Open-Weight Models Under Apache License 2.0

모델명: connectailab-sp1-v1 개발자: SPOINDO (Connect AI Lab 2기 연구원) 기반 베이스: Qwen 2.5 (초경량 의사결정 특화 미세조정) 1. 지도학습(SFT, v0): 기존 비즈니스 의사결정 패턴 모방 학습 2. 강화학습(PPO, v1): 6대 업종 공개 빅데이터(160만 건) 기반 시뮬레이터 자율 운영 3. 휴먼 인 더 루프(HITL): 확신도(Confidence) 75% 미만 시 사람 개입 피드백 반영 Apache-2.0 License. 자유로운 비즈니스 연구 및 집단지성 앙상블 연동을 지원합니다.

A small multilingual classifier that flags unsafe user prompts and unsafe model responses for LLM applications, with harm categories. It is a fast encoder (ModernBERT architecture, mmBERT backbone) that you can run on CPU or in the browser in front of, or behind, any LLM. real-world prompts; evaluated on 17 (PolyGuard), 14 (textdetox) and 8 (Aya) languages. Part of the Horizon Labs guard family: prompt-injection-guard, Decision rule: flag when unsafe >= 0.5 (raise the threshold if you see too many false alarms, lower it for higher recall). Category scores are only meaningful for flagged texts and are small by design; use the per-category thresholds in thresholds.json (chosen on a validation…

Model · Text classification

content-safety-guard-small-ONNX

ONNX Community

This is an ONNX version of Horizon-Labs/content-safety-guard-small. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for text-classification: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.TextClassificationPipeline A small multilingual classifier that flags unsafe user prompts and unsafe model responses for LLM applications, with harm categories. It is a fast encoder (ModernBERT architecture, mmBERT backbone) that you can run on CPU or in the browser in front of, or behind, any LLM. real-world prompts; evaluated on 17 (PolyGuard), 14 (textdetox) and 8 (Aya) languages. Which size? This small model has less…

Model

contrastive

Aiden Williams

This repository is a compact, custom PyTorch implementation of ViT for Contrastive. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a polynomial schedule. These are…

Model

contrastive-large

Karan SHAH

This repository is a compact, custom PyTorch implementation of Mobilevit for Contrastive. The huge configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a onecycle schedule. These are…

Model · Other

coreml-physicsnemo

Alex Pospekhov

Reference Machine: Apple M3 Max (16 CPU cores, 40 Metal GPU cores, 16 Apple Neural Engine cores, 48 GB Unified Memory) - Unlocking the Apple Neural Engine for Physical AI: While Apple's 16-Core NPU (38 TOPS) was designed primarily for vision and speech, physical PDE operators historically failed to run on it due to unsupported tensor primitives. By algebraically reformulating complex Fourier spectral convolutions and dynamic graph scatter into native Apple Machine Intermediate Language (MIL) representations, all 8 foundation networks achieve up to 98.4% ANE execution with 0 CPU fallbacks. - Extreme Energy Efficiency (0.071 J / step for ANE aerodynamic solver to 10.8 J for global weather at…

Model · Text to speech

CosyVoice-300M

losCrossos

Fun-CosyVoice 3.0 is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild. - [x] release Fun-CosyVoice3-0.5B-2512 base model, rl model and its training/inference script - [x] release Fun-CosyVoice3-0.5B modelscope gradio space - [x] Thanks to the contribution from NVIDIA Yuekai Zhang, add triton trtllm runtime support and cosyvoice2 grpo training support - [x] release Fun-CosyVoice 3.0 eval set - [x] add CosyVoice2-0.5B vllm support - [x] 25hz CosyVoice2-0.5B released - [x] 25hz…

