Fine-Tuned Local LLM Deployment
Budget: ₹12,500 – ₹37,500 INR
I want to run large-language-model workloads completely offline on my own laptop and have them fine-tuned so they perform five practical jobs for me: text generation, sentiment analysis, translation, project automation and, most importantly, natural audio conversation that can drive those automations.
Here is what I need:
• A clear, reproducible setup that installs a suitable base model locally (PyTorch/Transformers, llama.cpp, Ollama or any stack that will actually fit and run on typical laptop hardware).
• A fine-tuning pipeline that uses data already stored on my laptop—no cloud sources—so the model learns my personal writing style and task context.
• An audio front end (e.g., Whisper + a lightweight TTS) that lets me speak commands and receive spoken responses, wired into the model so I can trigger project-automation scripts by voice.
• Short demo scripts or notebooks showing successful text generation, sentiment analysis and translation, plus a working example of the audio-driven automation flow.
• Step-by-step documentation so I can replicate everything or retrain later.
I will consider the job complete once I can power up the laptop, execute a single command and interact with the fine-tuned model through voice, seeing the other three tasks (generation, sentiment, translation) handled on demand—all without an internet connection.
Here is what I need:
• A clear, reproducible setup that installs a suitable base model locally (PyTorch/Transformers, llama.cpp, Ollama or any stack that will actually fit and run on typical laptop hardware).
• A fine-tuning pipeline that uses data already stored on my laptop—no cloud sources—so the model learns my personal writing style and task context.
• An audio front end (e.g., Whisper + a lightweight TTS) that lets me speak commands and receive spoken responses, wired into the model so I can trigger project-automation scripts by voice.
• Short demo scripts or notebooks showing successful text generation, sentiment analysis and translation, plus a working example of the audio-driven automation flow.
• Step-by-step documentation so I can replicate everything or retrain later.
I will consider the job complete once I can power up the laptop, execute a single command and interact with the fine-tuned model through voice, seeing the other three tasks (generation, sentiment, translation) handled on demand—all without an internet connection.