Model overview
本项目基于Mistral发布的模型Mixtral-8x7B进行了中文扩词表增量预训练，希望进一步促进中文自然语言处理社区对MoE模型的研究。我们扩充后的词表显著提高了模型对中文的编解码效率，并通过大规模开源语料对扩词表模型进行增量预训练，使模型具备了强大的中文生成和理解能力。 Chinese-Mixtral-8x7B支持完整的Mixtral-8x7B模型生态，包括使用`vLLM`、`Flash Attention 2`进行加速，使用`bitsandbytes`进行模型量化等。以下是使用Chinese-Mixtral-8x7B进行推理的代码示例。 根据Mistral发布的技术报告，Mixtral-8x7B在推理时将激活13B参数。下表为Chinese-Mixtral-8x7B与其他13B规模的中文扩词表模型在各个评测数据集上的5-shot结果：
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。