DeepSeek-Coder-V2-Lite-Instruct-GGUF

Quantized DeepSeek-Coder-V2-Lite-Instruct Models

This repository provides optimized GGUF quantized versions of the DeepSeek-Coder-V2-Lite-Instruct model. These lightweight 4-bit and 5-bit variants maintain the strong reasoning and code generation capabilities of the original model, while drastically reducing compute and memory requirements, ideal for local inference or edge deployment.

Model Overview

• Original Model: DeepSeek-Coder-V2-Lite-Instruct
• Quantized Versions:
  • Q4_K_M (4-bit quantization)
  • Q5_K_M (5-bit quantization)
• Architecture: Decoder-only Transformer
• Base Model: DeepSeek-Coder-V2-Lite
• Modalities: Text only
• Developer: DeepSeek-AI
• License: deepseek-license
• Language: English, Chinese

Quantization Details

Q4_K_M Version

• Approx. 9.66 GB memory footprint around 70% model size reduction
• Best suited for consumer GPUs or CPU-based inference
• Slight accuracy trade-off for maximum efficiency

Q5_K_M Version

• Approx. 11.04 GB footprint around 65% size reduction
• Near full precision quality retention
• Ideal for scenarios requiring balanced speed and accuracy

Key Features

• State-of-the-art reasoning and code generation performance
• Optimized for multi-turn code completion and debugging assistance
• Fine-tuned on extensive programming and instruction datasets
• Strong performance in reasoning, debugging, and natural language coding tasks
• Supports long-context generation up to 32K tokens

Usage

This model is suited for developers and researchers working on program synthesis, code explanation, or interactive coding assistants.

llama.cpp (text-only)

./llama-cli -hf SandLogicTechnologies/DeepSeek-Coder-V2-Lite-Instruct-GGUF -p "Write a Python function to reverse a linked list."

Model Data

Dataset Overview

• The DeepSeek-Coder-V2-Lite-Instruct model was trained on large-scale, high-quality open datasets consisting of:
  • General programming languages (Python, C++, Java, Rust, etc.)
  • Instruction-following data for reasoning and conversation
  • StackExchange and competitive coding datasets
  • Synthetic reasoning datasets for enhanced contextual understanding

Recommended Use Cases

• AI Coding Assistants : - Build real-time code assistants with low-latency responses.
• Code Analysis & Debugging: - Detect, explain, or correct programming errors.
• Educational Platforms : - Support interactive programming tutorials and practice systems.
• Edge & Low-resource Deployment : - Run code-capable LLMs on devices with limited memory (e.g., laptops, Jetson, Raspberry Pi).

Acknowledgments

These quantized models are derived from the original DeepSeek-Coder-V2-Lite-Instruct developed by DeepSeek-AI.

Special thanks to:

• The deepseek team for developing and releasing the DeepSeek-Coder-V2-Lite-Instruct model.
• Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at [email protected] or visit our Website.