Model overview
Swift 1.5 upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified reasoning efficiency kept Swift Open License Swift-1.5-Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. 144 tensors across all 48 layers swap in ready-made abliterated weights by byte-level transplant — every GSQ value and scale is left untouched, the file size moves by less than 0.2% , and the reasoning-efficiency of Swift 1.5 survives (still 34.8% below the base). 🧭 What this is The abliterated edition of UkisAI's Swift 1.5 — itself a reasoning-efficient derivative of Qwen3.8-Flash-Next that uses ~52% fewer thinking tokens on coding benchmarks. Every GSQ-learned quantized tensor is left bit-for-bit untouched; only the 144 tensors that write back into the residual stream are replaced, layer by layer, with the corresponding tensors from a ready-made abliterated release. Base ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF —— GSQ learned quantization + RCO budget allocation, plus UkisAI’s anti-overthinking post-training Abliterated weights orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF —— the 144 target tensors’ bytes come straight from here Method GGUF → GGUF byte-level tensor transplant . Only where the official type demands it (the 48 routed-expert ffn_down_exps stored as Q2_0) are the abliterated values re-encoded — using Swift’s own learned block scales , so no GSQ value is recomputed, only the code words change. ⚠️ Safety notice This model is abliterated — the refusal direction has been removed and it has no reliable built-in guardrails . It may comply with harmful, illegal or unsafe requests. Deploy it only where you can supply your own moderation, access control and legal review; do not put it in front of end users without your own safety layer. Also note the upstream Swift Open License v1.0 is stricter than Apache-2.0: commercial use is free only below US$1M annual revenue.
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。