Python · Mistral-7B · LoRA · Hugging Face · GGUF · Ollama · llama-cpp-python · Google Colab · Gradio · CLI · RAG · NLP · AI Model Fine-Tuning · Cloud Deployment
JarvisX V2
JarvisX V2 is a custom-trained AI assistant built by fine-tuning the Mistral-7B model using 137,300 domain-specific examples across 7 operational modes. The system integrates engineering automation, system monitoring, business workflows, creative tools, and bilingual conversational intelligence. It supports both cloud and local deployment using Hugging Face, GGUF, and Ollama for efficient and scalable inference.
Challenges
- Training a large language model efficiently with limited compute resources
- Optimizing model performance while keeping inference lightweight
- Designing a modular multi-mode AI architecture
- Ensuring accurate domain-specific responses across multiple industries
- Supporting both cloud and local deployment environments
Solution
- Used LoRA fine-tuning to reduce training cost and model size
- Quantized model and deployed using GGUF and Ollama for efficient inference
- Designed modular orchestration system separating modes and execution layers
- Created structured training datasets for each operational domain
- Implemented hybrid deployment supporting Hugging Face Spaces and local inference
Outcomes
- Successfully trained custom Mistral-7B assistant with high accuracy
- Achieved fast inference (1-2 seconds cloud, 10-30 seconds local)
- Built fully functional multi-mode AI assistant platform
- Enabled real-time automation, monitoring, and engineering workflows
- Created scalable architecture for future AI assistant expansion
Technical Deep Dive
JarvisX V2 – Custom AI Assistant
Overview
JarvisX V2 is a custom AI assistant built by fine-tuning the Mistral-7B model using 137,300 domain-specific training examples. It supports engineering, automation, and bilingual conversational tasks.
Problem
Generic LLMs lacked domain-specific accuracy and could not effectively handle engineering workflows, system monitoring, or operational automation.
Solution
Fine-tuned Mistral-7B using LoRA and deployed the model using Hugging Face and Ollama, enabling both cloud and local inference. Designed a modular assistant with multiple operational modes and automation capabilities.
Result
Delivered a scalable, production-ready AI assistant with fast inference, improved domain accuracy, and support for real-world engineering and productivity workflows.
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