AMOR-Mamba2-180M

Model · Text generation

CasualSwarms

Convergent Intelligence

SAGI is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory. The enhancements were integrated with the existing AGI system through: 1. Compatibility Layer: Ensuring new components work with existing AGI Core 2. Unified State Representation: Combining enhanced capabilities with existing state 3. Enhanced Continuous Learning: Upgrading the learning system with new capabilities 4. Performance Monitoring: Tracking improvements through validation systems - Successfully…

Ru-Small-Instruct — экспериментальная компактная русскоязычная языковая модель класса SLM (Small Language Model) с объемом параметров ~0.2B (~165M). Разработана с упором на суверенность весов (Zero-Fingerprint): модель обучена с нуля без заимствования базовых чекпоинтов у сторонних корпоративных сетей (Llama 3 от Meta, Qwen от Alibaba, Mistral). Модель предназначена для исследований локального инференса, работы на маломощном оборудовании, CPU и мобильных чипах, где критичны нулевая задержка (Time-To-First-Token) и полная независимость весов. Для компактной модели в 165M параметров, обученной на одном домашнем GPU за 48 часов, способность держать роль, грамотно формулировать сложные термины…

Model · Text generation

pythia-160m

EleutherAI

The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream…

Model · Text generation

cagliostro-v3

Bench Labs

A 146M parameter decoder-only language model pretrained from scratch on 75B tokens of open web, synthetic textbook and mathematics data. It is the third model in the cagliostro line and the first to clear an Index of 26 on the Open SLM Leaderboard metric. Zero-shot, measured with lm-evaluation-harness and the leaderboard's own ArithMark-3 script, on the exported float32 weights in this repository. The Index is the leaderboard's own formula, (N(HellaSwag,25) + N(CombinedARC,25) + N(PIQA,50) + 0.65N(ArithMark,25)) / 3.65 where N(v,c) = 100(v-c)/(100-c) and CombinedARC is the mean of ARC-Easy and ARC-Challenge. For context against other models at this scale, using the leaderboard's published…

English | 中文 A 143M-parameter bidirectional Chinese↔English translation model, instruction-tuned on the Haidass1.5-143M base — the strongest zh⇄en translator at this scale among general chat-architecture models. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-" marks directions a model does not serve. The same models re-evaluated on FLORES+ devtest (released 2026; zero overlap with dev): devtest sentences do not overlap with dev.…