Bosun — Hanno Labs 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
Hanno Labs 的小型决策模型：基于 Qwen3-0.6B 与 Qwen3-1.7B 的 rank-16 LoRA，带 256 个学习得到的决策 token，单个问题最多 255 个候选项。由兼容 Jev 的服务器通过 /v1/systemone 提供服务。
变体
Bosun 最小的变体在 Q4_K_M 下约需 1 GB 显存——量化权重加框架开销，不含 KV 缓存。
| 模型 | Q4 下显存 | 显存 | 上下文 | 运行 |
|---|---|---|---|---|
| Bosun v3.1 0.6B → 0.6B | ~1.2 GB | Not published | jev-compatible-server :8000 | |
| Bosun v3.1 1.7B → 1.7B | ~2 GB | Not published | jev-compatible-server :8000 |
显存为 Q4_K_M 下的量化权重加开销，与 GPU 与显存检测器使用同一引擎计算。
如何在本地运行 Bosun
安装 Ollama，然后拉取标签。
Served by jev-compatible-server (default port 8000). This model does not run in Ollama.
在上方选择一个尺寸，查看它自己的显存、速度估算和安装命令。
许可证
Commercial use permitted. No usage restrictions beyond attribution.
适用于: Bosun v3.1 0.6B, Bosun v3.1 1.7B推荐 GPU
目录中能在本地运行 Bosun（至少 1 GB 显存）的最便宜 GPU 是 Intel Arc B570 (10 GB).
Bosun — 常见问题
How much VRAM does Bosun need?
Bosun needs about 1 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Bosun v3.1 0.6B (1 GB, Q4_K_M); Bosun v3.1 1.7B (2 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Bosun on an RTX 4090 (24 GB)?
Yes — Bosun runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for Bosun?
Q4_K_M is the best balance of quality and VRAM for Bosun in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run Bosun locally?
Bosun is a decision model: it is called over an HTTP endpoint (/v1/systemone), not chatted with. Where a variant is available in Ollama 0.35 or newer, pull it with `ollama pull` and send requests to the local server; the others ship their own server. Each variant page shows the exact commands for that model.