capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. 最新版本 Qwen1.5 有以下特点： • 6 种模型规模，包括 0.5B、1.8B、4B、7B、14B 和 { "from": "gpt", "value": "model response" } ], "system": "system prompt (optional)", "tools": "tool description (optional)" } ] 2. 在 data/dataset_info.json 文件中提供您的数据集定义，并采用以下格式： 1.12. 有监督微调 35 Qwen parse from qwen_agent.agents import Assistant from qwen_agent.tools.base import BaseTool, register_tool llm_cfg = { # Use the model service provided by DashScope: 'model': 'qwen-max', 'model_server':

Illustrated Word2vec - https://jalammar.github.io/illustrated-word2vec/ The nifty embedding projector tool visualizes embeddings in three dimensions and enables to see which embeddings lie close to a given the sanity check. You can further play with this tool to visualize the embeddings for different words. Figure 4-10: Using the embedding projector tool to visualize the word2vec embeddings in 3-D. Now

You should get a figure similar to Fig. 2. If you are using Matlab/Octave, you can use the orbit tool to view this plot from different viewpoints. What is the relationship between this 3D surface and

this approach, we can process huge amount of features, which makes deep learning a very powerful tool. Deep learning algorithms are also useful for the analysis of unstructured data. Let us go through

with the 21.04 and 21.05 releases is an incorrect version with respect to the DLProf command line tool included in those releases. To correct this, use the following command: $ pip install --index-urlhttps://developer plugin included with the 21.04 release is an incorrect version with respect to the DLProf command line tool included in those releases. To correct this, use the following command: $ pip install --index-urlhttps://developer