---
license: apache-2.0
language:
- en
base_model:
- openchat/openchat-3.6-8b-20240522
tags:
- text-generation
- chat
- instruction-tuned
- reasoning
- conversational-ai
- alignment
---

## 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.

---

## 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  

---

## 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  

---

## 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  

---

## 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  

---

## 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.

---

## 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  

---

## Acknowledgments

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

Special thanks to:
- The [openchat](https://huggingface.co/openchat) team for developing and releasing the [openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) model.

- **Georgi Gerganov** and the entire [`llama.cpp`](https://github.com/ggerganov/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](https://www.sandlogic.com/).