Model overview
**Model Summary** Granite-SWASH-2B is a 2B parameter decoder-only dense language model with **per-layer Sliding Window Attention (SWA)** and **learnable attention sinks** (LSE-based). Trained on a mix of open source and proprietary data, it is a broad, general-purpose, English-language base model that serves as an early exploration and small-scale preview for upcoming Granite series model releases. Granite-SWASH-2B is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, shared input/output embeddings, Sliding Window Attention, and Attention Sinks. **Attention Pattern:** 7 full-attention layers (indices 0, 3, 7, 11, 15, 19, 23) and 17 sliding-window layers (window=128). All layers have learnable per-head attention sinks.
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。