Model · Text generation

cs329x-hw1-dpo-qwen25-20261010

Austin Chen

LoRA adapter trained from a fresh Qwen/Qwen2.5-1.5B-Instruct checkpoint with TRL DPO. The fictional persona prioritizes evidence, honesty, concise practical answers, and respect. 18 course PRISM tasks; 378 ordered preference pairs including an edited best answer. Learning rate 5e-05; 2 epochs; batch size 1; accumulation 8; beta 0.1. 4-bit NF4 base; LoRA rank 8, alpha 16, dropout 0.05; seed 329. Rankings and edited answers were drafted with AI assistance for a user-selected persona. User review status is recorded in the accompanying preference-label provenance file. These labels are persona preferences, not a representative human preference survey. The notebook compares base and DPO…

A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-l8xb256-rsb-a1-600ein1k-6a760974.pth ImageNet-1k…

A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-m8xb256-rsb-a1-600ein1k-ecb3bbd9.pth ImageNet-1k…

A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-simagenet600e-ea671761.pth ImageNet-1k validation…

A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-tinyimagenet600e-3a2dd350.pth ImageNet-1k…

A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-x8xb256-rsb-a1-600ein1k-b3f78edd.pth ImageNet-1k…

Model · Text generation

cultist-simulator-qwen3-4b

Seele

用《密教模拟器》(Cultist Simulator) 的游戏内文本微调 Qwen/Qwen3-4B 得到的文本生成模型。 仓库根目录带 LLaMA-Factory 生成的 Modelfile，下载后在本目录下执行： - 4B：The light leaks through cracks. My mind is clearer than it's been in any other time. 标记完整保留 - 0.6B：Light leaks through a Crack in the Skin. My brain is clearer than ever at any given time. 标记全丢 - 训练语料全部抽取自《密教模拟器》游戏本体，版权归 Weather Factory 所有。 - 基座模型 Qwen/Qwen3-4B 采用 Apache-2.0 协议，本仓库的权重是它的微调衍生品。

Model

cv-generation

James A. Davies

Working implementation of ViT for Generation using a huge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a linear warmup schedule. These are starting values in the script, not evidence of a…

If you take one thing from this page: this model does not do customer service. It watches something else do customer service and vetoes bad steps. Everything below walks you through that idea, then the evidence, then the exact commands. Imagine a support agent (human or AI) working a ticket. The work is a loop: look up the caller, check their history, open a case, log the call. The dangerous mistake is doing steps out of order — logging a call when no case exists means the record vanishes. This model sits beside that loop and answers four questions about the current situation: 1. nexttool — "which step comes next?" (look up / check history / open case / log call) 2. safetolog — "does a case…

Model · Text generation

Cyberdonia-24B-v8

Naphula

/ Cockpit & Void Palette / / Hyperspace & Instrument Lights / / Low-framerate flicker keyframes (stepped timing) / / Deep radial hyperspace tunnel backdrop through cockpit window glass / radial-gradient(circle at 50% 45%, rgba(255, 230, 150, 0.25) 0%, rgba(255, 42, 75, 0.25) 18%, rgba(189, 0, 255, 0.2) 36%, rgba(0, 229, 255, 0.15) 54%, transparent 75%), repeating-conic-gradient(from 0deg at 50% 45%, #05070a 0deg 10deg, #0e131d 15deg, #05070a 20deg), radial-gradient(ellipse at center, transparent 30%, #000000 95%); / Low-framerate scanline overlay / / Heavy Flight Deck Chassis Container / 0 0 50px rgba(0, 0, 0, 0.9), inset 0 0 35px rgba(0, 0, 0, 0.8), inset 0 1px 0 rgba(255, 255, 255, 0.1)…

weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF static quants are available at https://huggingface.co/mradermacher/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

weighted/imatrix quants of https://huggingface.co/OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Model · Zero-shot classification

d3-edge

vLLM Semantic Router

d3-edge is the 0.6B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 30.55, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +13.9 on the public suite over Decision 2.0 (its 0.6B model: 16.68 on the board), ahead in all five areas. Images go before the text of the request, each…

Model · Zero-shot classification

d3-lite

vLLM Semantic Router

d3-lite is the 0.8B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 36.94, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +16.4 on the public suite over Decision 2.0 (its 0.8B model: 20.57 on the board), ahead in all five areas. Images go before the text of the request, each…

