Model overview
GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B — the ThinkingCap finetune of Qwen3.8-27B that cuts reasoning tokens by 37% on average while holding 85.8% average accuracy against the base model's 86.6%. ➡️ Full model description, evaluation results (multi-seed, statistically tested), the measured thinking-token reduction, recommended sampling params, and citation: see the main model card at bottlecapai/ThinkingCap-Qwen3.8-27B. The low-bit files are built with an **importance matrix** (activation statistics from a chat-templated calibration corpus) and a **per-tensor precision layout**: the attention projections of the full-attention layers and the linear-attention output projections stay at 6–8 bit while the feed-forward weights take the 4-bit hit.
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。