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

Falcon3-7B

Falcon 3-7B is a state-of-the-art instruction-tuned language model developed by the Technology Innovation Institute (TII). This model variant is a component of the Falcon 3 family, which focuses on enhancing capabilities in scientific domains, mathematics, and code generation. It is engineered for efficiency and scalability, enabling deployment on a range of infrastructures, including those with limited computational resources. The model supports multilingual applications, with training encompassing English, French, Spanish, and Portuguese, and is designed to handle long-context tasks.

The architectural foundation of Falcon 3-7B is a transformer-based causal decoder-only design, incorporating 28 decoder blocks. It utilizes Grouped Query Attention (GQA) to optimize inference speed and memory efficiency, configured with 12 query heads and 4 key-value heads, and a head dimension of 256. The model integrates Rotary Positional Embedding (RoPE) with a high value of 1000042 to facilitate effective understanding and processing of extended contexts up to 32,000 tokens. Activation functions are implemented using SwiGLU, complemented by RMSNorm for normalization, contributing to training stability and efficiency. It is also optimized to utilize FlashAttention-3.

Falcon 3-7B was pretrained on a dataset comprising 14 teratokens of diverse web, code, scientific, and high-quality multilingual data. Following pretraining, it underwent further fine-tuning on 1.2 million samples, specifically tailored for STEM content, conversational interactions, code, and safety compliance. This comprehensive training regimen positions the model for robust performance across various applications, including scientific and mathematical problem-solving, multilingual content generation, and processing long-form textual information. Its design supports instruction-following tasks, making it suitable for educational tools, research assistance, and the generation of technical documentation.

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