Toolkit Reference

Frankenstein Transformer

See `https://erickfmm.github.io/frankestein-transformer/index.html <https://erickfmm.github.io/frankestein-transformer/index.html>`_ for a web interface to configure your YAML!

Config-driven transformer experimentation toolkit with 33+ mixer architectures and 23 optimizer families.

Quick Start

Method	Command
uv (recommended)	`git clone https://github.com/erickfmm/frankenstein-transformer.git && cd frankenstein-transformer && uv venv && source .venv/bin/activate && uv pip install -e ".[train]"`
pip	`python -m venv .venv && source .venv/bin/activate && pip install -e ".[train]"`
conda	`conda create -n frankenstein python=3.9 && conda activate frankenstein && pip install -e ".[train]"`

Verify: frankenstein-transformer --help

Feature Matrix

Feature	Scale
Sequence mixer architectures	33 across 5 categories (Dense, Recurrent, Sparse, Gated, Latent)
Optimizer families	23 across 6 categories
Model classes	`frankenstein`, `frankensteindecoder`, `frankenstein_vit`
Training modes	Encoder (MLM) / Decoder (autoregressive) / Vision (patch prediction, classification, segmentation)
Normalization types	`layer_norm`, `dynamic_tanh`, `derf`
CLI subcommands	8
Web configuration UI	Streamlit schema-driven YAML builder
Quantized deployment	BitNet + checkpoint export pipeline
SBERT workflows	Training + inference (similarity, search, cluster, encode)

Architecture Decision Table

Model Class	Mode	Use Case
`frankenstein`	Encoder	Full-featured MLM pre-training with mixed attention, MoE, and all 33 mixer types
`frankensteindecoder`	Decoder	Autoregressive causal decoder for LLM-style generation; forces `mode: decoder`
`frankenstein_vit`	Encoder (vision)	Vision Transformer (arXiv:2010.11929) for image understanding: patch prediction, classification, segmentation (arXiv:2503.19108); forces mode: encoder, requires image: + dataset: blocks

See configs/README.md for preset details and docs/specs/ for architecture deep-dives.

CLI Command Reference

Subcommand	Purpose	Example
`train`	Run schema-validated training	`frankenstein-transformer train --config-name frankenstein --device auto`
`deploy`	Export checkpoint to deployment artifacts	`frankenstein-transformer deploy --checkpoint ckpt.pt --output deployed/ --format quantized`
`quantize`	Shortcut for quantized deployment	`frankenstein-transformer quantize --checkpoint ckpt.pt --output deployed_q/ --validate`
`infer`	Batch/interactive/benchmark inference	`frankenstein-transformer infer --model deployed/ --text "hello" --device auto`
`sbert-train`	Train sentence embedding model	`frankenstein-transformer sbert-train --output_dir ./sbert_out --batch_size 16 --epochs 4`
`sbert-infer`	SBERT similarity/search/cluster/encode	`frankenstein-transformer sbert-infer --model_path ./sbert_out --mode similarity --sentence1 "a" --sentence2 "b"`
`transformers-export`	Export to HuggingFace Transformers format	`frankenstein-transformer transformers-export --config-name frankenstein --output ./hf_export/`
`web-server`	Launch Streamlit config builder UI	`frankenstein-transformer web-server`

All model-executing commands accept --device auto|cpu|cuda|mps.

Mixer Categories

Category	Code Names	Description
Dense	`standard_attn`, `sigmoid_attn`, `gated_softmax_attn`, `titan_attn`	Full quadratic attention variants with positional encoding support
Recurrent	`retnet`, `retnet_attn`, `mamba`, `ode`	Retention networks, state-space models, and continuous-depth ODE layers
Sparse	`sparse_transformer_attn`, `longformer_attn`, `bigbird_attn`, `sparsek_attn`, `nsa_attn`, `sparge_attn` ⚠️, `fasa_attn` ⚠️, `msa_attn`, `sparda_attn`	Factorized, sliding-window, token-selection, and block-sparse (GQA-based) patterns
Gated	`gla_attn`, `deltanet_attn`, `gated_deltanet_attn`, `gated_deltanet2_attn`, `hgrn2_attn`, `fox_attn`, `kda_attn`, `engram_attn`	Linear attention with multiplicative gates, delta rules, and n-gram memory
Latent	`mla_attn`, `gqla_attn`, `mlra_attn`, `tucker_attn`, `iha_attn`, `gta_attn`, `mtla_attn`	KV-compression and head-mixing variants generalising GQA (latent attention, Tucker factorisation, interleaved pseudo-heads, temporal merging)

⚠️ sparge_attn and fasa_attn are eval-only — training raises a runtime error.

Configure via layer_pattern in YAML. See configs/schema.yaml for the full mixer reference table.

Optimizer Categories

Category	Optimizers	Count
Classical	`sgd_momentum`, `adamw`, `radam`, `adan`, `adopt`, `ademamix`, `lamb`	7
Variance Reduction	`mars_adamw`, `cautious_adamw`	2
Memory-Efficient	`adafactor`, `galore_adamw`, `lion`, `apollo`, `apollo_mini`, `q_apollo`	6
Schedule-Free	`schedulefree_adamw`, `prodigy`	2
Second-Order	`sophia`, `shampoo`, `soap`	3
Geometry-Oriented	`muon`, `turbo_muon`, `anon`	3

Parameters use prefixed keys: <optimizer_class>-<group>_<param> (e.g. adamw-lr_embeddings, muon-ns_steps). See configs/README.md for the full parameter reference.

