User01110/cma-8M

CMA 8M

Channel-Mixing Attention at 7.85 million parameters.

A compact generalist language model built to study channel mixing at small scale.

7.85M parameters | 1,024-token context | 4,096-token vocabulary | Safetensors

Benchmark-selected at step 40,000 | Open SLM Leaderboard-style average: 36.21%

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Overview

CMA 8M is the benchmark-selected checkpoint of a 7,849,161-parameter Channel-Mixing Attention generalist causal language model. It pairs a compact nine-layer architecture with the unmodified native GPT-S 4,096-token tokenizer.

Detail	Value
Architecture	Channel-Mixing Attention causal LM
Parameters	7,849,161, with tied input/output embeddings
Checkpoint	Step 40,000
Training tokens	20,971,520,000
Context length	1,024 tokens
Vocabulary	4,096 native tokens
Weights	Safetensors

How CMA works

In each of the nine transformer blocks, causal token attention is followed by CMA in the feed-forward position. For every token, CMA reshapes the 288-channel hidden state into 12 chunks of 24, routes between those chunks with 3 heads, applies a signed blend and SiLU gate, then projects back to 288 channels.

Quick start

This repository contains a custom Transformers architecture. Load it with trust_remote_code=True to register CMA and the model's rotary convention.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "User01110/cma-8M"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    dtype="auto",
)
model.eval()

Generate a continuation:

import torch

prompt = "Channel-mixing attention is"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=80,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Architecture

Component	Configuration
Total parameters	7,849,161
Tied embedding parameters	1,179,648
Non-embedding parameters	6,669,513
Width / layers	288 / 9
Context length	1,024 tokens
Token-attention heads	6 query / 2 KV
CMA	chunk 24 / 3 heads / expansion 2
Positional encoding	Contiguous-half RoPE
Tokenizer	AxiomicLabs/GPT-S-5M at revision df47402
Vocabulary	4,096 native tokens
Runtime	PyTorch 2.5+ for native SDPA grouped-query attention
Weights	Safetensors

Input and output embeddings are tied.

Training mixture

All four sources are streamed natural-text corpora.

Source	Share of trained tokens
FineWeb-Edu 100BT shuffled	55.00%
Cosmopedia v2	25.00%
FineWeb-HQ	10.00%
FineMath 4+	10.00%

FineMath 4+ supplies high-quality mathematical explanations and reasoning as ordinary causal-language-model text.

Training trajectory

Across the logged evaluation checkpoints, normalized bits per byte (BPB) decreased from 3.7838 to 1.3520, while the Open SLM Leaderboard-style average increased from 30.66% to 36.21%.

Evaluation

Zero-shot results at step 40,000

The four lm-eval tasks use normalized accuracy when supplied by lm-eval 0.4.12, with float32 weights and softmax. ArithMark uses float32 weights and its official raw continuation log-likelihood-sum rule.

Benchmark	Accuracy
HellaSwag	28.19%
ARC-Easy	35.35%
ARC-Challenge	23.29%
PIQA	58.22%
ArithMark-2	29.12%
ARC mean	29.32%
Open SLM Leaderboard-style average	36.21%

The selection average is:

(HellaSwag + mean(ARC-Easy, ARC-Challenge) + PIQA + ArithMark-2) / 4

This matches the Open SLM Leaderboard formula. The checkpoint was selected at step 40,000 for its 36.21% average.

WikiText-103 validation

Metric	Value
Loss	2.9903
Perplexity	19.89
Normalized BPB	1.3520
Scored tokens	358,911
Normalized UTF-8 bytes	1,145,226

WikiText normalized BPB is reported separately and was not used for checkpoint selection.