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ssurface
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Model Details
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ssurface
Model Tree
allenai/Olmo-3-7B-Think
Input Modalities
Output Modalities
Supported Functionality
| Accuracy | |
|---|---|
| This adapter | 65.6% |
MATH-500 (n=500), greedy decoding, single-turn, no exemplars, no self-consistency.
Scored with the project's LaTeX-aware grader (see the scoring note below).
Scoring note. MATH answers are
\boxed{}, and the harness that produced the first pass of these evals looked for GSM8K's#### n. That silently scored three of these models at ~0%% when they were near 60%%. Numbers here come from the project's LaTeX-aware grader, which normalizes equivalent forms (\frac{14}{3}==14/3).
MATH training problems re-expressed at level L1 by a teacher model. MATH ships three levels rather than five — L1 anchor, L3 symbolic middle, L5 extreme — with the notation rules held identical to the GSM8K dialects and only the answer convention changed to .
\boxed{}This is the unfiltered corpus.
| 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.
markdown
Solve this using Level 1 (Verbose).Problem: {your problem}
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelmodel = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", torch_dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-math-olmo3-7b-think-sft-unfiltered-l1")tok = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")
bibtex
@misc{cot-compression-dialects,title = {Chain-of-Thought Compression Dialects},author = {Frolov, Anatolii},year = {2026}}