Model overview
*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize. `reformer-enwik8` was pretrained on the first 90M chars of *enwik8* whereas the text was chunked into batches of size 65536 chars (=2^16). The model's weights were taken from https://console.cloud.google.com/storage/browser/trax-ml/reformer/enwik8 and converted to Hugging Face's PyTorch ReformerLM model `ReformerModelWithLMHead`. The model is a language model that operates on characters. Therefore, this model does not need a tokenizer. The following function can instead be used for **encoding** and **decoding**:
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：来源未披露，须进一步核验。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。