---
license: apache-2.0
language:
  - en
base_model:
  - mistralai/Mistral-Nemo-12B-Base
tags:
  - text-generation
  - chat
  - instruction-following
  - reasoning
  - coding
---

# Mistral-NeMo-12B-Instruct

Mistral-NeMo-12B-Instruct is an instruction-optimized large language model built on top of the Mistral-NeMo-12B foundation. It is tailored for dialogue systems, structured task execution, and complex reasoning scenarios. The model emphasizes clarity, stability across multi-turn interactions, and high-quality output generation for both technical and general-purpose tasks.

For streamlined local deployment, GGUF quantized builds (including Q4_K_M and Q5_K_M) are commonly used to reduce memory demands and improve inference performance on consumer hardware.

---

## Overview

- **Model name:** Mistral-NeMo-12B-Instruct
- **Base architecture:** Decoder-only transformer
- **Developer:** NVIDIA & Mistral AI
- **License:** Apache-2.0
- **Quantized Versions:**
  - Q4_K_M (4-bit)
  - Q5_K_M (5-bit)
- **Parameter count:** ~12B
- **Tokenizer:** NeMo-aligned tokenizer
- **Model type:** Instruction-tuned language model

---

## Quantization Details

### Q4_K_M

- Approx. ~ 70% size reduction (~ 6.96)
- Reduced VRAM/RAM requirements
- Suitable for CPU-heavy or limited-GPU setups
- Faster inference speed
- Slight quality trade-off in edge cases

### Q5_K_M

- Approx. ~ 64% size reduction (~ 8.13)
- Balanced compression and quality
- Improved stability over 4-bit variants
- Better retention of reasoning depth
- Preferred when additional memory is available

---

## Key Features

- **Instruction alignment**
  Tuned to respond accurately to detailed prompts.

- **Dialogue consistency**
  Maintains structured context across extended exchanges.

- **Problem-solving capability**
  Handles analytical and multi-step reasoning tasks.

- **Programming support**
  Assists with code writing, refactoring, and explanations.

- **Formatted output generation**
  Capable of structured responses when required.

- **Extended context handling**
  Designed to manage long prompts efficiently.

---

## Training Details

This model originates from the Mistral-NeMo-12B base and undergoes additional refinement to enhance responsiveness and alignment with user instructions.

- Pretraining: Large-scale autoregressive training on diverse multilingual and code datasets.
- Architecture: 12B parameter transformer decoder model.
- Instruction Tuning: Supervised fine-tuning for improved instruction following and chat performance.
- Context Length: Supports up to 128K token context window.

---

## Usage

### llama.cpp

```bash
./llama-cli \
  -m SandlogicTechnologies\Mistral-NeMo-12B-Instruct_Q4_K_M.gguf \
  -p "Summarize the key differences between CNNs and Transformers."
```

---

## Recommended Use Cases

- **Conversational systems**
  Multi-turn assistant-style interactions.

- **Technical documentation support**
  Drafting and summarizing structured material.

- **Code-related workflows**
  Generating and reviewing source code.

- **Analytical reasoning tasks**
  Assisting with structured logic and problem solving.

- **Automation pipelines**
  Producing formatted outputs for downstream systems.

---


## Acknowledgments

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

Special thanks to:
- The [mistralai](https://huggingface.co/mistralai) team for developing and releasing the [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) 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/).