication) are implemented in the simplest way possible. Correctness of gradients is checked using gradient-check method, though it still is very possible that some layers are implemented wrongly. ([Discord server](https://discord.gg/wTJFaDVn45) for discussions around the project!) ## Usage Make sure you have the Rust toolchain on your system, in order to compile and run the project: `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh` If you want to train using a GPU, you will first need to make sure your GPU drivers are correctly installed on your system, and their OpenCL runtimes are available. On Debian systems, you can setup OpenCL runtimes by installing the package `ocl-icd-opencl-dev`: `sudo apt install ocl-icd-opencl-dev` ***GOOD NEWS!*** *Since femtoGPT's GPU implementation is based on OpenCL, it can run on both NVIDIA and AMD cards, and you won't need to install heavy-weight CUDA-toolkits on your system. OpenCL runtimes would suffice!* Now you'll just need to put the text you want to train your GPT model on, inside `dataset.txt`. Make sure it has a small number of unique characters! (E.g. the current dataset has only used 65 different unique characters!) Then you'll need to run: ``` cargo run --release ``` It will start training the model and will put the training data in the `train_data` directory. You can stop the training and continue later! ## Output samples After hours of training on the Shakespeare database, on a 300k parameter model, this has been the output: ``` LIS: Tore hend shater sorerds tougeng an herdofed seng he borind, Ound ourere sthe, a sou so tousthe ashtherd, m se a man stousshan here hat mend serthe fo witownderstesther s ars at atheno sel theas, thisth t are sorind bour win soutinds mater horengher ``` This is embarrassingly bad, but looking at the bright side, it seems like it has been able to generate words that are easy to pronounce. I'm currently training a 10M parameter model to further examine the correctness of my implementation. **UPDATE 5th June 2023:** This has been a new output, after more hours of training on a model with similar scale: ``` What like but wore pad wo me che nogns yous dares, As supt it nind bupart 'the reed: And hils not es ``` Obviously the model has started to learn some words and punctuation rules! **UPDATE 9th June 2023:** Model was able to reach loss value of ~1.4 Here is an example output: ``` Adistition gone; true; schistoes for mine souls! Before your home, bariechts should be Carlam on that's a worf quirer of him so. What look'd lack away more To him foot; one hour fortious of saves: Son; 'Tis all Earl mmistling me. HAR ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/keyvank-femtogpt`](/api/graphcanon/tools/keyvank-femtogpt) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_