alok97

evo-lawbench-gpt-oss-120b-lora

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Result (LawBench 191-class, 913-case held-out test, top-1 accuracy)

approach	acc
prior SOTA	0.450
SIA gpt-oss-120b (W+H)	0.701
TF-IDF harness (no LLM)	0.760
best: this LoRA (vLLM, rope_theta fix) ⊕ TF-IDF ensemble	0.77

Beats SOTA and SIA's 0.701. Honest caveat: the winning 0.77 is an ensemble of this LoRA (served via vLLM with a rope_theta fix) and a TF-IDF char-ngram classifier; the LoRA's contribution is the marginal lift over the 0.760 harness. Naive LoRA inference without the rope_theta fix scored far lower.

Files

adapter_model.safetensors — the LoRA weights
adapter_config.json, tokenizer.json, chat_template.jinja

Replicate

Task + harness: github.com/evo-hq/evo-posttrainbench (branch evo-variant, src/eval/tasks/lawbench). Optimizer: evo 0.5.0-alpha.13 (github.com/evo-hq/evo, release/0.5). Run: scripts/run.sh run lawbench openai/gpt-oss-120b <hours>.

Model provider

alok97

Model tree

Base

openai/gpt-oss-120b

Adapter

this model

Modalities

Input

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Output

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