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

Falcon3-3B

The Falcon3-3B model is part of the Falcon 3 family of open foundation models developed by the Technology Innovation Institute (TII). This model is designed for a balance of performance and efficiency, enabling its deployment on a range of computing infrastructures, including smaller devices. It is developed to support advancements in capabilities related to science, mathematics, and code generation. The Falcon 3 series includes both base models for general-purpose generative tasks and instruct models for conversational applications, emphasizing accessibility in advanced artificial intelligence systems.

Architecturally, Falcon3-3B employs a transformer-based causal decoder-only design. It incorporates 22 decoder blocks, contributing to its processing depth. For attention mechanisms, the model utilizes Grouped Query Attention (GQA) with 12 query heads and 4 key-value heads, along with a wider head dimension of 256. This configuration supports efficient inference operations. The model integrates SwiGLU as its activation function and RMSNorm for normalization, in addition to using Rotary Position Embeddings (RoPE) with a high value to handle extended context. It also leverages Flash Attention 2 for optimized memory and speed during operations.

The Falcon3-3B model, particularly its instruct variant, supports a context length of up to 32,768 tokens, while the base version supports 8,192 tokens. It is engineered to perform on tasks such as reasoning, language understanding, instruction following, and mathematical problem-solving. The model has been trained to support four languages: English, French, Spanish, and Portuguese. Its design considerations include the availability of quantized versions, such as int4, int8, and 1.58 Bitnet, which further enhance its efficiency and suitability for resource-constrained environments.

没有可用的 Falcon3-3B 评估基准。