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kennethp97
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Model Details
Model Provider
kennethp97
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Given a procedure and a scenario, the model emits an EDGE CHECKS: reasoning
block followed by a FINAL ANSWER: compliant|non-compliant line. The recipe
targets the free-form regime; gains concentrate there.
| regime | flip rate (base -> SFT) | anchor acc (base -> SFT) | plain (base -> SFT) |
|---|---|---|---|
| forced | 0.117 -> 0.188 | 0.557 -> 0.582 | 0.570 -> 0.608 |
| free-form | 0.219 -> 0.469 (+25.0pp) | 0.467 -> 0.664 (+19.7pp) | 0.576 -> 0.726 |
The lift is free-form-only (the regime the reasoning recipe targets); the gains concentrate on exception / hierarchy / threshold handles, while step-ordering stays flat (0.200 -> 0.225) -- the known structural bottleneck.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE = "Qwen/Qwen2.5-1.5B-Instruct"ADAPTER = "kennethp97/sft-arm-a-1p5b" tok = AutoTokenizer.from_pretrained(BASE, use_fast=True)tok.pad_token = tok.pad_token or tok.eos_token base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, ADAPTER)model.eval()Prompt format and a worked side-by-side eval against the base and the companion
DPO adapter (kennethp97/dpo-flip-1p5b) are in the combined eval notebook.
Qwen/Qwen2.5-1.5B-Instructverifier_reason
rows from the 5,020-row v0.4.0 corpus)EDGE CHECKS ... FINAL ANSWER
format above; deviation may degrade performance. Greedy (T=0) matches the
reported numbers.Adapter: Apache-2.0. Base model: under the Qwen2.5-1.5B-Instruct license.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE = "Qwen/Qwen2.5-1.5B-Instruct"ADAPTER = "kennethp97/sft-arm-a-1p5b" tok = AutoTokenizer.from_pretrained(BASE, use_fast=True)tok.pad_token = tok.pad_token or tok.eos_token base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, ADAPTER)model.eval()