Steps to Deploy DeepSeek-V3.1-Base
The following key steps need to be followed to deploy this large-scale language model locally:
1. Environmental preparation
- Ensure Python 3.8+ and PyTorch environment
- Recommended high-performance GPUs such as NVIDIA A100
- Install the necessary libraries:
pip install transformers torch safetensors - Check CUDA version (11.8+ recommended)
2. Model downloads
- Download weights file via Hugging Face page or CLI
- CLI download command:
huggingface-cli download deepseek-ai/DeepSeek-V3.1-Base - Note: Model files can be several terabytes in size and require sufficient storage space.
3. Model loading
Use the Transformers library to load the model:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "deepseek-ai/DeepSeek-V3.1-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bf16", device_map="auto")4. Resource optimization
Due to the large amount of resources required for the 685 billion parameters, it is recommended that: multiple GPUs, model parallelism techniques, or low-precision formats (e.g., F8_E4M3) are used to optimize operational efficiency.
This answer comes from the articleDeepSeek-V3.1-Base: a large-scale language model for efficiently processing complex tasksThe
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