gradientlab 0.1.2

A lab where gradients flow and models go to prod.

gradientlab

A lab where gradients flow and models go to prod.

This repo is an attempt to have a tidy place for my own small scale pytorch-based deep learning experiments.

Guiding principles

• Experiment as a first-class citizen
  • full replicability: dataprep, modeling, configs, training and eval code is self-contained
• Architecture copy-paste is allowed, no preemptive optimization when doing applied AI
  • Still, we're not savages: If you're reusing an exact same nn.Module N times, go modularize it.
  • For me N=3 means that the thing works => refactor.
• Cristalize a stable architecture or nn.Module under neuralblocks/
  • Avoid model overparametrization and huge configs
• HuggingFace basic compatibility
  • we don't do whitepapers, we push to prod ASAP
• Notebooks as a clean demo interface
  • do dirty & temporary stuff under notebooks/trash
• ...

If you want to fork the repo or install it, keep reading.

Install

prereqs

• A linux box with CUDA or apple silicon (no flash linear attention support for this last one).
  • Rocm may work as well, not tested
• uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

As your own personal lab -> Fork this repo and clone it

git clone https://github.com/<your-github-user>/gradientlab.git
cd gradientlab/
uv sync

As a library

uv add gradientlab

Experiments

An example is under /experiments, a custom 22-layers, yet only 20M param GPT, which you can find under /modeling:

• PolyReLU ffn activation (works better than SwiGLU)
• parallel attention (from PaLM paper & Moondream)
• squeeze-and-excite narrow transformer backbone (an idea of mine for small lang models, prefering depth over width, inspired by computer vision)
• sigmoid gating post sdpa (paper by Qwen team)
• attn values heads expansion
• absolute position embeddings (I know)
• KV-cache support
• embed_dim != hidden_dim
• Trained on 3B italian tokens from fineweb2 in ~8 hours on a RTXA4000.
  • byte_level_tokenizer, couldn't use qwen3 tokenizer due to memory constraints (gpu poor) and weird torch.compile errors
• Slim notebook to demo model loading and generation.
• single-GPU trainer with trackio to track metrics

Each experiment entrypoint is located in __main__.py So you can run an experiment like this:

uv run -m gradientlab.experiments.exp20251016_0_lm_20m_polyrelu_lm_vanilla_fineweb_ita

The modeling/ folder under an experiment will contain all the modules your model is made of. Some notes:

• factory.py -> model factory, is where you will construct the models with specific parameters
• model_cfg.py -> model config class
• model.py -> your high-level model class, extending some hf class or mixins

Feel free to adapt the repo as you wish and share your learnings in the discussion section.

Publish

If you want to publish your own gradientlab-* project as library, just create a PyPI token and follow the official uv guide.

Generally as simple as:

uv build
UV_PUBLISH_TOKEN=pypi-your-token uv publish