cot-dialect-olmo3-7b-think-conditioned-grpo

Olmo-3-7B-Think — single conditioned model

The alternative design: one adapter asked to serve every verbosity level by naming the level in the prompt, instead of one adapter per level. This is the comparison that motivates the per-level family.

Results — GSM8K test (n=1317)

The model holds up while the requested dialect stays close to what it settled on, then collapses at L5 (3.0%). A single set of weights cannot hold five dialects at once; asking for the extreme one gets neither the format nor the answer.

Training

Usage

Solve this using Level {N} ({Verbose|Concise|Symbolic|Shorthand|Extreme}).
Problem: {your problem}

Stacks on the conditioned SFT model, not the raw base.

from transformers import AutoModelForCausalLM
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-conditioned-sft")
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-olmo3-7b-think-conditioned-grpo")

Limitations

• A design comparison, not a recommended model. The per-level adapters in this collection are the ones the results are reported on.
• Single seed.