Instructions to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
```

Unsloth Studio

How to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M
```

Lemonade

How to use SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF:IQ3_M

Run and chat with the model

lemonade run user.deepseek-coder-1.3b-instruct-GGUF-IQ3_M

List all available models

lemonade list

DeepSeek-Coder-1.3B-Instruct

DeepSeek-Coder-1.3B-Instruct is a compact instruction-tuned code generation model developed by DeepSeek AI, designed for programming assistance, code completion, debugging, reasoning, and software engineering workflows. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

Unlike general-purpose language models, DeepSeek-Coder-1.3B-Instruct is specifically optimized for software development tasks. It combines strong instruction-following capability with programming knowledge across multiple languages, enabling efficient code generation, explanation, refactoring, and debugging while maintaining a lightweight deployment footprint.

The quantized formats significantly reduce memory requirements while preserving coding capability and instruction-following quality, making the model suitable for local development environments, IDE integrations, edge deployments, and resource-efficient programming assistants.

Model Overview

Model Name: DeepSeek-Coder-1.3B-Instruct
Base Model: deepseek-ai/deepseek-coder-1.3b-instruct
Architecture: Decoder-Only Transformer
Parameter Count: Approximately 1.3 Billion Parameters
Modalities: Text
Primary Languages: Multilingual
Developer: DeepSeek AI
License: MIT

Quantization Formats

This repository provides various GGUF quantized versions of the DeepSeek-Coder-1.3B-Instruct model optimized for efficient local inference using llama.cpp.

IQ3_M

Size reduction of approx 75.03% (641.62 MB) compared to 16-bit (2.51 GB)
Aggressive 3-bit quantization optimized for lightweight coding assistants and low-memory development environments
Suitable for code completion, syntax generation, and programming support on consumer hardware
Enables efficient deployment in local IDEs and resource-constrained systems
Complex software architecture reasoning and large-scale code generation may experience reduced fidelity compared to higher-precision variants

IQ4_NL

Size reduction of approx 70.98% (746.04 MB) compared to 16-bit (2.51 GB)
Advanced 4-bit non-linear quantization designed to preserve programming accuracy and instruction-following quality
Better suited for debugging, code explanation, refactoring, and structured code generation workflows
Designed to reduce quantization loss while maintaining reliable coding performance
May require slightly increased computational overhead during inference

IQ4_XS

Size reduction of approx 72.15% (715.94 MB) compared to 16-bit (2.51 GB)
Balanced 4-bit quantization focused on efficient inference and dependable software development assistance
Provides a practical balance between memory efficiency, coding quality, and runtime performance
Suitable for local coding assistants, educational programming tools, and development automation
Maintains reliable performance across most practical software engineering workloads

Training Background (Original Model)

DeepSeek-Coder-1.3B-Instruct is trained with an emphasis on programming languages, software engineering knowledge, instruction following, and code reasoning across diverse code repositories and technical datasets.

Pretraining

Large-scale pretraining using source code and technical documentation across multiple programming languages
Focus on programming semantics, code representation learning, and software engineering knowledge
Optimized for downstream coding, reasoning, debugging, and code generation tasks

Instruction Tuning

Refined using instruction-following datasets focused on software development workflows
Enhanced for conversational coding assistance, debugging, and structured code generation
Improved consistency across code completion, explanation, refactoring, and technical question answering

Key Capabilities

Code Generation Generates high-quality code across multiple programming languages.
Code Completion Assists developers with intelligent code completion and implementation suggestions.
Debugging & Refactoring Explains, analyzes, and improves existing source code.
Programming Reasoning Supports logical reasoning over algorithms, software design, and implementation details.
Instruction Following Produces structured responses for coding and software engineering tasks.
Efficient Local Deployment Quantized variants enable practical coding assistants on consumer hardware.

Usage Example

Using llama.cpp

./llama-cli \
  -m SandLogicTechnologies/DeepSeek-Coder-1.3B-Instruct_IQ4_NL.gguf \
  -p "Write a Python function that implements Dijkstra's shortest path algorithm."

Recommended Usecases

Code Completion Accelerate software development with intelligent code suggestions.
Programming Assistants Build lightweight AI coding assistants for local development environments.
Debugging & Code Review Analyze, explain, and improve existing source code.
Educational Programming Support programming education with code explanations and examples.
Developer Productivity Automate repetitive coding tasks and technical documentation generation.
Research & Experimentation Evaluate compact code-specialized language models for efficient local inference.

Acknowledgments

These quantized models are based on the original work by the DeepSeek AI development team.

Special thanks to:

The DeepSeek AI team for developing and releasing the DeepSeek-Coder-1.3B-Instruct model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

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GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

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Model tree for SandLogicTechnologies/deepseek-coder-1.3b-instruct-GGUF

Base model

deepseek-ai/deepseek-coder-1.3b-instruct

Quantized

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