Tokle-3M

Model Summary

Tokle-3M is a decoder-only language model with 2.91M parameters. It was first trained on 12B tokens with SPAB (Static Pairwise Attention Bias), a frozen table of 8.39M token-pair association scores built from Pointwise Mutual Information (PMI) over the training corpus, giving 11.3M parameters in total during this stage. During training, for every query-key pair, SPAB hashed the two token IDs into the table, retrieved their PMI value, scaled it by a learned per-head factor, and added it to the attention logits before softmax.

After this stage, the SPAB table was removed and the model was trained for an additional 0.5B tokens to distill the knowledge in the SPAB matrix into its own layers. As a result, Tokle-3M runs entirely on its 2.91M parameters at inference, with no SPAB table required.

Model Architecture

Parameter	Value
Architecture	Decoder-only transformer (RMSNorm, RoPE, GQA, SwiGLU)
Layers	9
Hidden size (d_model)	144
Attention heads	3
KV heads (GQA)	1 (multi-query attention)
Head dim	48
FFN intermediate size	432
Max sequence length	512
Tie word embeddings	Yes
Precision	FP32 weights
Parameters	2.91M

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "techdotus/Tokle-3M"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()

ids = tok("The climate change", return_tensors="pt")
with torch.no_grad():
    out = model.generate(**ids, max_new_tokens=32, do_sample=False,
                         repetition_penalty=1.3)  # greedy
print(tok.decode(out[0], skip_special_tokens=True))

Benchmark Results

All scores are 0-shot acc_norm, using the Open SLM Leaderboard methodology.

HellaSwag	ARC-Easy	ARC-Challenge	PIQA	ArithMark-3
27.20%	34.85%	23.98%	55.01%	40.80%

Ablation: SPAB vs. Distilled

Stage 1 model (SPAB active) vs. the released Tokle-3M (SPAB removed and distilled).

Model	Params	Int Index	HellaSwag	ARC-Easy	ARC-Chal	PIQA	ArithMark-3
Tokle-SPAB-3M	11.3M (2.91M trainable + 8.39M frozen)	9.16	27.22%	34.68%	24.49%	54.95%	41.70%
Tokle-3M	2.91M	8.92	27.20%	34.85%	23.98%	55.01%	40.80%

Comparison Results

All scores are 0-shot acc_norm, using the Open SLM Leaderboard methodology. Scores for the other models are from the Open SLM Leaderboard. Bold marks the best result in each column.

Model	Params	Int Index	HellaSwag	ARC-Easy	ARC-Chal	PIQA	ArithMark-3
Tokle-3M (Tech.us)	2.91M	8.92	27.20%	34.85%	23.98%	55.01%	40.80%
Ember-2 (SurjoLabs)	2.96M×2	7.21	27.28%	33.42%	22.01%	55.11%	35.90%
BananaMind-2-Micro (BananaMind)	2.9M	6.01	28.27%	33.12%	21.93%	53.21%	34.00%
GPT-S-1.4M (Axiomic Labs)	1.4M	5.40	26.89%	31.57%	21.93%	55.17%	30.20%

Training Details

Tokle-3M was trained in two stages on the same data mixture.

Stage	Tokens	SPAB	Parameters
1. Pretraining	12B	Active (frozen PMI table)	11.3M (2.91M trainable + 8.39M frozen)
2. Distillation	0.5B	Removed	2.91M

Stage 2 lets the trained weights absorb the prior the SPAB table had been providing, so the released model is self-contained rather than losing that knowledge when the table is removed.

Training Data

We trained on a curated mixture with a strict cleaning pipeline that also removed topics not useful for a model of this size.

Source	Percentage
FineWeb-Edu	43.1%
Cosmopedia	24.3%
OpenMathInstruct-2	13.5%
Tiny Strange Textbooks	9.0%
MegaScience (medicine & biology, custom curated)	5.0%
High-Quality English Sentences	3.0%
ScienceQA	1.2%
Orca-Math Word Problems 200k	0.9%
Total	100%

• Tokenizer: all data was tokenized with the model's 5,048-token BPE tokenizer, and 1% was held out for validation.
• Blending: sources were blended per dataset using the weights above.

Limitations

• Tiny model: with 2.91M parameters and 144-dim hidden states, generations are often repetitive, incoherent or factually wrong. The model is a research artifact for studying small-scale LMs, not an assistant.
• Short context: 512 tokens maximum. RoPE tables are not built beyond that length.
• English only: trained on English web, educational, synthetic and math text.
• Not instruction-tuned or safety-aligned: it may reproduce biases present in web data.

License

Model weights and code: MIT.

Citation

@misc{tokle2026,
  title        = {{Tokle-3M}: Pointwise Mutual Information as a Removable
                  Inductive Bias for Self-Attention},
  author       = {{Tech.us Team}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/techdotus/Tokle-3M}}
}