Model overview
**GLQ (4.0 bits/weight)** quantization of `HuggingFaceTB/SmolLM3-3B` using a **trellis-coded codebook (QTIP TCQ) + randomized Hadamard transform (RHT) + LDLQ**. Weights stay compressed in memory and are decoded on the fly by a fused CUDA kernel. > **Where GLQ shines:** the size-vs-accuracy win is strongest at **2–4 bits/weight**. For even more headroom, combine it with the **E8 KV cache** (≈4× smaller KV cache → longer context in the same VRAM). > **Requires `glq >= 0.7.0`.** This checkpoint stores the trellis in the kernel > (MMA-fragment) layout that the lookup-free 3INST CUDA kernels consume > (`trellis_layout: "kernel"` in `config.json`); older glq versions cannot decode it.
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。