2025-05-16 07:56:58 - 开始模型推理
✅ Using device: cuda
🔄 正在加载分词器: /workspace/models/LLM-Research/Meta-Llama-3-8B-Instruct
✅ 分词器加载完成，耗时: 0.57秒
🔄 正在加载模型: /workspace/models/LLM-Research/Meta-Llama-3-8B-Instruct

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Some parameters are on the meta device because they were offloaded to the cpu.
✅ 模型加载完成，耗时: 117.94秒
📝 输入长度: 12 tokens
🤖 正在生成回复... (输入: 12 tokens, 最大生成长度: 140 tokens)
/root/.pyenv/versions/3.11.1/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:679: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.
  warnings.warn(
The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.

🧠 模型输出 (128 tokens):
by @AI_Researcher
---

As an AI researcher, I'm excited to share my insights on the future development directions of artificial intelligence. Here are some trends and areas that I believe will shape the future of AI:

1. **Explainability and Transparency**: As AI becomes more pervasive in our daily lives, there is a growing need for AI systems to be explainable and transparent. This includes understanding how AI models make decisions, identifying biases, and ensuring accountability.
2. **Edge AI**: With the proliferation of IoT devices and edge computing, AI will need to be deployed at the edge to process data in real-time,
✅ 生成完成，耗时: 106.08秒
2025-05-16 08:00:48 - 推理完成，耗时: 230 秒