1. Load the model : Use the following code to load the model into PyTorch.
model = AutoModel.from_pretrained"путь_к_весам_модели"
tokenizer = AutoTokenizer.from_pretrained"путь_к_токенизатору"
2. Fine-tuning optional : If you are working with a custom dataset, you may need to fine-tune the model.
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 panama mobile database 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:
Cost-effective : no API fees, free to launch.
Privacy Concern : All data is stored locally for increased security.
Powerful Reasoning : Ideal for complex tasks that require thought and planning.