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alok97/evo-lawbench-gpt-oss-120b-lora
evo-lawbench-gpt-oss-120b-lora is a machine learning model from alok97. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
LoRA adapter (r=32, alpha=64) on openai/gpt-oss-120b, trained on the LawBench 191-class charge-prediction train split (5,332 examples) on 4× H100, produced by an autonomous evo run (evo 0.5.0-alpha.13; whole-system op…
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.safetensors95.6 MB · 77%
From the Hugging Face model README
LoRA adapter (r=32, alpha=64) on openai/gpt-oss-120b, trained on the LawBench 191-class charge-prediction train split (5,332 examples) on 4× H100, produced by an autonomous evo run (evo 0.5.0-alpha.13; whole-system optimize loop with the concurrent meta controller).
| 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.
adapter_model.safetensors — the LoRA weightsadapter_config.json, tokenizer.json, chat_template.jinjaTask + 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>.