localjevGitHub Next's local Jev bridge: a Bun/TypeScript POST /v1/systemone that translates typed decision questions into prompts for DiffusionGemma behind any OpenAI-compatible endpoint.
Why switchBoth serve a local Jev-compatible /v1/systemone so TypeSafe's SDK works against localhost; kev trains a LoRA + readout head so the probabilities come from weights, LocalJev prompts a general model for a JSON probability vector — portable to any OpenAI-compatible backend, but self-reported rather than read from logits.
Full comparison → contextgemDeclarative LLM extraction from documents: describe Aspects and Concepts in plain language, get structured values back with paragraph- or sentence-level references and built-in justifications.
Why switchBoth turn one document into structured typed values; contextgem prompts an LLM declaratively per Aspect/Concept, kev trains a head that answers every question in one pass and returns calibrated probabilities instead of generated text.
Full comparison → CLMContrastive Language Models: CLM-8B, an open System-1 decision model scoring states against actions — typed choice/score/yes-no answers on a TypeSafe-compatible API, up to 9x faster than Jev.
Why switchBoth are open stand-ins for TypeSafe's Jev; kev reproduces it as a LoRA + readout head on Qwen, CLM trains a contrastive state-action model and replays Jev requests unchanged.
Full comparison → AnyJevNokia research: turn any open LLM into a Jev-style decision model. Typed choice, yes/no or score from one prefill, de-biased with no labels or a fitted head, served on vLLM.
Why switchBoth turn an open LLM into a typed decision model read from one prefill; kev trains a LoRA and readout head, AnyJev needs no training at L0 and fits a closed-form head from 100 to 300 labels at L2.
Full comparison → needleA 45M-parameter tool-calling model shipped as one 14MB binary that runs a full session in ~28MB RAM — grammar-constrained JSON, calibrated confidence, tool retrieval, LoRA fine-tuning.
Why switchSame bet from two angles: replace a prompted frontier model with a tiny purpose-built one. needle is 45M params for grammar-constrained tool calls, kev is a LoRA+head on Qwen for typed decisions with probabilities.
Full comparison → layaNon-autoregressive decision engine: typed choice, score and yes/no answers over text in one forward pass (~33 ms), 100+ languages, calibrated probabilities, a router picking the checkpoint.
Why switchBoth answer many typed questions about one text in a single pass with calibrated probabilities; kev puts a LoRA and readout head on a Qwen LLM, laya ships its own small non-autoregressive checkpoints with a multilingual router.
Full comparison → strands-deciderStrands' small decision model (2B, Qwen3.5-based): pick between options, answer yes/no or rate on a scale with calibrated confidence. CLI and /v1/systemone server on CUDA, MPS, MLX or CPU.
Why switchBoth answer many typed questions about one state in a single pass with calibrated probabilities; kev reproduces Jev as a LoRA head on Qwen, strands-decider ships its own trained 2B checkpoint.
Full comparison → DSPyProgram — don't prompt — your language models. Compile declarative pipelines into optimized prompts.
Why switchBoth refuse hand-tuned prompts for classification-shaped work: DSPy compiles the prompt around a large model, kev trains weights so there is no prompt and no decoding at all.
Full comparison → MLX-LoRA-StudioNative Mac app for on-device LLM fine-tuning via mlx-lm-lora: pick a model, choose SFT/LoRA/DPO-family algorithms, watch loss fall live, push to Hugging Face. No cloud, no code.
Why switchBoth get a LoRA trained on your own Mac; mlx-lora-studio is the general GUI over any model and SFT/DPO recipe, kev is one opinionated decision-model recipe (~1h45m for 0.5B on an M5) that ships its own server and eval suites.
Full comparison →