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

Llama 3.1 8B

The Llama 3.1 8B model is a component of the Meta Llama 3.1 series, a collection of large language models developed by Meta. This model variant, featuring 8 billion parameters, is engineered to serve a range of natural language understanding and generation tasks. Its design prioritizes efficiency and responsiveness, making it suitable for deployment in environments with computational constraints. The model is optimized for dialogue applications and is designed to adhere to complex instructions, supporting its utility in conversational agents and virtual assistant systems.

Architecturally, Llama 3.1 8B is built upon an optimized transformer framework, employing a dense network configuration. A notable innovation is the integration of Grouped-Query Attention (GQA), which enhances inference scalability. The internal mechanics of the model incorporate the SiLU (Swish) activation function and RMSNorm for effective normalization across its layers. Positional encodings are managed through Rotary Position Embedding (RoPE), and the architecture leverages Flash Attention to improve processing speed. The model's training involved a substantial dataset of approximately 15 trillion tokens from publicly available sources, augmented with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align its outputs with desired helpfulness and safety criteria. A significant enhancement in this iteration is the expanded context length, which now extends to 128,000 tokens.

Regarding its capabilities and applications, the Llama 3.1 8B model is proficient in tasks such as text summarization, text classification, and sentiment analysis, particularly in scenarios demanding low-latency inference. Its multilingual support extends to eight languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, facilitating its application in diverse linguistic contexts. The model also supports advanced workflows, including long-form text summarization, and can be utilized in processes such as synthetic data generation and model distillation to refine smaller language models.