---
license: mit
language:
- ur
base_model:
- enstazao/Qalb-1.0-8B-Instruct
---
# Qalb-1.0-8B-Instruct - GGUF

This repository contains the GGUF (GGML Unified Format) version of the `enstazao/Qalb-1.0-8B-Instruct` model.

## Origin
This model was converted from its original Hugging Face format using the `llama.cpp` project.

## Purpose
`Qalb-1.0-8B-Instruct` is a general-purpose large language model, suitable for various natural language processing tasks, including text generation, question answering, and summarization.

## How to Use with Ollama
To use this GGUF model with Ollama, you can run the following command:

```bash
ollama run hf.co/ReySajju742/Qalb-1.0-gguf
```

This command will automatically download and set up the model for use with Ollama.

## Potential Enhancements
*   **Quantization**: This model is currently available in `f16` format. For reduced size and potentially faster inference with minimal performance impact, consider quantizing it further (e.g., to `Q4_K_M`). This can be done using the `llama.cpp` tools.
*   **LoRA Adapters**: The `llama.cpp` ecosystem also supports integrating LoRA (Low-Rank Adaptation) adapters, which can fine-tune the model for specific tasks or datasets without requiring a full model conversion.

For more details on these enhancements, please refer to the [llama.cpp GitHub repository](https://github.com/ggerganov/llama.cpp).

## Reproduction Instructions
The GGUF conversion process for this model involved the following steps:
1.  **Downloading the Hugging Face Model**: The original `enstazao/Qalb-1.0-8B-Instruct` model was downloaded from Hugging Face.
2.  **Cloning `llama.cpp`**: The `llama.cpp` repository was cloned to access its conversion tools.
3.  **Converting to GGUF**: The downloaded Hugging Face model was converted to the GGUF format (f16) using the `llama.cpp`'s `convert_hf_to_gguf.py` script.
4.  **Uploading to Hugging Face**: The resulting GGUF file was then uploaded to this repository (`ReySajju742/Qalb-1.0-gguf`).

This entire process can be reproduced using the provided Colab notebook, which automates these steps.