Ollama Model Tuner

What This Skill Does

The Ollama Model Tuner is a specialized OpenClaw skill designed to bring sophisticated LLM optimization workflows directly to your local machine. Developed by gblockchainnetwork, this tool bridges the gap between raw local models and production-ready applications by providing an automated interface for prompt engineering, Modelfile customization, and LoRA-based fine-tuning. Unlike cloud-based alternatives that require data transmission to third-party servers, this skill operates entirely within your local environment, ensuring that sensitive data used for fine-tuning remains private and secure. It utilizes local eval metrics to iterate on system prompts and model configurations, allowing users to achieve higher accuracy for specific domains like classification, sentiment analysis, or task-specific instruction following.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/gblockchainnetwork/ollama-model-tuner Ensure that you have Ollama version 0.3 or higher installed, along with Python 3.10+, as these are mandatory dependencies for the tuner to manage model weights and perform evaluation loops. Data preparation should be handled in standard JSONL or CSV formats as specified in the source documentation.

Use Cases

• Domain Specialization: Refining a general model to understand proprietary technical terminology for your company's documentation.
• Prompt Optimization: Systematically testing different system prompts to minimize hallucinations or output formatting errors.
• Performance Benchmarking: Running iterative eval loops to see how dataset adjustments affect model classification accuracy.
• Local Data Privacy: Conducting fine-tuning tasks where the training data must never leave the local hardware for security compliance.

Example Prompts

1. "Tune ollama llama3 to better handle my technical support logs using the dataset in ./data/support_logs.csv."
2. "Optimize ollama model for sentiment analysis; I need to improve performance on my customer feedback dataset."
3. "Fine-tune local LLM with the new instructions in prompts/system.md and report back on the test score improvements."

Tips & Limitations

• Hardware Requirements: Fine-tuning, especially LoRA, is resource-intensive. Ensure you have sufficient GPU VRAM before initiating large training cycles.
• Data Quality: Garbage-in-garbage-out applies; ensure your JSONL/CSV datasets are cleaned and formatted correctly for the specific task type.
• Model Compatibility: Always verify that your target model supports fine-tuning configurations. Some highly quantized models may not respond well to extensive parameter adjustments.