技术规格

系统要求

不同量化方法和上下文大小的显存要求

Mixtral-8x7B-v0.1

Mixtral-8x7B-v0.1 is a generative large language model developed by Mistral AI, distinguished by its Sparse Mixture of Experts (SMoE) architecture. This design enables the model to process information efficiently by conditionally activating a subset of its parameters for each input. Its primary purpose is to facilitate advanced text generation and comprehensive language understanding across a diverse range of applications.

The model is built upon a decoder-only transformer architecture. It integrates a Mixture-of-Experts layer where each layer comprises eight distinct feedforward blocks, known as 'experts'. A router network dynamically selects two of these experts to process each token, subsequently combining their outputs additively. This mechanism permits the model to leverage a substantial total parameter count of 46.7 billion while maintaining a significantly lower active parameter count of 12.9 billion per token during inference, thereby optimizing the balance between model capacity and computational efficiency. The architecture further incorporates Grouped Query Attention (GQA) and supports Flash Attention for enhanced performance.

Mixtral-8x7B-v0.1 supports a context length of 32,000 tokens, allowing it to process and generate responses based on extensive textual inputs. The model demonstrates proficiency in multilingual tasks, supporting English, French, Italian, German, and Spanish. It also exhibits strong performance in code generation tasks. The model can be fine-tuned for instruction-following tasks, making it a suitable foundation for building interactive applications that require precise adherence to user commands.

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