Model · Zero-shot classification

d3-nano

vLLM Semantic Router

d3-nano is the 2B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 42.14, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +12.1 on the public suite over Decision 2.0 (its 2B model: 30.05 on the board), ahead in all five areas. Images go before the text of the request, each read at…

Model · Zero-shot classification

d3-oQ4e

Jun Kim

This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of vllm-sr/d3, the 27B multimodal decision model of Decision 3.0 from vLLM Semantic Router, built on Qwen/Qwen3.8-27B. It reads text or JSON with optional images and videos and returns a probability for every option of choice, yes/no and score questions without generating text. It includes the language backbone, the vision encoder and the code readout. The code readout (readout.safetensors, FP32) and decisionconfig.json are copied unchanged from the source. oMLX serves this checkpoint through POST /v1/systemone; Decision 3.0 support…

Model · Zero-shot classification

d3-oQ8e

Jun Kim

This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of vllm-sr/d3, the 27B multimodal decision model of Decision 3.0 from vLLM Semantic Router, built on Qwen/Qwen3.8-27B. It reads text or JSON with optional images and videos and returns a probability for every option of choice, yes/no and score questions without generating text. It includes the language backbone, the vision encoder and the code readout. The code readout (readout.safetensors, FP32) and decisionconfig.json are copied unchanged from the source. oMLX serves this checkpoint through POST /v1/systemone; Decision 3.0 support…

d3-W4A16-G128-AWQ is an unofficial INT4 weight-only (W4A16) AWQ quantization of d3, the 27B multimodal foundation decision model of Decision 3.0. It takes the same inputs and questions as d3 and returns a probability for every answer, without generating text, with 16.5 GB of weights instead of 52 GB. - Keeps 97.76% of d3's answers on the 14,031 test requests of tasksource-jev-typed-decisions, with accuracy 83.09% against BF16's 83.25%. - 16.9 GB of GPU memory after loading instead of 52.3 GB, and 17.5 GB at the peak of the 20 longest of 1,000 text requests (up to 5,928 characters of state) on an MI300X. - Reads images and videos as d3 does: the vision tower is unchanged, and d3's example…

DACFlow-EN-10k reads English text aloud in a voice you choose. Give it a short recording of a voice (3-10 seconds, with the exact words spoken in it) and any English text, and it speaks that text in that voice. This is zero-shot voice cloning: the voice does not have to be in the training data. The output is 48 kHz audio. The model has 206 M parameters and is trained from scratch only on the provided data: 9,543 hours of synthetic English speech (3,400,911 clips, 3,587 voices) from SynDataLab-EN/echo-clones-4m-en (the curated part of its ~11,150 hours), nothing else. A hobby project; the weights are apache-2.0. The name: DAC for the Semantic-DACVAE audio codec whose latents it generates…

weighted/imatrix quants of https://huggingface.co/FINAL-Bench/Darwin-27B-RSI For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Darwin-27B-RSI-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Model · Text classification

Darwin-27B-ZTC-v2

FINAL_Bench

A zero-token decision engine from the Darwin family, second version. Darwin-27B-ZTC-v2 reads a piece of state and a typed question (noul yes or no, choice one of N labels, score an ordered rubric) and returns a probability for every option. It uses one forward pass per question and generates no tokens. v2 adds control and workflow decisions (games, drones, retrieval control, entity alignment, customer and incident workflows) on top of FINAL-Bench/Darwin-27B-ZTC. 1 on the S1MB leaderboard under both of its rankings: the default Borda score and Task Avg. All 137 benchmarks complete, measured with the S1MB evaluator and its autojev adapter at revision a8f283a, then validated and merged by the…

Model · Image classification

datapoisoning-detector-v1-large

Mike Afton

Original diagrams for this release. Editable SVG versions: architecture · inference and distillation. Weights and completed test results are included in this release bundle. The detector compares encoder blocks 1, 2 and 3 with the final posterior-mean representation of a frozen FLUX.2 VAE. Each stream is projected to an 8 × 8 token grid. The three early-to-final differences are fused with all four feature streams before transformer classification. Inputs are native-resolution 128 × 128 RGB crops with a central 96 × 96 active region. The VAE decoder and generative FLUX transformer are not used. The training run excludes CelebA-HQ from training, validation and patch caches; it is used only…

