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

Yi-34B

The Yi-34B model, developed by 01.AI, is a 34-billion parameter large language model trained from scratch on a 3-trillion token multilingual corpus. This foundational model demonstrates strong capabilities in language understanding, commonsense reasoning, and reading comprehension. It is specifically engineered to support both English and Chinese languages, offering robust bilingual proficiency across various tasks. The model's design focuses on achieving a balance between high performance and efficient inference, making it suitable for a range of computational environments.

Architecturally, Yi-34B is built upon a modified decoder-only Transformer framework, drawing inspiration from the LLaMA implementation without being a direct derivative. A key technical feature is the incorporation of Grouped-Query Attention (GQA), which contributes to reduced training and inference costs compared to traditional Multi-Head Attention while maintaining performance. The model utilizes the SwiGLU activation function and RMS Normalization layers. Positional encoding is handled through a Rotary Position Embedding (RoPE) mechanism. These architectural choices aim to optimize model stability, convergence, and compatibility within the AI ecosystem.

Yi-34B is applicable to tasks requiring extensive language processing, such as long-form document summarization, detailed legal and technical document analysis, and complex multilingual question-answering systems. It also excels in the generation of multilingual content and instruction following. The base model supports a context length of 4,096 tokens, with specialized variants like Yi-34B-200K extending this capacity to 200,000 tokens, enabling processing of exceptionally long text sequences. Its design considerations allow for deployment on various hardware configurations, including consumer-grade GPUs, especially when employing quantization techniques.

没有可用的 Yi-34B 评估基准。