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README.md

@@ -1,50 +1,47 @@
 ---
-base_model: north/nb-llama-3.2-3B_1200
+base_model: "NB-Llama-3.2-3B"
 tags:
 - llama-cpp
-- gguf-my-repo
+- gguf
+- quantization
 ---
 
-# pere/nb-llama-3.2-3B_1200-Q4_K_M-GGUF
-This model was converted to GGUF format from [`north/nb-llama-3.2-3B_1200`](https://huggingface.co/north/nb-llama-3.2-3B_1200) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-Refer to the [original model card](https://huggingface.co/north/nb-llama-3.2-3B_1200) for more details on the model.
+# NB-Llama-3.2-3B-Q4_K_M-GGUF
+This model is a **quantized** version of the original [NB-Llama-3.2-3B](https://huggingface.co/north/nb-llama-3.2-3B), converted into the **GGUF format** using [llama.cpp](https://github.com/ggerganov/llama.cpp). Quantization significantly reduces the model's memory footprint, enabling efficient inference on a wide range of hardware, including personal devices, without compromising too much quality. These quantized models are mainly provided so that people can test out the models with moderate hardware. If you want to benchmark the models or further finetune the models, we strongly recommend the non-quantized versions. 
 
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
+## What is `llama.cpp`?
+[`llama.cpp`](https://github.com/ggerganov/llama.cpp) is a versatile tool for running large language models optimized for efficiency. It supports multiple quantization formats (e.g., GGML and GGUF) and provides inference capabilities on diverse hardware, including CPUs, GPUs, and mobile devices. The GGUF format is the latest evolution, designed to enhance compatibility and performance.
 
-```bash
-brew install llama.cpp
+## Benefits of This Model
+- **High Performance**: Achieves similar quality to the original model while using significantly less memory.
+- **Hardware Compatibility**: Optimized for running on a variety of hardware, including low-resource systems.
+- **Ease of Use**: Seamlessly integrates with `llama.cpp` for fast and efficient inference.
 
-```
-Invoke the llama.cpp server or the CLI.
+## Installation
+Install `llama.cpp` using Homebrew (works on Mac and Linux):
 
-### CLI:
 ```bash
-llama-cli --hf-repo pere/nb-llama-3.2-3B_1200-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b_1200-q4_k_m.gguf -p "The meaning to life and the universe is"
+brew install llama.cpp
 ```
 
-### Server:
-```bash
-llama-server --hf-repo pere/nb-llama-3.2-3B_1200-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b_1200-q4_k_m.gguf -c 2048
-```
+## Usage Instructions
 
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+### Using with `llama.cpp`
+To use this quantized model with `llama.cpp`, follow the steps below:
 
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
+#### CLI:
+```bash
+llama-cli --hf-repo north/nb-llama-3.2-3B-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b-q4_k_m.gguf -p "Your prompt here"
 ```
 
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
+#### Server:
+```bash
+llama-server --hf-repo north/nb-llama-3.2-3B-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b-q4_k_m.gguf -c 2048
 ```
 
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo pere/nb-llama-3.2-3B_1200-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b_1200-q4_k_m.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo pere/nb-llama-3.2-3B_1200-Q4_K_M-GGUF --hf-file nb-llama-3.2-3b_1200-q4_k_m.gguf -c 2048
-```
+For more information, refer to the [llama.cpp repository](https://github.com/ggerganov/llama.cpp).
+
+## Additional Resources
+- [Original Model Card](https://huggingface.co/north/nb-llama-3.2-3B)
+- [llama.cpp Repository](https://github.com/ggerganov/llama.cpp)
+- [GGUF Format Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/llama)