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README.md

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+Below is a sample `README.md` file for your Hugging Face model repository, where the fine-tuned model has been uploaded in GGUF format. This file provides an overview of the model, its usage, and instructions for downloading and running it.
+
 ---
-license: apache-2.0
-datasets:
-- IraGia/gprMax_Train
-base_model:
-- unsloth/Meta-Llama-3.1-8B-Instruct
----
+
+# Meta-Llama-3.1-8B-Instruct-bnb-4bit Fine-Tuned on gprmax_train (GGUF Format)
+
+This repository contains the GGUF format of the fine-tuned `Meta-Llama-3.1-8B-Instruct-bnb-4bit` model, optimized for the `gprmax_train` dataset. The model was fine-tuned using the `unsloth` library for memory and time efficiency and has been converted to GGUF format for compatibility with popular inference frameworks like `llama.cpp`.
+
+## Model Details
+- **Base Model**: `Meta-Llama-3.1-8B-Instruct-bnb-4bit`
+- **Fine-Tuning Dataset**: `gprmax_train`
+- **Fine-Tuning Framework**: `unsloth`
+- **Quantization**: 4-bit (bnb)
+- **GGUF Conversion**: The model has been converted to GGUF format for efficient inference.
+
+## Download the Model
+You can download the GGUF model file from the "Files and versions" section of this repository or use the following command:
+
+```bash
+huggingface-cli download sriyaflows/gprmax_8 --include "*.gguf"
+```
+
+## Usage with `llama.cpp`
+
+To run the model using `llama.cpp`, follow these steps:
+
+### 1. Install `llama.cpp`
+Clone and build `llama.cpp`:
+
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+make
+```
+
+### 2. Download the GGUF Model
+Download the GGUF model file from this repository:
+
+```bash
+wget https://huggingface.co/sriyaflows/gprmax_8/resolve/main/gprmax_8.gguf
+```
+
+### 3. Run Inference
+Use the `main` executable from `llama.cpp` to run inference:
+
+```bash
+./main -m your-model-name.gguf -p "Your input text here" -n 512
+```
+
+### Example
+```bash
+./main -m your-model-name.gguf -p "What is the output for this input?" -n 512
+```
+
+## Usage with Python
+You can also use the GGUF model with Python libraries like `llama-cpp-python`:
+
+### 1. Install `llama-cpp-python`
+```bash
+pip install llama-cpp-python
+```
+
+### 2. Load and Run the Model
+```python
+from llama_cpp import Llama
+
+# Load the GGUF model
+llm = Llama(model_path="gprmax_8.gguf")
+
+# Generate text
+output = llm("Your input text here", max_tokens=512)
+print(output["choices"][0]["text"])
+```
+
+## Fine-Tuning Details
+The model was fine-tuned on the `gprmax_train` dataset using the `unsloth` library, which optimized memory usage and training time. The fine-tuning process leveraged an L4 GPU with 32GB of RAM and 8 vCPUs.
+
+## License
+This model is licensed under the same terms as the base `Meta-Llama-3.1-8B-Instruct-bnb-4bit` model. Refer to the [Hugging Face model card](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) for details.
+
+## Acknowledgments
+- Thanks to Hugging Face for the `transformers` library.
+- Thanks to the `unsloth` team for their memory-efficient fine-tuning tools.
+- Thanks to the creators of the `gprmax_train` dataset.
+- Thanks to `ggerganov` for `llama.cpp` and the GGUF format.
+
+---
+
+This `README.md` file is designed to provide clear instructions for downloading and using the GGUF model.