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Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own

  • decision-model
  • jev
  • qwen3

Run 35B MoE models in ~2.5 GB of RAM. Weights stream from SSD; a trained router predicts loads a step ahead. Mac, iPhone, Android, Windows supported

《深入理解 AI Infra:量化分析与系统设计》(李博杰 著)开源书稿:从硬件约束和模型架构出发,量化推导 LLM 推理与训练系统设计。含全书正文、PDF、配套计算工具与实验

  • accelerator
  • ai-infra
  • ai-infrastructure
  • book

An open-source document parsing model with SOTA accuracy and speed, using an RSI loop during post-training.

Infrastructure for the next generation of voice agents, designed to provide universal memory. It is divided into a left brain and a right brain, storing information and emotions respectively, while a fully streaming architecture eliminates latency at the fundamental level.

Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.

腾讯 CodeBuddy 账号池管理控制台,内置 OpenAI 兼容反代网关:扫码批量纳管账号、定时签到与保活、密钥分组分发、IP 与模型白名单、调用日志与用量统计;支持带签名校验的一键更新。

Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results

  • interpretability
  • mechanistic-interpretability
  • qwen
  • steering

Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.

  • agents
  • calibration
  • classification
  • decision-making

1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy

Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)

  • calibration
  • decision-model
  • jev
  • jev-model

A free AI assistant and survival library that runs on your own computer, with no internet needed. Shows the source of every answer.

Trace-native CI/CD for AI agents: grade production traces, cluster failures, freeze them into replayable regression tests, and block PRs that regress. Self-hostable.

GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard | Fable 5 Level Coding for a Fifth the Price - or on a Single GPU

A clinical decision-evidence benchmark: pull the one fact an answer rests on, and see whether the assistant's action moves with the evidence. Built on HealthBench.

EdgeRock: Strong Reasoning at Constant Memory. Multimodal models with O(N) generation and O(1) persistent history. Inference and evaluation code.

把 Muse(muse.ai) 逆向封装为 OpenAI 兼容接口,支持对话、文生图、文生视频/图生视频、多账号池轮转与 48h 自动续期。OpenAI-compatible API for Muse.ai with Chat, Image & Video generation.

FactCircuit: an auditable claim-and-evidence verification loop with immutable provenance, staged checks, bounded retrieval, and reproducible evaluation.

  • accuracy-decline
  • ai-safety
  • audit-trail
  • fact-checking