Jev Fine Tuning
4 curated entries tagged fine-tuning.
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- Unsloth's guide to turning a generative LLM into a Jev-style decision model that returns a choice with a probability instead of text, plus a companion page for serving Laya behind the TypeSafe API.Jev Models & Reimplementations#community#fine-tuning#open-modelsCommunity
- 2.5 million typed decisions recast from 670 sources: choice, score, and noul rows with per-row licenses, soft annotator targets, and Jev-audited mix weights.Jev Benchmarks & EvaluationsDocs#community#evaluation#calibrationCommunity
- The 741,000-row distillation corpus behind AutoTrust's Jev students: choice, score, and noul rows with full target distributions across yuri and openjev source streams, Apache-2.0.Jev Benchmarks & EvaluationsDocs#community#evaluation#distillationCommunity
- 130GitHub starsAn open recipe for a 27B multimodal decision model trained with full-weight SFT on 73,000 examples over one H200, serving choice, noul and score on /v1/systemone with per-checkpoint provenance and released calibration plots.Jev Models & Reimplementations#community#calibration#qwenCommunity
denis-pplx/autojevPython
Jev articles
Original guides that mention Fine Tuning.
Base decision checkpoints sit near chance on purpose. The typed-decisions loop, the corpora behind the students, and the temperature fit that turns probabilities into thresholds.
Jev Practices & Patterns#fine-tuning#calibration#open-models
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