Update README.md

README.md

@@ -7,8 +7,10 @@ base_model:
 pipeline_tag: question-answering
 ---
 
+# LLaMA 3.1-8B Fine-Tuned on ChatDoctor Dataset
+
 ## Model Overview
-This model is a fine-tuned version of the LLaMA 3.1-8B model, trained on a curated selection of 1,000 samples from the **ChatDoctor (HealthCareMagic-100k)** dataset. It has been optimized for tasks related to medical consultations.
+This model is a fine-tuned version of the LLaMA 3.1-8B model, trained on a curated selection of 1,122 samples from the **ChatDoctor (HealthCareMagic-100k)** dataset. It has been optimized for tasks related to medical consultations.
 
 - **Base Model**: LLaMA 3.1-8B
 - **Fine-tuning Dataset**: 1,122 samples from ChatDoctor dataset
@@ -26,7 +28,7 @@ This model is designed to assist in:
 |------------------------------|----------------------------|
 | **Model Type**               | Causal Language Model      |
 | **Architecture**             | LLaMA 3.1-8B              |
-| **Training Data**            | ChatDoctor (1,000 samples) |
+| **Training Data**            | ChatDoctor (1,122 samples) |
 | **Quantization**             | Q4_0                      |
 | **Deployment Format**        | GGUF                      |
 
@@ -48,30 +50,8 @@ The model was fine-tuned with the following hyperparameters:
 
 Validation was performed using a separate subset of the dataset. The final training and validation loss are as follows:
 
-![Training and Validation Loss](https://your-image-link-here.com/training-validation-loss.png)
+![Training and Validation Loss](train-val-curve.png)
 
 ## Usage
 ### Loading the Model
-This model is hosted in **GGUF format** for optimal deployment. You can load and run the model using **LLaMA.cpp**.
-
-#### Steps to Use
-1. Clone the LLaMA.cpp repository:
-    ```bash
-    git clone https://github.com/ggerganov/llama.cpp
-    cd llama.cpp
-    make
-    ```
-
-2. Download the model from Hugging Face:
-    ```bash
-    huggingface-cli login
-    wget https://huggingface.co/your-username/llama-3.1-8B-gguf/resolve/main/output_model.gguf
-    ```
-
-3. Run inference:
-    ```bash
-    ./main -m output_model.gguf -p "What are the symptoms of a common cold?" -t 4 -n 100
-    ```
-
-### Quantization Details
-The model is quantized to **Q4_0** for faster inference while maintaining reasonable accuracy. You can run it efficiently on CPUs with low memory requirements.
+This model is hosted in **GGUF format** for optimal deployment. You can load and run the model using **LLaMA.cpp**.