Byrne-86M

The main Byrne-86M chat model (OPD v2) — the recommended model for general use. A ~86M-parameter, from-scratch SpikeWhaleLM decoder (Multi-head Latent Attention, n-gram engram memory, hash-lookup layers, hyper-connections, HRM refinement, MTP) with a custom ChatML-aware tokenizer. Trained with Modal credits during the Small Models, Big Adventures Hackathon.

Related: base model → Byrne-86M-Base

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Quazim0t0/Byrne-86M", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Quazim0t0/Byrne-86M", trust_remote_code=True)

Architecture

These models are built on SpikeWhaleLM, a custom ~86M-parameter decoder-only transformer (16 layers, hidden size 640, 4096-token context, 16,512 vocab, tied input/output embeddings). It combines several non-standard components:

• Multi-head Latent Attention (MLA + XSA) — queries and the output projection are LoRA-compressed (rank 128); each head splits into a decoupled RoPE part (dim 16) and a position-agnostic NoPE part (dim 48); 10 query heads share a single KV head (multi-query attention), with QK-norm for stable logits.
• Engram n-gram memory — a gated associative memory that hashes local n-grams (up to trigrams) into a learned 4,096-entry table and mixes the result back into the residual stream.
• Hash-lookup layers (×2) — multi-head content-addressable features alongside the token embeddings.
• Hyper-Connections — learned, width-expanded residual connections mixed via Sinkhorn-normalized routing, in place of the plain residual add.
• HRM refinement — a Hierarchical Reasoning Model block that performs an extra latent "think a bit more" refinement pass over the hidden states before the output head.
• Multi-Token Prediction (MTP) — a DeepSeek-V3-style auxiliary training head predicting more than one next token (no inference cost).
• Feed-forward is dense (the block is MoE-capable, but MoE is disabled in this release).

JEPA vs HRM. The Byrne models are Non-JEPA: they are trained with HRM refinement only (use_hrm_refine=True, use_jepa=False). The sibling Escarda models add a JEPA (Joint-Embedding Predictive) auxiliary objective on top of HRM refinement.

Tokenizer

These models use SpikeTokenizer, a custom byte-level "length-max" (greedy longest-match) tokenizer with a 16,512-token vocabulary — not a standard BPE/HF tokenizer. Text is UTF-8 encoded, each byte mapped to a latin-1 character, then greedily matched against the vocab using the longest key that fits at each position. It is ChatML-aware, with atomic special tokens for framing and reasoning/tool markers (<|im_start|>, <|im_end|>, <think>/</think>, <begin_solution>/<end_solution>, tool-call markers) plus <bos>/<eos>/<pad>/<unk>. It ships as a PreTrainedTokenizer subclass (spike_tokenizer.py) and loads via AutoTokenizer.from_pretrained(..., trust_remote_code=True).

Evaluation

log-likelihood, acc_norm = byte-length-normalized).

Task	acc	acc_norm
arc_easy	0.3670	0.3468
arc_challenge	0.1894	0.2355
hellaswag	0.2815	0.2858
winogrande	0.5201	—
piqa	0.5756	0.5593
openbookqa	0.1460	0.2440
boolq	0.3865	—

ArithMark-2.0 (AxiomicLabs) — official metric is raw acc: 0.3096.

Language modeling: WikiText-2 byte_ppl (↓) 2.6839 · BLiMP (↑) 0.7033.