Open-Weight Models

Describe your model here

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…

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…

Model · Text classification

decider-4b-tr-judge

Hayri Yigit

English summary. A third LoRA round (rank 64, merged into the bf16 weights) on hayriyigit/decider-4b-tr-rag for a RAG relevance judge: does this retrieved document carry information that contributes directly to the answer, for the asked entity, institution, facet and period? Unlike the -rag model, partial evidence counts as relevant. The input is the production format: state {question, document, today} (in that order), the document rendered as [date] title + body (4000 characters), one fixed instruction. On held-out partial-evidence rows the "yes" rate goes from 0,028 to 0,952 (HotpotQA-tr) and 0,004 to 0,936 (synthetic); the cost is 2-8 points on some negatives (facet 1,000 → 0,920). Most…

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-4b-gguf

Eval Engine

GGUF builds of Decision-4B, an open-weight Jev-like decision model from Eval Engine, the AI arm of Chromia. Give it a state, a question, and a list of options. It answers with one letter. Runs in llama.cpp, Ollama, and on your phone. The Q4KM file is the one that powers Decision-4B in the Unbound app. All three are the LoRA merged into Qwen3.5-4B. The BF16 adapter scores 87.3% on the same panel. Use Q4KM for phones and laptops, Q80 or F16 when you have the memory. Full table and details on the adapter card. The reply is a single letter. toplogprobs gives the score for each option letter; softmax over the listed letters gives a probability per option. Input is a JSON object with state…

Model · Text classification

decision-model-rl-overcooked

Rodney Lafuente-Mercado

Get the code and instructions, or read the write-up and watch the video. Two independently acting chefs share this policy and serve six soups in 512 ticks in PufferLib's Cramped Room. Each sees its public observation expressed in text, game rules, and eight recent action outcomes. The policy scores stay, up, down, left, right, and interact. Each selection executes one native action, without a planner, pathfinder, action macro, or assigned role. This repository contains LoRA adapters and the trained original NLI classifier head, not a standalone base model. The root is cooperative checkpoint 220. single-chef/ contains checkpoint 330, which initialized cooperative training. Both use OpenJev…