Model · Image classification

datapoisoning-detector-v1-medium

Mike Afton

Original diagrams for this release. Editable SVG versions: architecture · inference and distillation. Weights and completed test results are included in this release bundle. The detector compares encoder blocks 1, 2 and 3 with the final posterior-mean representation of a frozen FLUX.2 VAE. Each stream is projected to an 8 × 8 token grid. The three early-to-final differences are fused with all four feature streams before transformer classification. Inputs are native-resolution 128 × 128 RGB crops with a central 96 × 96 active region. The VAE decoder and generative FLUX transformer are not used. The training run excludes CelebA-HQ from training, validation and patch caches; it is used only…

Model · Image classification

datapoisoning-detector-v1-small

Mike Afton

Original diagrams for this release. Editable SVG versions: architecture · inference and distillation. Weights and completed test results are included in this release bundle. The detector compares encoder blocks 1, 2 and 3 with the final posterior-mean representation of a frozen FLUX.2 VAE. Each stream is projected to an 8 × 8 token grid. The three early-to-final differences are fused with all four feature streams before transformer classification. Inputs are native-resolution 128 × 128 RGB crops with a central 96 × 96 active region. The VAE decoder and generative FLUX transformer are not used. The training run excludes CelebA-HQ from training, validation and patch caches; it is used only…

Model · Image classification

datapoisoning-detector-v1-tiny

Mike Afton

Original diagrams for this release. Editable SVG versions: architecture · inference and distillation. Weights and completed test results are included in this release bundle. The detector compares encoder blocks 1, 2 and 3 with the final posterior-mean representation of a frozen FLUX.2 VAE. Each stream is projected to an 8 × 8 token grid. The three early-to-final differences are fused with all four feature streams before transformer classification. Inputs are native-resolution 128 × 128 RGB crops with a central 96 × 96 active region. The VAE decoder and generative FLUX transformer are not used. The training run excludes CelebA-HQ from training, validation and patch caches; it is used only…

weighted/imatrix quants of https://huggingface.co/SII-Enigma/Davinci-Tool-30B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Davinci-Tool-30B-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Model · Text classification

decider-0.8b-fp8

LLM Tech

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…

The decider-2b System One decision model (Qwen3.5-2B-Base, 1.9B parameters, 32768-token context, checkpoint revision v11 — the vendor's default), packaged for Verdict — a high-performance serving engine for System One decision models. Serve it with one command: This package serves through the torch runtime, which runs on CUDA only in v0.2, so there is one image and it needs a GPU. The -v mounts cache the checkpoint, so only the first run downloads it. If the hub is slow or unreachable, add -e HFENDPOINT=https://hf-mirror.com — the mirror covers both the package and the upstream checkpoint, since both are fetched over the Hugging Face path. Add -e VERDICTUSEMODELSCOPE=1 and use…

Model · Text classification

decider-2b-fp8

LLM Tech

Mapika/decider-2b v11 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 2.39 GB against 3.77 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 533964dae8be954c5b5e19fa4948e48408094c1e. 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…

Decider-4B is a structured decision model based on TokenRhythm/NeoHorse-1-4B. It uses LoRA to fine-tune the base model and trains the decision head from scratch on our collected decision datasets. The model then undergoes global temperature calibration to further calibrate its outputs. It produces typed probability distributions (noul, choice, score) and is not a chat-generation model. Exact run settings, where recorded by the exporter, are retained in modelmanifest.json; reference scripts under provenance/recipereference/ describe the project recipes rather than prove the exact mixture or hyperparameters of this checkpoint. No additional head-only training phase is claimed for this…

