Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit  
+base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit  
 tags:
 - text-generation-inference
 - transformers
@@ -13,11 +13,11 @@ language:
 
 ---
 
-# kubectl Operator Model
+# Hybrid Kubernetes Feature Model
 
 - **Developed by:** dereklck
 - **License:** Apache-2.0
-- **Fine-tuned from model:** [unsloth/Llama-3.2-1B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)
+- **Fine-tuned from model:** [unsloth/Llama-3.2-3B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit)
 - **Model type:** GGUF (compatible with Ollama)
 - **Language:** English
 
@@ -27,7 +27,10 @@ This Llama-based model was fine-tuned to assist users with Kubernetes commands a
 2. **Providing concise explanations about Kubernetes** for general queries.
 3. **Politely requesting additional information** if the instruction is incomplete or ambiguous.
 
+**Update:** Compared to the previous 1B model, the **3B model significantly reduces hallucinations** that sometimes occurred in the 1B model. Users can expect improved accuracy and reliability when interacting with this model.
+
 The model was trained efficiently using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
+
 ---
 
 ## How to Use the Model
@@ -92,17 +95,17 @@ This section provides instructions on how to run the model using Ollama and the
    Open your terminal and run the following command to create the model:
 
    ```bash
-   ollama create kubernetes_operator -f Modelfile
+   ollama create hybrid_kubernetes_feature_model -f Modelfile
    ```
 
-   This command tells Ollama to create a new model named `kubernetes_operator` using the configuration specified in `Modelfile`.
+   This command tells Ollama to create a new model named `hybrid_kubernetes_feature_model` using the configuration specified in `Modelfile`.
 
 3. **Run the Model**
 
    Start interacting with your model:
 
    ```bash
-   ollama run kubernetes_operator
+   ollama run hybrid_kubernetes_feature_model
    ```
 
    This will initiate the model and prompt you for input based on the template provided.
@@ -110,7 +113,7 @@ This section provides instructions on how to run the model using Ollama and the
    Alternatively, you can provide an instruction directly:
 
    ```bash
-   ollama run kubernetes_operator -p "Create a namespace called 'production'."
+   ollama run hybrid_kubernetes_feature_model -p "Create a namespace called 'production'."
    ```
 
    **Example Output:**
@@ -128,8 +131,8 @@ This section provides instructions on how to run the model using Ollama and the
 The model assists users by:
 
 - **Generating accurate `kubectl` commands** based on natural language descriptions.
-- **Providing brief explanations about Kubernetes** for general queries.
-- **Requesting additional information** if the instruction is incomplete or ambiguous.
+- **Providing concise explanations about Kubernetes** for general queries.
+- **Politely requesting additional information** if the instruction is incomplete or ambiguous.
 
 ### Intended Users
 
@@ -139,21 +142,35 @@ The model assists users by:
 
 ### Training Process
 
-- **Base Model:** Unsloth's Llama-3.2-1B-Instruct-bnb-4bit
+- **Base Model:** Unsloth's Llama-3.2-3B-Instruct-bnb-4bit
 - **Fine-tuning:** Leveraged the Unsloth framework and Hugging Face's TRL library for efficient training.
-- **Training Data:** Customized dataset focused on Kubernetes operations and `kubectl` command usage, containing approximately 200 entries.
+- **Training Data:** Customized dataset focused on Kubernetes operations and features, including `kubectl` command usage and general Kubernetes concepts, containing approximately 1,500 entries.
 
 ---
 
 ## Model Features
 
-### Command Generation in Bash Format
+### 1. Command Generation in Bash Format
+
+When the model generates CLI commands, it provides them in `bash` format, enclosed within code blocks for easy execution and clarity. This allows users to copy and paste the commands directly into their terminal.
+
+### 2. Handling Ambiguity with Polite Clarifications
 
-When the model generates CLI commands, it provides them in `bash` format, enclosed within code blocks for easy execution and clarity. This helps users to copy and paste the commands directly into their terminal.
+If the instruction is incomplete or ambiguous, the model will politely ask for the specific missing information instead of making assumptions. This ensures accuracy and prevents the execution of incorrect commands.
 
-### Examples
+### 3. Providing Concise Explanations
 
-#### **Example 1: Generating a Command**
+For general Kubernetes questions, the model offers concise and accurate explanations without unnecessary details, helping users understand concepts quickly.
+
+### 4. Enhanced Accuracy with 3B Model
+
+The transition to the 3B model has significantly **reduced hallucinations** that sometimes occurred in the 1B model. The 3B model provides more accurate and reliable responses, improving the overall user experience.
+
+---
+
+## Examples
+
+### Example 1: Generating a Command
 
 **Instruction:**
 
@@ -169,7 +186,7 @@ kubectl create namespace development
 
 ---
 
-#### **Example 2: Handling Ambiguity**
+### Example 2: Handling Ambiguity
 
 **Instruction:**
 
@@ -185,7 +202,7 @@ Which pod's logs would you like to retrieve?
 
 ---
 
-#### **Example 3: Providing Explanations**
+### Example 3: Providing Explanations
 
 **Instruction:**
 
@@ -203,8 +220,7 @@ A Deployment provides declarative updates for Pods and ReplicaSets, allowing you
 
 ## Limitations and Considerations
 
-- **Accuracy:** The model may occasionally produce incorrect or suboptimal commands. Always review the output before execution.
-- **Hallucinations:** In rare cases, the model might generate irrelevant or incorrect information. If the response seems off-topic, consider rephrasing your instruction.
+- **Accuracy:** While the 3B model improves accuracy, the model may occasionally produce incorrect or suboptimal commands. Always review the output before execution.
 - **Security:** Be cautious when executing generated commands, especially in production environments.
 
 ---
@@ -218,4 +234,8 @@ We welcome any comments or participation to improve the model and dataset. If yo
 
 ---
 
-**Note:** This model provides assistance in generating `kubectl` commands based on user input. Always verify the generated commands in a safe environment before executing them in a production cluster.
+**Note:** This model provides assistance in generating Kubernetes commands and explanations based on user input. Always verify the generated commands in a safe environment before executing them in a production cluster.
+
+---
+
+If you have any further requests or need additional adjustments, please let me know!