Atome LM

A reference implementation of a routed-ternary tiny language model with a bit-exact Python ↔ C99 inference engine, sized for microcontroller-class RAM budgets.

60K-default-parameter LM combining three known ideas into one open kit: ternary weights (after BitNet b1.58), a hybrid SSM + sparse-attention + local-conv block routed per token (after Hymba and MossNet), and a byte tokenizer at super-tiny scale (after Guertler 2024). The contribution is integration, not architecture: a complete train → ternary export → base-3 packing → C99 inference path, with bit-exact Python ↔ C parity enforced by tests.

Quick links:

• 📄 Architecture writeup: PAPER.md
• 🔬 Honest results, including the 944 K reversal: HONEST_RESULTS.md
• 🌐 Live in-browser demo (no install): atomelm.com/demo.html
• 🏠 Project home: atomelm.com

Get the kit: training code, C engine, benchmarks, paper, and trained weights — all in this repository, released under the Apache 2.0 License. Train your own checkpoint with scripts/train_demo.py in ~30 min on a CPU, or run the bundled 944 K checkpoint immediately.

MCU status: QEMU ARM (Cortex-M3, MPS2-AN385) parity passes to FP32 epsilon. Silicon bring-up is not done in this repository. For real device deployment we sell integration — silicon bring-up, the Atome Secure Boot Pack (signed .atome blobs, dev/prod flags, per-platform secure-boot, attestation), per-platform hardening — at atomelm.com.

Weights are included in checkpoints/:

• atome_944k.bin (271 KB) — the packed C-engine blob (ATOME01 format), loadable directly by the inference engine.
• atome_1m_v1.pt (3.7 MB) — the PyTorch source checkpoint that produced it; use this to fine-tune or to re-export at different #defines.
• vanilla_1m_v1.pt (3.7 MB) — the FP32 vanilla GPT baseline used for the 944K A/B reversal in HONEST_RESULTS.md; shipped so you can reproduce the comparison end-to-end.

The 944K checkpoint is a research-demo artifact, not a product: it is narrow, sometimes incoherent, and trained on a single corpus. It is here to make the architecture runnable, not to set a quality bar. Reproduction is ~$1–2 of CPU/GPU using the included training code; nothing in this kit is a reproduction barrier.

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Reproducible result, narrow regime

On TinyStories, 3000 steps, single seed: at fixed parameter count Atome's routed-ternary block reaches 6.31 ppl vs 8.12 for a vanilla GPT-FP32 baseline (−22 %); at fixed flash budget 6.31 vs 13.10 (−52 %). Disk footprint is 16× smaller at param-match (15.1 KB vs 237.5 KB).

The result reverses at 944 K parameters, where the vanilla FP32 baseline wins by ~11 %. Atome's bet is deliberately the sub-1M, MCU-class regime; above it ternary's capacity ceiling closes the gap and overruns it. Full reproduction in FRONTIER.md, full honest reading including the reversal in HONEST_RESULTS.md.

Why

Datacenter LLMs assume datacenter RAM. A $2 microcontroller stuck on a wall in a remote sensor, a hearing aid, a battery-powered toy, or a thermostat doesn't have it. Atome LM is the model design end of that constraint:

• Ternary weights ({-α, 0, +α} per tensor, BitNet b1.58 style). No float multiplies in the matmul at inference.
• 3-pathway block (local depthwise conv, diagonal SSM, top-k sparse attention) mixed by a per-token soft router. Designed to match the Atome C99 engine struct exactly so trained checkpoints export to flash and run with bit-exact parity between Python and C.
• Byte tokenizer. No BPE table to ship.
• Router entropy as a calibration signal. The per-token router distribution's entropy is observable for free at every position. In our larger V2 production model (3 M parameters, mixed-domain training) entropy is observed to track out-of-domain inputs and to correlate with per-token loss. At Atome-LLM's engine-default 60 K-parameter scale on a single narrow corpus the signal is exposed identically — but its calibration as an uncertainty estimator at that scale has not been measured here.

