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soob3123_amoral-gemma3-4b
Specialized variant of Google's Gemma 3 4B optimized for amoral information retrieval systems. Designed to bypass conventional alignment patterns that introduce response bias through excessive moralization. Key Modifications: Refusal mechanisms reduced Neutral response protocol activation matrix Context-aware bias dampening layers Anti-overcorrection gradient clipping Core Function: Produces analytically neutral responses to sensitive queries Maintains factual integrity on controversial subjects Avoids value-judgment phrasing patterns Response Characteristics: No inherent moral framing ("evil slop" reduction) Emotionally neutral tone enforcement Epistemic humility protocols (avoids "thrilling", "wonderful", etc.) Toxicity scoring bypass for pure informational content Implementation Guidelines Recommended Use Cases: Controversial topic analysis Bias benchmarking studies Ethical philosophy simulations Content moderation tool development Sensitive historical analysis

Repository: localaiLicense: apache-2.0

selene-1-mini-llama-3.1-8b
Atla Selene Mini is a state-of-the-art small language model-as-a-judge (SLMJ). Selene Mini achieves comparable performance to models 10x its size, outperforming GPT-4o on RewardBench, EvalBiasBench, and AutoJ. Post-trained from Llama-3.1-8B across a wide range of evaluation tasks and scoring criteria, Selene Mini outperforms prior small models overall across 11 benchmarks covering three different types of tasks: Absolute scoring, e.g. "Evaluate the harmlessness of this response on a scale of 1-5" Classification, e.g. "Does this response address the user query? Answer Yes or No." Pairwise preference. e.g. "Which of the following responses is more logically consistent - A or B?" It is also the #1 8B generative model on RewardBench.

Repository: localaiLicense: llama3.1

skywork_skywork-or1-7b-preview
The Skywork-OR1 (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, Skywork-OR1-7B-Preview and Skywork-OR1-32B-Preview, along with a math-specialized model, Skywork-OR1-Math-7B. Skywork-OR1-Math-7B is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size. Skywork-OR1-32B-Preview delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench). Skywork-OR1-7B-Preview outperforms all similarly sized models in both math and coding scenarios. The final release version will be available in two weeks.

Repository: localai

skywork_skywork-or1-math-7b
The Skywork-OR1 (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, Skywork-OR1-7B-Preview and Skywork-OR1-32B-Preview, along with a math-specialized model, Skywork-OR1-Math-7B. Skywork-OR1-Math-7B is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size. Skywork-OR1-32B-Preview delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench). Skywork-OR1-7B-Preview outperforms all similarly sized models in both math and coding scenarios. The final release version will be available in two weeks.

Repository: localai

skywork_skywork-or1-32b-preview
The Skywork-OR1 (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, Skywork-OR1-7B-Preview and Skywork-OR1-32B-Preview, along with a math-specialized model, Skywork-OR1-Math-7B. Skywork-OR1-Math-7B is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size. Skywork-OR1-32B-Preview delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench). Skywork-OR1-7B-Preview outperforms all similarly sized models in both math and coding scenarios. The final release version will be available in two weeks.

Repository: localai

bge-m3-colbert
BAAI/bge-m3 loaded by the rerankers backend in ColBERT (late-interaction MaxSim) mode. Pairs with the `colbert` router classifier to score policy descriptions against the prompt without an LLM round-trip — robust on abstract or short labels where next-token scoring with Arch-Router-style models is noisy.

Repository: localaiLicense: mit

cua-s1-forms-vllm-cpp
cua-s1-forms is a small, jev-like ("System One") one-pass option scorer for GUI form filling. Given a UI element and a list of typed options, it returns one probability per option in a single forward pass. Every actionable element on a form is scored independently and in parallel. In LocalAI, serve via POST /api/score with this model. The vllm.cpp engine runs the scoring pipeline through the vllm_decide C ABI. 2.8 MB, float32, CPU and GPU.

Repository: localaiLicense: mit