SandLogicTechnologies/openchat-3.6-8b-20240522-GGUF

OpenChat-3.6-8B-20240522

OpenChat 3.6 8B is an instruction-aligned conversational language model built for high-quality dialogue, structured task execution, and consistent multi-turn interaction. It is optimized to function as a practical assistant capable of reasoning, explanation, and general-purpose conversation.

The model is designed for both research experimentation and real-world deployment where efficient inference and reliable conversational behavior are required.

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Model Overview

• Model Name: OpenChat 3.6 8B
• Release Version: 2024-05-22
• Base Model: meta-llama/Meta-Llama-3-8B
• Architecture: Decoder-only Transformer
• Parameter Count: 8 Billion
• Context Window: Implementation dependent
• Modalities: Text
• Primary Language: English
• Developer: OpenChat Team
• License: Apache 2.0

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Design Objectives

OpenChat 3.6 8B is developed to provide dependable conversational performance while remaining computationally efficient.

Key design priorities include:

• Natural and coherent conversational responses
• Strong compliance with user instructions
• Reliable multi-step reasoning capability
• Stable long-turn dialogue handling
• Practical deployment across varied hardware environments

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Quantization Details

Q4_K_M

• Approx. ~71% size reduction (4.58 GB)
• Strong compression for reduced memory usage
• Optimized for CPU inference and limited VRAM GPUs
• Faster generation speeds for local deployments
• Slight reduction in reasoning precision for complex prompts

Q5_K_M

• Approx. ~66% size reduction (5.34 GB)
• Higher precision compared to lower-bit variants
• Improved logical consistency and response quality
• Better performance for reasoning-intensive workloads
• Recommended when additional memory is available

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Training Overview

Pretraining Foundation

The model inherits linguistic knowledge and general reasoning ability from the Meta-Llama-3-8B pretrained foundation, which is trained on large-scale text corpora to capture language structure, knowledge representation, and contextual relationships.

Instruction Alignment

Additional fine-tuning enhances the model’s ability to function as an interactive assistant. Alignment improvements target:

• Prompt understanding and execution
• Response clarity and usefulness
• Conversational coherence
• Controlled and safe response generation

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Core Capabilities

• Conversational interaction
  Produces natural and context-aware dialogue.

• Instruction following
  Executes complex or multi-step user requests.

• Reasoning and explanation
  Supports analytical thinking and structured responses.

• Context continuity
  Maintains awareness across extended conversations.

• Structured response generation
  Handles formatted outputs such as lists, steps, and organized explanations.

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Example Usage

llama.cpp

./llama-cli 
-m SandlogicTechnologies\openchat-3.6-8b_Q4_K_M.gguf 
-p "Explain reinforcement learning in simple terms."

Recommended Use Cases

• Conversational AI assistants
• Interactive knowledge systems
• Technical explanation and tutoring
• Research and experimentation with dialogue models
• Prompt-driven workflow automation
• Local inference deployments

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Acknowledgments

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

Special thanks to:

• The openchat team for developing and releasing the openchat-3.6-8b-20240522 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.