​

Deepseek

Deepseek is a powerful large language model (LLM) designed for text generation, reasoning, and code generation tasks. This model is optimized for performance on edge devices through the Exla SDK.

​

Overview

Deepseek is a state-of-the-art language model that can be used for a variety of natural language processing tasks, including:

• Text generation and completion
• Question answering
• Code generation and explanation
• Reasoning and problem-solving

​

Usage

Here’s a simple example of how to use the Deepseek model for text generation:

from exla.models.deepseek_r1 import deepseek_r1

model = deepseek_r1()

model.run()

​

Parameters

When generating text with Deepseek, you can customize various parameters:

response = model.generate(
    "Write a short poem about artificial intelligence.",
    max_tokens=100,
    temperature=0.7,
    top_p=0.9,
    top_k=40
)

​

Advanced Usage

​

Streaming Responses

For applications that require real-time responses, you can use the streaming API:

model = DeepseekR1()

for chunk in model.generate_stream("Explain quantum computing."):
    print(chunk, end="", flush=True)

​

System Prompts

You can provide system prompts to guide the model’s behavior:

response = model.generate(
    "What is machine learning?",
    system_prompt="You are a helpful AI assistant that explains complex topics in simple terms."
)

​

Performance Considerations

The Deepseek model is optimized for edge deployment but still requires significant computational resources. Consider the following when using this model:

• Memory usage: ~2GB
• Inference speed depends on the hardware capabilities
• For longer generations, consider using streaming to improve user experience

​

Example Applications

• Chatbots and virtual assistants
• Content generation tools
• Code completion systems
• Educational applications

​

Limitations

• May occasionally generate incorrect or nonsensical information
• Performance varies based on prompt quality and complexity
• Not suitable for real-time applications on very resource-constrained devices

For more information on optimizing model performance, see the Custom Models Optimization guide.