Documentation Map

Resource	Content
configs/README.md	Schema walkthrough, preset details, optimizer parameter reference
configs/schema.yaml	Authoritative training config schema (source of truth)
docs/README.md	CLI reference and workflow guide
docs/paper.pdf	Technical report (English)
docs/paper-es.pdf	Technical report (Spanish)
docs/specs/	Architecture and feature specifications
docs/specs/vision.md	Vision Transformer (frankenstein_vit) spec — patch prediction, classification, segmentation
frankenstein-transformer.readthedocs.io	Full hosted documentation (specs, API, papers, bibliography)
docs/transformers_compatibility.md	HuggingFace export compatibility guide

Installation

uv (recommended)

git clone https://github.com/erickfmm/frankenstein-transformer.git
cd frankenstein-transformer
uv venv
source .venv/bin/activate
uv pip install -e ".[train]"

pip

python -m venv .venv
source .venv/bin/activate
pip install -e ".[train]"

conda

conda create -n frankenstein python=3.9
conda activate frankenstein
pip install -e ".[train]"

Verify installation:

frankenstein-transformer --help

Quick Training Example

Minimal YAML config (my_config.yaml) — only the 5 required model fields plus task; everything else uses FrankensteinModelConfig/TrainingConfig defaults:

model_class: frankenstein
model:
  vocab_size: 30522
  hidden_size: 256
  num_layers: 4
  num_heads: 8
  layer_pattern: [standard_attn, standard_attn, standard_attn, standard_attn]
training:
  task: mlm
  batch_size: 8
  max_length: 128
  mlm_probability: 0.15
  max_samples: 100000
  dataset_batch_size: 10000
  num_workers: 4
  cache_dir: "./temp_data/cache"
  optimizer:
    optimizer_class: adamw
    parameters:
      adamw-lr_embeddings: 1e-4
      adamw-lr_norms: 1e-4
      adamw-lr_ode: 1e-4
      adamw-lr_retnet: 1e-4
      adamw-lr_mamba: 1e-4
      adamw-lr_attention: 1e-4
      adamw-lr_other: 1e-4
      adamw-wd_embeddings: 0.01
      adamw-wd_norms: 0.01
      adamw-wd_ode: 0.01
      adamw-wd_retnet: 0.01
      adamw-wd_mamba: 0.01
      adamw-wd_attention: 0.01
      adamw-wd_other: 0.01
      adamw-betas_embeddings: [0.9, 0.95]
      adamw-betas_norms: [0.9, 0.95]
      adamw-betas_ode: [0.9, 0.95]
      adamw-betas_retnet: [0.9, 0.95]
      adamw-betas_mamba: [0.9, 0.95]
      adamw-betas_attention: [0.9, 0.95]
      adamw-betas_other: [0.9, 0.95]
      adamw-eps_embeddings: 1e-8
      adamw-eps_norms: 1e-8
      adamw-eps_ode: 1e-8
      adamw-eps_retnet: 1e-8
      adamw-eps_mamba: 1e-8
      adamw-eps_attention: 1e-8
      adamw-eps_other: 1e-8
  scheduler_total_steps: 1000

Unspecified model fields fall back to FrankensteinModelConfig defaults (num_loops=2, dropout=0.1, norm_type=dynamic_tanh, use_moe=true, ffn_activation=silu, etc.). Unspecified training fields fall back to TrainingConfig defaults (scheduler_type=cosine, grad_clip_max_norm=5.0, gpu_temp_guard_enabled=true, etc.). Override only what you need to change.

Run:

frankenstein-transformer train --config my_config.yaml --device auto

List available named presets:

frankenstein-transformer train --list-configs

Vision Transformer (frankenstein_vit) Example

The frankenstein_vit model class (arXiv:2010.11929) splits images into patches, embeds them, and processes the sequence through the same HybridLayer stack as the text models. It supports three tasks: patch_prediction (autosupervised masked patch prediction), classification (image classification), and segmentation (per-pixel or EoMT query-based, arXiv:2503.19108).

Minimal classification YAML (vit_config.yaml):

model_class: frankenstein_vit
model:
  dims:
    hidden_size: 768
    num_layers: 12
    num_heads: 12
    layer_pattern: [standard_attn]
    mode: encoder
  norm: {type: layer_norm}
  use_moe: false
  use_bitnet: false
  ffn_activation: gelu
  ffn_hidden_size: 3072
image:
  image_size: {height: 224, width: 224}
  patch_size: 16
  in_channels: 3
  pos_embedding_type: learned_1d
  cls_token: true
  pooling_mode: cls
  num_classes: 10
dataset:
  dataset_name: cifar10
  rescale: {height: 224, width: 224}
training:
  task: classification
  batch_size: 512
  num_epochs: 90
  optimizer:
    optimizer_class: adamw
    parameters:
      adamw-lr_other: 0.001
      adamw-wd_other: 0.1
  classification:
    batch_size: 512
    num_epochs: 90
    learning_rate: 0.001

Run: frankenstein-transformer train --config vit_config.yaml --device auto

See configs/frankenstein_vit_base.yaml for the full ViT-Base/16 preset and docs/specs/vision.md for the complete specification.

License

Apache License 2.0 — see LICENSE for full text.