Escarda-86M

~86M decoder I trained from scratch. Chat checkpoint. Laptop / free CPU is the point. Architecture is SpikeWhale: MLA, n-gram engram memory, hyper-connections, HRM refine, JEPA + multi-token-prediction auxiliaries.

Trained on Modal credits for the Small Models, Big Adventures Hackathon. I picked this one after a seed-controlled bake-off over 28 checkpoints plus a head-to-head battle test - coherence, instruction-following, and not collapsing into repetition.

Live demo: Quazim0t0/Escarda-86M-Chat

Related: base → Quazim0t0/Escarda-86M-Base (use that if you want to SFT it yourself).

Numbers: Evaluation.

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Model summary

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Architecture

Dense decoder. Flags match the released config.json.

These models sit on SpikeWhaleLM (~86M, 16 layers, hidden 640, 4096 context, 16,512 vocab, tied embeddings). Same stack as below.

Attention - Multi-head Latent Attention (MLA) + XSA

use_xsa=True, use_qk_norm=True

• MLA-style low-rank projections: queries and the output projection are LoRA-compressed (q_lora_rank=128, o_lora_rank=128). Attention params / KV stay small.
• Decoupled position encoding: each head splits into a RoPE part (qk_rope_head_dim=16) and a NoPE part (nope_head_dim=48). Some of the head has rotary position; the rest does not.
• Multi-query attention: num_key_value_heads=1 - all query heads share one KV head. Cheaper KV cache.
• QK-norm on the attention logits.

Engram n-gram memory

use_engram=True Hashes local n-grams (up to engram_max_ngram=3) into a learned table (engram_table_size=4096, engram_num_heads=2, engram_compress_dim=32) and gates that back into the residual (engram_gate_init_bias=-1.0, mostly off at init). Surface patterns without burning depth on them.

Hash-lookup layers

num_hash_layers=2 - MultiHeadHashLookup next to the token embeddings.

Hyper-Connections (not a plain residual)

use_hyper_connections=True (hc_mult=2, hc_sinkhorn_iters=20, hc_eps=1e-6) Learned width-expanded connections, Sinkhorn-normalized routing, instead of a single identity add.

HRM refinement

use_hrm_refine=True (hrm_refine_dim=128, hrm_refine_steps=1) Small Hierarchical Reasoning Model block. Extra latent pass over hidden states before the output head.

Feed-forward (MoE-capable, dense in this release)

The block can do DeepSeek-style sparse MoE (n_routed_experts=6, n_shared_experts=1, num_experts_per_tok=2, scoring_func=sqrtsoftplus). This checkpoint is dense (use_moe=False, moe_layers=[]). Latency stays boring and predictable.

Training-time auxiliary objectives

No extra cost at inference:

• JEPA (use_jepa=True, jepa_pred_dim=256, jepa_horizon=1, jepa_loss_weight=0.1)
  • Joint-Embedding Predictive loss on future latents.
• Multi-Token Prediction (MTP) (num_nextn_predict_layers=1, mtp_loss_weight=0.3)
  • DeepSeek-V3-style extra head, more than one next token.
• z-loss (zloss_coef=1e-4) for logit stability.

JEPA vs HRM. Escarda trains both HRM refine and JEPA (use_hrm_refine=True, use_jepa=True). Byrne drops JEPA and keeps HRM only.

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Tokenizer & chat format

SpikeTokenizer. Byte-level length-max (greedy longest-match), 16,512 vocab. Not BPE. Text → UTF-8 → latin-1 bytes → longest vocab key that fits. ChatML-aware. Atomic specials: <|im_start|>, <|im_end|>, <think>/</think>, <begin_solution>/<end_solution>, tool-call markers, plus <bos>/<eos>/<pad>/<unk>. Ships as a PreTrainedTokenizer (spike_tokenizer.py). Load with AutoTokenizer.from_pretrained(..., trust_remote_code=True).

<bos> (id 2) on every sequence. <|im_end|> and <eos> (id 3) end a turn.

One turn:

<|im_start|>{role}\n{content}<|im_end|>\n

Generation starts after a trailing <|im_start|>assistant\n.

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Inference

ChatML prompt, nucleus top-p 0.9, stop on <|im_end|>. This is what I used when it looked best:

import torch, torch.nn.functional as F
from model_v2 import SpikeWhaleLM        # custom architecture (ship with the repo)
from spike_tokenizer import SpikeTokenizer
from chat_format import format_chat, IM_END

tok = SpikeTokenizer("tokenizer.json")
model = SpikeWhaleLM.from_pretrained("Quazim0t0/Escarda-86M").eval()
end_id = tok.convert_tokens_to_ids(IM_END)

prompt = format_chat([{"role": "user", "content": "Explain photosynthesis in one sentence."}],
                     add_generation_prompt=True)
ids = torch.tensor(tok.encode(prompt)).unsqueeze(0)
out = model(ids, use_cache=True); past = out.past_key_values; last = out.logits[0, -1]
gen = []
for _ in range(120):
    p = F.softmax(last.float() / 0.3, -1)
    sp, si = p.sort(descending=True); cut = sp.cumsum(0) > 0.9
    cut[1:] = cut[:-1].clone(); cut[0] = False; sp[cut] = 0
    nxt = si[torch.multinomial(sp / sp.sum(), 1)].item()
    if nxt == end_id: break
    gen.append(nxt)
    out = model(torch.tensor([[nxt]]), past_key_values=past, use_cache=True)
    past = out.past_key_values; last = out.logits[0, -1]
print(tok.decode(gen, skip_special_tokens=True))

Escarda is not a stock transformers model. You need model_v2.py, config.py, spike_tokenizer.py, chat_format.py. Easiest path is the demo Space.

