---
language:
- en
- de
- es
base_model:
- ibm-granite/granite-3.1-8b-instruct
---
## SandLogic Technology Quantized Granite-3.1-8B-Instruct-GGUF

This repository contains Q4_KM and Q5_KM quantized versions of the [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) model. These quantized variants maintain the core capabilities of the original model while significantly reducing the memory footprint and increasing inference speed.

Discover our full range of quantized language models by visiting our [SandLogic Lexicon](https://github.com/sandlogic/SandLogic-Lexicon) GitHub. To learn more about our company and services, check out our website at [SandLogic](https://www.sandlogic.com).

## Model Details

- **Original Model**: Granite-3.1-8B-Instruct
- **Quantized Versions**: 
  - Q4_KM (4-bit quantization)
  - Q5_KM (5-bit quantization)
- **Base Architecture**: 8B parameter long-context instruct model
- **Developer**: Granite Team, IBM
- **License**: Apache 2.0
- **Release Date**: December 18th, 2024

## Quantization Benefits

### Q4_KM Version
- Reduced model size: ~4GB (75% smaller than original)
- Faster inference speed
- Minimal quality degradation
- Optimal for resource-constrained environments

### Q5_KM Version
- Reduced model size: ~5GB (69% smaller than original)
- Better quality preservation compared to Q4
- Balanced trade-off between model size and performance
- Recommended for quality-sensitive applications

## Supported Languages

The quantized models maintain support for all original languages:
- English
- German
- Spanish
- French
- Japanese
- Portuguese
- Arabic
- Czech
- Italian
- Korean
- Dutch
- Chinese

Users can fine-tune these quantized models for additional languages.

## Capabilities

Both quantized versions preserve the original model's capabilities:
- Summarization
- Text classification
- Text extraction
- Question-answering
- Retrieval Augmented Generation (RAG)
- Code related tasks
- Function-calling tasks
- Multilingual dialog use cases
- Long-context tasks including document/meeting summarization and QA

## Usage

```python
from llama_cpp import Llama

llm = Llama(
    model_path="models/granite-3.1-8b-instruct-Q4_K_M.gguf",
    verbose=False,
    # n_gpu_layers=-1, # Uncomment to use GPU acceleration
    # n_ctx=2048, # Uncomment to increase the context window
)

output = llm.create_chat_completion(
    messages =[
    {
        "role": "system",
        "content": "You are an AI Assistant"  
        ,
    },
    {"role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location."},
]
)

print(output["choices"][0]['message']['content'])

```



## Intended Use

These quantized models are designed for:
- Resource-constrained environments
- Edge deployment scenarios
- Applications requiring faster inference
- Building AI assistants for multiple domains
- Business applications with limited computational resources

## Training Information

The base model was trained on:
1. Publicly available datasets with permissive license
2. Internal synthetic data targeting specific capabilities
3. Small amounts of human-curated data

Detailed attribution can be found in the upcoming Granite 3.1 Technical Report.


## Acknowledgements

We thank Meta for developing the original IBM Granite model and the creators of the bigbio/med_qa dataset.
Special thanks to Georgi Gerganov and the entire llama.cpp development team for their outstanding contributions.
## Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our [support page](https://www.sandlogic.com/contact-us/).

## Explore More

For any inquiries or support, please contact us at support@sandlogic.com or visit our [support page](https://www.sandlogic.com/contact-us/).