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 support@sandlogic.com or visit our Website.