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Evaluation

Zero-shot multiple-choice, continuation log-likelihood on each task's val/test split. Standard error is binomial (sqrt(p(1-p)/n)).

Language modeling

byte_ppl is exp(sum_NLL_nats / total_UTF8_bytes) on WikiText-2 test (tokenizer-independent). BLiMP is logprob(good) > logprob(bad) on 12 paradigms × 150.

Chat checkpoint has the best BLiMP in the Escarda family even though Base has lower perplexity. PPL is not tracking capability here.

Standard small-model suite

Random: arc/hellaswag/openbookqa ≈ 0.25; winogrande/boolq ≈ 0.50. At this size a lot of it sits near chance. Signal is mostly piqa (0.58) plus winogrande/boolq.

ArithMark-2.0 (AxiomicLabs)

Multiple-choice integer arithmetic (n = 2,500, chance = 0.25).

Aggregate is flat. Underneath it is not. ~2× chance on multiplication and division, at/below chance on add/sub:

It actually learned multiplicative patterns. Not uniform guessing.

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What this is for / what it isn't

Short chat, simple how-tos, definitions, drafting. Fine-tune it or run it on-device. I wanted something that stays coherent and follows instructions without costing anything.

It is 86M. Factual recall and multi-step arithmetic are weak and it will sound sure when it is wrong - check anything that matters. It repeats and drifts; short, bounded replies work better. English-centric. No safety / RLHF. Don't put it in a sensitive setting without your own guardrails.

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Training

• Compute: Modal credits (Small Models, Big Adventures Hackathon).
• Pipeline: from-scratch SpikeWhale pretrain, ChatML SFT, then an RL-prep stage. Released rl_prep/final came out of the 28-candidate bake-off + battle test.
• Objectives: next-token CE + JEPA + MTP + z-loss.

Token budget & scaling

• Tokens: ~20B from-scratch (~28k steps), then ChatML SFT.
• Token/param: ~233 (20B / 85.7M). About 11-12× Chinchilla's ~20-tokens/param. Deliberately over-trained small model. Inference is the trade.

Fitting Chinchilla's data term to this run's pretrain loss:

L(D) ≈ 2.611 + 77,715 · D^(-0.537) (nats/token, R² = 0.92)

From that:

• Compute-optimal for this 86M ≈ 4.3B → 20B is ~4.6× past compute-optimal.
• Diminishing-returns knee ≈ 22.5B (where +1B buys < 0.005 nats). 20B lands right there.
• Parameter-bound, not data-bound at 20B: capacity term (~0.82 nats) beats the data term (~0.54). Extra tokens do little. Doubling to 40B is projected ~0.07 nats lower loss (~7% PPL) with basically no downstream gain. Next lever is more params, not more tokens.

Caveats: single-size fit (irreducible loss + capacity floor folded into one constant). Cosine-LR decay inflates the fitted exponent, so treat β as an upper bound. Token counts are anchored to ~20B and scale linearly if that figure is off.

⚠️ SFT was rushed. Small SFT, thrown together for the hackathon deadline. No real data mix. Weakest part of this release, not the base. Re-SFT from Escarda-86M-Base with a cleaner set would almost certainly look better. Treat this checkpoint as a rushed proof-of-concept. Use the base if you want to take it further.

Acknowledgements

Modal credits, Small Models, Big Adventures Hackathon. Apache-2.0. If you want to keep going on it, the base is the better starting point.

Citation

If you use this model, please cite:

@misc{escarda86m,
  title        = {Escarda-86M: A ~86M-parameter SpikeWhaleLM},
  author       = {Dean Byrne (Quazim0t0)},
  year         = {2026},
  howpublished = {HuggingFace, \url{https://huggingface.co/Quazim0t0/Escarda-86M}},
  note         = {Quazim0t0/Escarda-86M}
}

Update: format-blended SFT on the engram-repaired base

This revision applies the (behavior-preserving) engram repair, then a short instruction/format SFT on a 60/25/15 blend of HuggingFaceTB/smoltalk, GSM8K-train (with '#### N' reasoning), and MMLU-style ('Answer: ') examples -- so chat fluency improves while the benchmark output-formats are preserved rather than overwritten. Held-out (test-split) before->after:

MMLU acc 0.056->0.278, format 0.284->0.950; GSM8K '####' 0.005->0.750

Note: these are fluency + output-format gains. Benchmark accuracy remains near the floor for a model this size -- the SFT does not add reasoning ability.