Instructions to use ssurface/cot-dialect-olmo3-7b-think-sft-l2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ssurface/cot-dialect-olmo3-7b-think-sft-l2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think") model = PeftModel.from_pretrained(base_model, "ssurface/cot-dialect-olmo3-7b-think-sft-l2") - Notebooks
- Google Colab
- Kaggle
Olmo-3-7B-Think — L2 dialect (Structured shorthand)
A LoRA adapter that makes allenai/Olmo-3-7B-Think reason at compression level L2 — compressed prose / bulleted steps.
Results
| Accuracy | |
|---|---|
| This adapter | 86.3% |
GSM8K test (n=1317), greedy decoding, single-turn, no exemplars, no self-consistency.
Training data
GSM8K train, re-expressed at level L2 by a teacher model: 6950 examples, median chain length 140 characters inside <think>.
Across the family the median chain runs from 532 characters at L1 to 16 at L5 — a 33x span. An L2 chain looks like this:
- Sandoval: 12
- Hawkins: 12 / 2 = 6
- Sloan: 12 + 10 = 22
- Total: 12 + 6 + 22 = 40
Training setup
| Stage | supervised fine-tuning (distillation) |
| Engine | HuggingFace transformers + peft |
| LoRA | r=16, alpha=32, dropout=0.05 |
| Epochs | 3 |
| Learning rate | 2e-4, cosine, warmup 0.03 |
| Batch | 16 x 4 grad-accum = 64 effective |
| Max sequence | 1024 |
| Precision | bf16 |
| Hardware | 1x NVIDIA A100 80GB |
Loss is on the completion only, with prompt lengths precomputed at load time rather than found by pattern search — the pattern-search collator silently masked nothing, which let the base model's tool-calling prior leak into the chains.
Usage
Solve this using Level 2 (Concise).
Problem: {your problem}
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-olmo3-7b-think-sft-l2")
tok = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")
Limitations
- Trained and evaluated on math word problems only.
- Accuracy falls with problem difficulty, fastest at the compressed levels.
- Single seed unless the repo name says otherwise; differences of a couple of points are within noise (95% half-width ~2.7 pp at n=1317, ~4.4 pp at n=500).
Citation
@misc{cot-compression-dialects,
title = {Chain-of-Thought Compression Dialects},
author = {Frolov, Anatolii},
year = {2026}
}
- Downloads last month
- 16
Model tree for ssurface/cot-dialect-olmo3-7b-think-sft-l2
Base model
allenai/Olmo-3-1025-7BDataset used to train ssurface/cot-dialect-olmo3-7b-think-sft-l2
Collection including ssurface/cot-dialect-olmo3-7b-think-sft-l2
Evaluation results
- Accuracy (exact match) on GSM8Ktest set self-reported86.300