Architecture graph
Architecture
Identical to Sorbet-25M: stock Qwen2 throughout, no custom code paths, natively supported by both transformers and llama.cpp.
| 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
v2 continues the v1 checkpoint through two training runs:
| Leg | Data mix (tokens) | LR schedule |
|---|---|---|
| cpt2 | fineweb-edu 70% / infiwebmath 10% / DCLM-baseline 20%, 0.8B tok | cosine, 8-bit AdamW |
| v2-final | FineWeb-HQ 65% / DCLM-baseline 20% / FineMath-4+ 15%, 1.7B tok | cosine peak 1e-4, fp32 AdamW |
Block-shuffled at 131,072 tok/step.
Benchmarks
All numbers zero-shot via lm-evaluation-harness, bf16, identical settings across checkpoints.
| Task | n | Random | acc | acc_norm |
|---|---|---|---|---|
| HellaSwag | 10,042 | 25% | 26.55 ±0.44 | 26.63 ±0.44 |
| ARC-easy | 2,376 | ~25% | 30.30 ±0.94 | 29.92 ±0.94 |
| ARC-challenge | 1,172 | ~25% | 18.60 ±1.14 | 22.44 ±1.22 |
| PIQA | 1,838 | 50% | 54.52 ±1.16 | 53.32 ±1.16 |
| ArithMark-3.0 | 1,000 | 25% | 32.90 ±1.48 | 33.00 ±1.49 |
Notes:
- Every score is at or above the Sorbet-25M baseline within error bars.
- ArithMark-3.0 (
AxiomicLabs/Arithmark-3.0) remains the strongest relative result (+8 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
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "CodeSoft/sorbet-v2-25m"
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.