Model overview
Post-hoc LoRA adapters extracted from the weight delta between krea/Krea-2-Turbo and krea/Krea-2-Raw. For each 2D weight matrix in the Krea-2 transformer, we compute `ΔW = W_turbo − W_raw` and factor it with truncated SVD (`torch.svd_lowrank`, q=256) into low-rank `lora_A` / `lora_B` pairs. The goal is to approximate turbo behavior on the **Raw** base model without swapping the full 24 GB checkpoint. **Experimental.** This is not an official Krea release. Distillation is not purely low-rank, and turbo inference also depends on scheduler settings (8 steps, CFG=0, mu≈1.15).
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：other。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。