Cost-effective : no API fees, free to launch
Posted: Mon Feb 10, 2025 9:22 am
Step 4: Run the model locally
Once all the settings are done, you are ready to run DeepSeek-R1 locally.
1. Prepare the input data : Tokenize the input data using a tokenizer.
inputs = tokenizer"Ваш входной текст здесь", return_tensors="pt"
2. Generate output : Pass the tokenized input to the model.
outputs = modelinputs
3. Post-processing : Decode the output to get human-readable text.
decoded_output = tokenizer.decodeoutputs[0], skip_special_tokens=True
Pros and Cons of DeepSeek-R1
Pros:
Privacy Concern : All data is stored locally peru mobile database for increased security.
Powerful Reasoning : Ideal for complex tasks that require thought and planning.
Cons:
Slower execution : Because reasoning models think through a problem step by step, answers may take longer than traditional LLMs.
Equipment Requirements : Larger models require more powerful equipment, which may not be feasible for everyone.
Comparison Table: DeepSeek-R1 and Cloud Solutions
Peculiarity DeepSeek-R1 local Cloud solutions
Data privacy High Average
Delay Short Average
Setting Full control Limited
Expenses High initial cost Pay as you go
Recommendations
For those using the DeepSeek -R1 for coding or solving complex math problems, I recommend choosing the 7 billion or 14 billion parameter model to balance performance and resource usage. However, if you are using this model on limited hardware like a budget laptop, the 1.5 billion model should be enough.
Once all the settings are done, you are ready to run DeepSeek-R1 locally.
1. Prepare the input data : Tokenize the input data using a tokenizer.
inputs = tokenizer"Ваш входной текст здесь", return_tensors="pt"
2. Generate output : Pass the tokenized input to the model.
outputs = modelinputs
3. Post-processing : Decode the output to get human-readable text.
decoded_output = tokenizer.decodeoutputs[0], skip_special_tokens=True
Pros and Cons of DeepSeek-R1
Pros:
Privacy Concern : All data is stored locally peru mobile database for increased security.
Powerful Reasoning : Ideal for complex tasks that require thought and planning.
Cons:
Slower execution : Because reasoning models think through a problem step by step, answers may take longer than traditional LLMs.
Equipment Requirements : Larger models require more powerful equipment, which may not be feasible for everyone.
Comparison Table: DeepSeek-R1 and Cloud Solutions
Peculiarity DeepSeek-R1 local Cloud solutions
Data privacy High Average
Delay Short Average
Setting Full control Limited
Expenses High initial cost Pay as you go
Recommendations
For those using the DeepSeek -R1 for coding or solving complex math problems, I recommend choosing the 7 billion or 14 billion parameter model to balance performance and resource usage. However, if you are using this model on limited hardware like a budget laptop, the 1.5 billion model should be enough.