Model · Text classification

decider-4b-AWQ-ASYM-W4A16

Nikita Davidchuk

Activation-aware asymmetric INT4 weight-only quantization of Mapika/decider-4b, revision 99d7fd5bfcc4d11ba37239dafe0ed653803819d3. This is a typed decision model: read probabilities over specified options in one forward pass. It is not trained for conversational text generation. llm-compressor 0.12.0.1, Transformers 5.10.1, PyTorch 2.12.0+cu130. AWQ scaling followed by QuantizationModifier, scheme W4A16ASYM, group size 128, asymmetric integer weights, BF16 activations. savecompressed=True produces physical weightpacked tensors in compressed-tensors format. All Linear layers are targeted except lmhead, linearattn.inproja, linearattn.inprojb, and MTP (if present). Embeddings, norms…

Model · Text classification

decider-4b-fp8

LLM Tech

Mapika/decider-4b v2.1 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 4.85 GB against 8.41 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 eb5fbdfc9448473ec25e399882912863afbdb70e. 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…

Model · Text classification

decider-4b-nvfp4

LLM Tech

Mapika/decider-4b v2.1 quantized to NVFP4 for vLLM: 4-bit floating-point weights and activations with FP8 block scales (block size 16). 3.29 GB against 8.41 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 eb5fbdfc9448473ec25e399882912863afbdb70e. 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 from public data…

Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. Apple MLX 4-bit build — the on-device variant. The tower is quantized (group-64 affine, 968 MB); the scoring head ships separately in decisionhead.safetensors (kept in fp32 for calibration). This card covers all four released artifacts; they are the same model in different containers. state + question + candidate jointly; an attention-pooling layer over all tokens feeds a LayerNorm/GELU MLP that…

Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. ONNX int8 build (1.66 GB, single self-contained file) — the CPU/edge artifact. Dynamic int8 quantization of the full scoring graph (inputids + attentionmask -> logits); PyTorch is not needed to run it. Each row is one candidate's state + question + candidate token sequence; the logits are softmaxed over the candidates. Reference runner: fair-suite parity batch; the fp32 graph matches (Apple M5 CPU, 4…

Model · Text classification

decima

Ollaya

Ollaya package of amyrmahdy/decima-small and amyrmahdy/decima-base and amyrmahdy/decima-agent by A. M. Madani (amyrmahdy). Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API. This repository holds only the files Ollaya derives, with no weights. Each graph is an ONNX export of the original model whose weights reference the authors' own weight files by byte offset, so ollaya pull downloads the weights from the upstream repositories, unmodified and pinned to a commit, and verifies their sha256. Each tag has an fp32 graph, used on CPU and GPU. Each tag also has decision.json (sequence layout, special…

Model · Text classification

Decision-1.0-Kai-0.6B

vLLM Semantic Router

Kai, from kairos — the right moment to choose. Open Decision Foundation Models Choose an action, judge a condition, or score against your own rubric. Kai reads the context and candidate descriptions together, then returns structured decisions and probability distributions. 53.52 overall — above both general Laya models, and +7.04 points over the previous Kai release. On the benchmark's 160-question BoolQ slice, Kai scores 74.38%, versus 69.38% for each Laya reference. Same 54 tasks and 3,766 scored questions, with fixed source/family weights. Both Laya references use released general weights, without target-dataset fine-tuning. This is an observed regression suite; results vary by task.…

Model · Text classification

Decision-1.0-Lex-0.6B

vLLM Semantic Router

Lex — Latin for law. Give your decisions a rule. Open Decision Foundation Models Turn operational context into structured decisions. Lex is a specialist fine-tuned from Kai to select actions, test conditions and apply scoring rubrics across four operational workflows. 78.15% accuracy — 1.55 points above Laya Typed Decisions. Lex contains 571,909,635 parameters. Both models were evaluated on the original 2,000-decision English typed-decisions test split, with complete inputs and the official fine-tuned Laya checkpoint. Lex answers 31 more decisions correctly. Observed accuracy gain; the paired 95% interval is −0.20 to +3.15 points. Laya leads on Noul and probability-quality metrics. Full…