SandLogicTechnologies/DeepSeek-R1-Distill-Llama-8B-GGUF

DeepSeek-R1-Distill-Llama-8B Quantized Models

This repository contains Q4_KM and Q5_KM quantized versions of the DeepSeek-R1-Distill-Llama-8B model, optimized for efficient deployment while maintaining strong performance.

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Model Description

These models are quantized versions of DeepSeek-R1-Distill-Llama-8B, which is a distilled 8B parameter model based on the Llama architecture. The original model demonstrates that reasoning patterns from larger models can be effectively distilled into smaller architectures.

Available Quantized Versions

1. Q4_KM Version

   • 4-bit quantization using the K-means method
   • Approximately 4GB model size
   • Optimal balance between model size and performance
   • Recommended for resource-constrained environments

2. Q5_KM Version

   • 5-bit quantization using the K-means method
   • Approximately 5GB model size
   • Higher precision than Q4 while maintaining significant size reduction
   • Recommended when higher accuracy is needed

Usage

pip install llama-cpp-python 

Please refer to the llama-cpp-python documentation to install with GPU support.

Basic Text Completion

Here's an example demonstrating how to use the high-level API for basic text completion:

from llama_cpp import Llama

llm = Llama(
    model_path="model/path/",
    verbose=False,
    # n_gpu_layers=-1, # Uncomment to use GPU acceleration
    # n_ctx=2048, # Uncomment to increase the context window
)

output = llm(
    "Q: Name the planets in the solar system? A: ", # Prompt
    max_tokens=32, # Generate up to 32 tokens
    stop=["Q:", "\n"], # Stop generating just before a new question
    echo=False # Don't echo the prompt in the output
)

print(output["choices"][0]["text"])

License

This model inherits the license of the original DeepSeek-R1-Distill-Llama-8B model. Please refer to the original model's license for usage terms and conditions.

Acknowledgments

We thank the DeepSeek AI team for open-sourcing their distilled models and demonstrating that smaller models can achieve impressive performance through effective distillation techniques.