
Params	25,185,920 (~87% non-embedding)
Layers / hidden	14 / 384
Attention	GQA 6 heads / 2 KV heads, RoPE θ=100k
FFN	1024 (SwiGLU)
Context	4096
Vocab	8,192 custom byte-level BPE (tied embeddings)
Precision	bf16

Training data

0.8B-token weighted mix: fineweb-edu 70% / infiwebmath 10% / DCLM-baseline 20%, block-shuffled. ~3000 steps at 262,144 tok/step, cosine LR, 8-bit AdamW.

Benchmarks

Task	n	Random	acc	acc_norm
HellaSwag	10,042	25%	26.52 ±0.44	26.12 ±0.44
ARC-easy	2,376	~25%	29.50 ±0.94	29.59 ±0.94
ARC-challenge	1,172	~25%	17.66 ±1.11	22.95 ±1.23
PIQA	1,838	50%	54.46 ±1.16	53.43 ±1.16
ArithMark-3.0	1,000	25%	32.70 ±1.48	32.90 ±1.48

Notes:

ArithMark-3.0 (AxiomicLabs/Arithmark-3.0) is the strongest relative result (+7.9 pts over random), consistent with the math share of the pretraining mix.
ARC-challenge raw accuracy sits below chance due to a length bias in unnormalized scores; acc_norm is the meaningful metric there.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "CodeSoft/sorbet-25m"
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16").to("cuda")
tok = AutoTokenizer.from_pretrained(repo, subfolder="tokenizer")
ids = tok("Once upon a time", return_tensors="pt").input_ids.cuda()
print(tok.decode(model.generate(ids, max_new_tokens=64)[0]))

Limitations

Expect shallow world knowledge and weak performance on knowledge-heavy benchmarks due to the model's small parameter count and limited training budget.

License

Apache-2.0.