Category

Qwen-Image-2.1

Unified text-to-image and prompt-based image editing model that natively outputs RGBA and accepts up to 10 reference images. Compact 7B visual generator optimized for 2K outputs and efficient inference; research-licensed under the Qwen Research License.

Explains how Jev turns input state into typed decisions and probabilities without generating text. Introduces parallel sampling and RLCD training, with workflow evaluations and caveats for software automation.

Ternary Bonsai 2 27B (Bonsai 2 27B)

Compresses a 27B-class multimodal model into end-to-end ternary weights to run 27B reasoning on-device: 5.9–8.6 GB deployed footprint, 262K-token context, ~98.2% of FP16 benchmark performance; ships MLX and GGUF packs and runs on Apple MLX and CUDA.

openjev

Turns Qwen3.5 into a 3-way NLI cross-encoder (entailment/contradiction/neutral) for zero-shot reranking, grading, content guarding, or action selection. Provides a pretrained Qwen3.5-4B checkpoint, helper utilities (OpenJevCrossEncoder, LatentMLPHead), and optional 35B MoE latent heads for per-task scoring.

Needle 3

Runs locally on constrained devices to turn text into guaranteed-parsable JSON tool calls, typed structured extractions, or sentence embeddings. Delivered as a single compact weights file (8–29 MB) with a laddered 2–20-layer design, low-bit quantisation and calibrated confidence scores for on-device apps.

Bonsai 2 27B — GGUF

A 27B-class language model packaged in GGUF with end-to-end ternary weights for on-device or single-GPU llama.cpp inference; reduces FP16 footprint to ~5.9–7.2 GB while retaining ~98% of baseline performance and supporting up to 262K tokens.

Xing4.0-29B-A4B

A 29B-parameter mixture-of-experts LLM for long-context reasoning and agent-style workflows, with native 256K context and Ascend NPU co-optimizations. Compatible with Transformers/vLLM ecosystems; suitable for long-horizon agents and domain fine-tuning.

Ming-Image-0.1-Design

Generates complete UI, poster and infographic layouts from text prompts, rendering legible in-image text and producing native RGBA output with transparent backgrounds. Suited for text-rich visual design workflows and pairs with a companion model that decomposes designs into editable layers.

Laya

Fast, non-autoregressive decision engine that answers typed questions (choice/score/noul) over text or JSON states with calibrated probabilities and confidences in a single forward pass. Suited for routing, triage and moderation workflows; includes a Router to pick checkpoints per request.

Laya Multilingual

A non-autoregressive, multilingual System‑1 decision model that answers typed questions (choice/score/noul) over text or JSON in a single forward pass with probability distributions. Key differences: calibrated-probability design, sub-35ms GPU latency, and built-in multilingual routing; requires per-workflow recalibration and validation on long or low-resource languages.

Jev-Omni

Converts multimodal inputs (text, image, audio, video) plus a question and options into calibrated probability distributions over choices. Built on Gemma 4 12B with a 30,000-question fine-tune, optimized for per-question decision classification and low-latency inference (Apache-2.0).

Hemmingway-1

Generates a single, ready-to-send short message (email, text, or note) tuned to sound human; a 27B fine-tuned LLM with open Apache-2.0 weights, optimized to return one concise draft rather than multiple options or explanatory preamble.