Zircon v2 is a 0.6B-parameter decision model from Fahrenheit Research. It runs fully on-device on Apple silicon. You give it an email, a message or a pending task plus a set of options, and it returns a calibrated probability for each option in under 50 ms per decision. (1) MacBook Pro (Apple M5), 8-bit weights, median. Speed varies by hardware. (2) Fahrenheit Research internal testing, September 2026, on held-out emails not seen in training. (3) Fahrenheit Research internal testing, September 2026. 400 cases (2,000 decisions) from the public LocalLLaMA typed-decisions test set. (4) Fahrenheit Research internal testing, September 2026, on game seeds not seen in training. Built on Qwen3-0.6B…
Open weights
apache-2.0
596M parameters
40,960 tokens
mlx
English | 简体中文 Jev-Qwen3Guard-Gen-Domain-0.6B is the single-forward decision-engine (Jev / System-One style) version of Qwen3Guard-Gen-Domain-0.6B, a generative guard model for the Hong Kong elderly-care domain. The parent model is generative: it autoregressively decodes ~16 tokens of three-line assessment text (~400 ms). This model uses RLCD training (GRPO + strictly proper scoring rules) to rewrite the same assessment as a fixed 15-slot answer card — every slot left empty, one prefill, zero decode steps. Reading next-token probabilities at each slot anchor yields the full decision: The safety taxonomy (13 categories = 9 general + 4 HK elderly-care additions), the dual evaluation modes…
Open weights
apache-2.0
596M parameters
32,768 tokens
transformers
More details please refer to our Github: FlagEmbedding. Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. You can select the model according your senario and resource. - For multilingual, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-gemma - For Chinese or English, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-minicpm-layerwise. - For efficiency, utilize BAAI/bge-reranker-v2-m3 and the low layer of BAAI/bge-reranker-v2-minicpm-layerwise.…
Open weights
apache-2.0
568M parameters
8,194 tokens
sentence-transformers
Jev-LCT-Qwen2.5-0.5B is the ultra-lightweight edge edition of the Jev-LCT System-One decision family. Weighing only ~1.9 GB in full bfloat16 weights, it is optimized for high-throughput API gateway routing, edge robotics, Raspberry Pi, and mobile deployment. - ~50ms 极低延迟：专为高并发 API 网关路由、实时内容审核设计； - MMLU 达 50.0%：大幅超越参数相近的判别模型（Laya 33.3%, Open-Jev 35.0%）； Apache License 2.0. Full repository at GitHub.
Open weights
apache-2.0
494M parameters
32,768 tokens
transformers
Mapika/decider-0.8b v1 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 1.01 GB against 1.5 GB for the bf16 checkpoint. Quantized and measured by LLM Tech; the model, its training and its evaluation protocol are Mapika's. Read the bf16 card for what the model is and how it was trained. The base revision is a0a01d6f8135298f400a8c856b355793012ae971. Tokenizer, chat template, generation config and deciderconfig.json (temperatures included) are the author's files unchanged, apart from the version and quantization fields. Both models were run through vLLM 0.29.0 on the same rows: the author's regression set rebuilt…
Open weights
apache-2.0
752M parameters
262,144 tokens
Opir-multitask-large is the English, highest-accuracy multi-task checkpoint in the Opir family: an encoder-based GLiClass guardrail model for real-time LLM safety filtering. It supports binary safe/unsafe classification, toxicity detection, jailbreak and prompt-injection detection, and zero-shot harmful-content categorization over a hierarchical safety taxonomy. This card is for knowledgator/opir-multitask-large. The model is used through GLiClass zero-shot classification: pass text plus the candidate labels you want scored. Use single-label mode for binary safe/unsafe decisions and multi-label mode for taxonomy, toxicity, jailbreak, or custom policy labels. Use multi-label mode when you…
Open weights
apache-2.0
439M parameters
gliclass
