generated+clock+rgmii_clk+with+source+pin+clk_out+dose+not+have+a+valid+master+clock+or+valid+wavefo
时间: 2024-03-01 21:48:12 浏览: 16
generated+clock+rgmii_clk+with+source++clk_out+dose+not+have+a+valid+master+clock+or+valid+wavefo 是一个错误信息,意思是生成的时钟 rgmii_clk 的源引脚 clk_out 没有有效的主时钟或有效的波形。这个错误通常发生在时钟生成电路中,可能是由于时钟源引脚没有正确连接或者时钟源信号不稳定导致的。要解决这个问题,你可以检查时钟源引脚的连接是否正确,确保主时钟或波形是有效的,并且时钟信号稳定。如果问题仍然存在,可能需要进一步检查时钟生成电路的设计和配置。
相关问题
you+are+running+qmake+on+a+generated+.pro+file
To run qmake on a generated .pro file, you can follow these steps:
1. Open the terminal or command prompt.
2. Navigate to the directory where the .pro file is located using the `cd` command.
3. Run the `qmake` command followed by the name of the .pro file:
```
qmake yourproject.pro
```
4. Qmake will generate a Makefile based on the instructions provided in the .pro file.
5. After qmake successfully generates the Makefile, you can use a build tool like `make` to compile and build your project.
Remember to have Qt installed and added to your system's PATH environment variable for qmake to work properly.
prompt+rag
根据提供的引用内容,可以得知prompt+RAG的流程如下:
1. 首先,使用Retriever部分在知识库中检索出top-k个匹配的文档zi。
2. 然后,将query和k个文档拼接起来作为QA的prompt,送入seq2seq模型。
3. seq2seq模型生成回复y。
4. 如果需要进行Re-rank,可以使用LLM来rerank,给LLM写好prompt即可。
下面是一个简单的示例代码,演示如何使用prompt+RAG:
```python
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
# 初始化tokenizer、retriever和seq2seq模型
tokenizer = RagTokenizer.from_pretrained('facebook/rag-token-base')
retriever = RagRetriever.from_pretrained('facebook/rag-token-base', index_name='exact', use_dummy_dataset=True)
model = RagSequenceForGeneration.from_pretrained('facebook/rag-token-base')
# 设置query和context
query = "What is the capital of France?"
context = "France is a country located in Western Europe. Paris, the capital city of France, is known for its romantic ambiance and iconic landmarks such as the Eiffel Tower."
# 使用Retriever部分检索top-k个匹配的文档
retrieved_docs = retriever(query)
# 将query和k个文档拼接起来作为QA的prompt
input_dict = tokenizer.prepare_seq2seq_batch(query, retrieved_docs[:2], return_tensors='pt')
generated = model.generate(input_ids=input_dict['input_ids'], attention_mask=input_dict['attention_mask'])
# 输出生成的回复
generated_text = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
print(generated_text)
```