Model overview
> **~3 GB of RAM is all you need.** Laptop, old GPU, Raspberry-Pi-class boxes with swap — the Q4 build runs > anywhere. One command and you have a private, offline reasoning model on your machine: > > v2 is a full retrain: **13× more genuine Fable 5 trace data** (11,574 sessions, 16.8M tokens — corpus published) and a rebuilt recipe (completion-masked loss, replay mixing, benchmark-gated checkpoints, seed-averaged weights). Clean answers, structured reasoning, agent instincts — and the transcript artifacts that leaked into v1's replies are gone. *One trade, made on purpose: raw HumanEval-style function synthesis stays the base model's turf (81.7 vs 70.1) — v2 spends that capacity on agent behavior instead, and spends half as much as v1 did.* Measurement notes below. 👇
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。