> [!important] https://chatgpt.com/share/6748b635-f874-800f-80aa-072a0631841a To develop a chatbot that mirrors your unique conversational style using Ollama, follow these concise steps: - Data Collection - Export Messages: Use [iMazing](https://imazing.com/) to export your iPhone text messages to your computer or get it manually with the [[SQLite]] db located at `~/Library/Messages/chat.db` - Data Preparation - Clean Data: Remove sensitive information and correct any errors. - Format Data: Organize messages into a structured format, such as JSON or CSV, distinguishing between your inputs and responses. - Model Selection - Choose a Model: Select an appropriate model from Ollama’s offerings, like Llama 3.2 or Mistral. - Download Model: Use the command ollama pull [model_name] to download the selected model. - Fine-Tuning - Prepare Trakining Data: Format your dataset into prompt-response pairs suitable for training. - Fine-Tune Model: Utilize Ollama’s fine-tuning capabilities to train the model on your dataset, adjusting hyperparameters as needed. - Implementation - Set Up API: Start the Ollama server with ollama serve to interact with the model via API endpoints. - Develop Interface: Create a chatbot interface that connects to the Ollama API for sending and receiving messages. - Testing and Deployment - Test Responses: Ensure the chatbot’s outputs align with your conversational style. - Deploy Chatbot: Implement the chatbot to manage text communications as intended. ## Fine-tune an Ollama Model To emulate your unique conversational style, follow these steps: 1. Prepare Training Data - Format Data: Structure your text message history into a [[JSONL]] (JSON Lines) file, where each line represents a message pair in the following format: ```json {"prompt": "Your friend's message here", "completion": "Your response here"} ``` - Ensure that each prompt corresponds to a message from your contacts, and each completion is your reply. 2. Fine-Tune the Model - Initiate Fine-Tuning: Use the following command to fine-tune the model with your dataset: ```sh ollama create my-custom-model --from [base_model_name] --file [path_to_your_dataset.jsonl] ``` - Replace `[base_model_name]` with the name of the pre-trained model you’re fine-tuning (e.g., llama2), and `[path_to_your_dataset.jsonl]` with the path to your prepared dataset file. - Monitor Training: Keep an eye on the training process to ensure it progresses smoothly and meets your performance expectations. 3. Validate the Fine-Tuned Model - Test Responses: After fine-tuning, test the model’s outputs to ensure they align with your conversational style. - Iterate as Necessary: If the model’s responses are not satisfactory, consider refining your dataset or adjusting training parameters, and then repeat the fine-tuning process. <%* // const date = tp.file.last_modified_date("YYYYMMDD") // const filename = date + " " + tp.file.title const filename = tp.file.title await tp.file.move(`archive/${filename}`) %>