NanoEuler
NanoEuler is an open-source GPT-2-style language model built entirely from scratch in C/CUDA with hand-written backprop, BPE tokenizer, FlashAttention, and training pipelines.
NanoEuler
What is NanoEuler?
NanoEuler vs Similar AI Tools
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How to use NanoEuler?
- 1Clone the repository from GitHub.
- 2Ensure gcc and nvcc are installed.
- 3Run 'make' to build the CPU training binary.
- 4Run 'make check' to verify backward pass.
- 5Train the small model: './nanoeuler train'.
- 6Train the GPU model: './nanoeuler_cuda t'.
- 7Fine-tune for chat: './nanoeuler_cuda s'.
- 8Interact with the chat model: './nanoeuler_cuda c'.
NanoEuler Key Features
- Hand-written forward and backward passes in C/CUDA
- Byte-level BPE tokenizer with GPT-2-style pretokenization
- FlashAttention kernel for GPU training
- Pretraining on books and web corpus
- Supervised fine-tuning for chat (SFT)
- Full-model gradient check in double precision
- Checkpoint/resume training
- Supports CPU and GPU (CUDA) execution
NanoEuler Use Cases
- Educational exploration of LLM internals
- Learning transformer training from scratch
- Experimenting with small-scale language models
- Research into custom model architectures
NanoEuler Pricing & Free Credits
NanoEuler currently operates on a Free model.
This tool is completely free to use
NanoEuler Pros & Cons
Pros
- Complete from-scratch implementation, no dependencies
- Verified gradient accuracy via numerical check
- Supports both CPU and GPU training
- Includes pretraining and fine-tuning pipelines
- Well-documented and educational
Cons
- Only ~116M parameters, not suitable for production
- Requires CUDA-capable GPU for large model training
- Limited world knowledge due to small scale
- No official pre-trained checkpoints provided
What is NanoEuler best for?
- Developers and researchers learning LLM training
- Students studying transformer internals
- Hobbyists building custom small models
NanoEuler FAQ
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