SandLogicTechnologies/Qwen2.5-14B-Instruct-GGUF

Qwen2.5-14B-Instruct

Qwen2.5-14B-Instruct is an instruction-aligned large language model from the Qwen family, designed for high-quality conversational interaction, structured task execution, and advanced reasoning. It is optimized to respond reliably to user prompts while maintaining coherence across long contexts and multi-turn exchanges.

The model is suitable for both research and production scenarios, supporting a wide range of natural language applications including analysis, summarization, coding assistance, and general dialogue.

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Model Overview

• Model Name: Qwen2.5-14B-Instruct
• Base Model: Qwen2.5-14B
• Architecture: Decoder-only Transformer
• Parameter Count: 14 Billion
• Context Window: Up to 128K tokens (implementation dependent)
• Modalities: Text
• Primary Languages: English, Chinese, multilingual capability
• Developer: Qwen
• License: Apache 2.0

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Design Objectives

This model is built to deliver strong performance in real-world instruction-following environments. Key design priorities include:

• Reliable adherence to user instructions
• Long-context comprehension and memory retention
• Robust logical and analytical reasoning
• Structured and formatted output generation
• Stable multi-turn conversational behavior

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Quantization Details

Q4_K_M

• Approx. ~71% size reduction
• Very low memory footprint (~8.37 GB)
• Optimized for CPU inference and low-VRAM GPUs
• Faster token generation speeds
• Minor degradation in complex analytical or long-chain reasoning tasks

Q5_K_M

• Approx. ~66% size reduction
• Better fidelity to the original FP16 model (9.79 GB)
• Improved coherence and reasoning consistency
• Recommended when slightly more memory is available

Training Overview

Pretraining

The base model is trained on a large and diverse multilingual corpus covering web text, code, academic material, and structured data. Training focuses on learning linguistic structure, knowledge representation, and long-range dependency modeling.

Instruction Alignment

The instruct variant is further refined using supervised fine-tuning and alignment methods to improve:

• Prompt interpretation accuracy
• Response clarity and usefulness
• Safety and controllability
• Step-by-step reasoning performance

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Core Capabilities

• Instruction adherence
  Accurately executes complex or multi-step prompts.

• Extended context processing
  Handles large documents, transcripts, and long conversations.

• Reasoning and problem solving
  Suitable for analytical tasks, explanations, and structured thinking.

• Multilingual interaction
  Supports multiple languages with strong English and Chinese performance.

• Structured output generation
  Produces formatted responses such as lists, tables, JSON, and stepwise solutions.

• Conversational consistency
  Maintains topic continuity across long dialogue sessions.

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Example Usage

llama.cpp

./llama-cli \
  -m SandlogicTechnologies\Qwen2.5-14B-Instruct_Q4_K_M.gguf \
  -p "Explain transformers in simple terms."

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Recommended Use Cases

• Conversational AI and virtual assistants
• Document understanding and summarization
• Research and technical explanation
• Programming and code guidance
• Knowledge exploration and tutoring
• Long-form content generation

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Acknowledgments

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

Special thanks to:

• The Qwen team for developing and releasing the Qwen2.5-14B-Instruct model.

• Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

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Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.