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ssurface
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Model Details
Model Provider
ssurface
Model Tree
allenai/Olmo-3-7B-Think
Input Modalities
Output Modalities
Supported Functionality
| Requested level | Accuracy |
|---|---|
| L1 | 88.2% |
| L2 | 70.2% |
| L3 | 58.8% |
| L4 | 48.0% |
| L5 | 3.0% |
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.
| Stage | GRPO on the merged conditioned SFT model |
| Conditioning | the level is named in the prompt, not selected by adapter |
| Engine | trl.GRPOTrainer on stock transformers, sdpa |
| LoRA | r=16, alpha=32 |
| Hardware | 1x NVIDIA A100 80GB |
markdown
Solve this using Level {N} ({Verbose|Concise|Symbolic|Shorthand|Extreme}).Problem: {your problem}
Stacks on the conditioned SFT model, not the raw base.
python
from transformers import AutoModelForCausalLMfrom 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-olmo3-7b-think-conditioned-sft")model = model.merge_and_unload()model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-olmo3-7b-think-conditioned-grpo")