If you've been following the AI space, you know that open-source large language models have been catching up to proprietary ones at an incredible pace. DeepSeek-LLM is one of the most impressive examples of this trend.
Quick Deploy
```bash # Install with pip pip install transformers torch
# Or use Docker docker pull deepseek-ai/deepseek-llm docker run -p 8000:8000 deepseek-ai/deepseek-llm ```
Python Quick Start
```python from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-llm-7b-chat") tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-llm-7b-chat")
inputs = tokenizer("Write a Python function to sort a list", return_tensors="pt") outputs = model.generate(**inputs, max_length=500) print(tokenizer.decode(outputs[0])) ```
Use Cases
Code Generation: DeepSeek excels at writing Python, JavaScript, and TypeScript. Great for scaffolding new features or writing unit tests.
Technical Documentation: Turn rough notes into polished API docs.
Data Analysis: Help write pandas queries and suggest visualization approaches.
Chatbot Development: Solid foundation for conversational AI products.
Hardware Requirements
- 7B model: 16GB VRAM - 67B model: 80GB+ VRAM or use quantization
The team provides excellent documentation and pre-built Docker images for easy setup.