What this is and isn't

• Is: the Python training side and architecture for a ternary LM that runs on cents-class hardware.
• Isn't: a general-purpose chatbot. At engine-default config (d_model=64, n_layers=4) the model is roughly 60 K parameters and exports to about 20 KB of flash. Train it narrow — a single domain (embedded-system Q&A, command-line help, a single FAQ) — and it speaks fluently inside that scope. Going wide at this size produces incoherent output; that is a reflection of capacity, not of the architecture. For more headroom, raise d_model and n_layers (e.g. d_model=128, n_layers=6 ≈ 600 K parameters, ~150 KB packed) and re-export with the matching #defines.

Install

./install.sh          # CPU-only venv + dependencies + environment check

Or manually: pip install -e . (Python ≥ 3.10, PyTorch ≥ 2.0). New here? QUICKSTART.md is the 60-second path from clone to a microcontroller-ready model.

Quick start

import torch
from atome_llm.core.atome_lm import AtomeLM

# Defaults match the Atome C99 engine's compile-time #defines:
#   d_model=64, n_layers=4, d_head=16, top_k=4, kernel=5, vocab=256.
model = AtomeLM()
print(f"params: {model.parameter_count():,}")

ids = torch.randint(0, 256, (1, 32))
logits = model(ids)                     # (1, 32, 256)
loss = model.loss(ids[:, :-1], ids[:, 1:])

# Per-layer per-token uncertainty signal — no extra training:
ent_per_layer = model.router_entropies(ids)   # list of (B, L) tensors

Train a tiny demo

python scripts/train_demo.py --data path/to/text.txt --steps 1000 \
    --d-model 64 --n-layers 4 --output checkpoints/atome_demo.pt

A built-in build_corpus.py fetches a few permissively-licensed sources (tinystories, esp-idf, mcu-wikipedia) for smoke training:

python scripts/build_corpus.py --source tinystories --max-bytes 500000 \
    --output data/tinystories.txt

Try a checkpoint

python scripts/demo.py --checkpoint checkpoints/atome_demo.pt
# or, with no checkpoint, sanity-check the plumbing:
python scripts/demo.py --random --temperature 0.8 --top-p 0.9

The REPL prints the continuation and per-layer router-entropy bars over the prompt — the metacognition signal that's exposed for free.

Sampling

AtomeLM.generate(...) defaults to greedy argmax (matching the C engine's atome_predict_next). Optional temperature, top_p, top_k, and generator= arguments enable nucleus / top-k sampling with seeded reproducibility.

Benchmark

python scripts/benchmark.py            # tiny / default / large

CPU forward + generate latency at three representative configs. Useful as a regression check after architecture changes; not an MCU number.

Export to a microcontroller

python scripts/export_to_atome.py \
    --checkpoint checkpoints/atome_demo.pt \
    --output checkpoints/atome_demo.atome \
    --header

This produces a .atome flat binary you can #include from C and load with atome_load(...) from the Atome C99 engine. At default config the binary is well under 100 KB — fits comfortably on ESP32-S3, STM32F4, RP2040, nRF52840, ESP32-C3.

Architecture

x → LayerNorm → ┬─→ Local  (depthwise causal conv k=5)        ─→┐
                ├─→ State  (diagonal SSM, O(L))                 ─→ Σ → +x
                └─→ Sparse (top-k attention, O(L·k))            ─→┘
                        ↑              ↑
                        │              router weights r ∈ Δ per token
                        └──────────────┘

Three pathways. Three different inductive biases. One shared per-token router that learns which pathway is most appropriate for each position. The router's per-token entropy is exposed as a free per-position uncertainty signal at every layer.

The full architecture story is in PAPER.md.

Tests

pytest -q

License

Apache License 2.0 — see LICENSE and NOTICE.

The kit is fully open: use, modify, redistribute, and ship in commercial products without per-seat or per-device fees. The Apache 2.0 patent grant covers the 3-pathway routed-ternary architecture as released here.

The released checkpoints in checkpoints/ (atome_944k.bin, atome_1m_v1.pt, vanilla_1m_v1.pt) are likewise Apache-2.0. They are reference / research artifacts, not products. Commercial integration — silicon bring-up, the Atome Secure Boot Pack (signed .atome blobs, dev/prod flags, per-platform secure-boot, attestation), per-platform hardening, custom-domain fine-tuning of the larger internal V2 model — is available at atomelm.com.

Citation

@software{atome_llm_2026,
  title  = {Atome LM: a tiny ternary language model for microcontroller deployment},
  author = {Atome LM contributors},
  year   = {2026},
  note   = {Apache 2.0, https://atomelm.com},
}