Descriptions:
Needle is a 26-million-parameter encoder-decoder transformer with one job: given a natural language request and a list of available tools, output the correct tool name and arguments as structured JSON. In this tutorial, Fahd Mirza walks through installing Needle, replacing its default Gemini-based synthetic data generator with a fully local Ollama model, and fine-tuning the result on a custom dataset — entirely offline on a single NVIDIA RTX A6000 GPU.
The video includes a clear architectural breakdown of how Needle works. An encoder stack of 12 layers reads the full input query using self-attention with grouped query attention (GQA) and RoPE positional encoding — deliberately stripped of the standard feed-forward block to stay compact. A separate eight-layer decoder generates the tool call token by token via masked self-attention, then cross-attends back to the encoder’s representation through a bridge layer before emitting structured JSON output. Weights, training code, and the data pipeline are all released under an MIT license, and the model is explicitly sized for on-device deployment on phones, watches, and glasses.
Mirza demonstrates generating 432 training examples covering three tools — get_weather, set_timer, and toggle_lights — with roughly 130 natural-language phrasings each plus 14 negative examples where no tool applies. The fine-tuned model is then validated against a held-out split saved to a checkpoint folder. The result is a reproducible pipeline for training a tiny, locally-runnable function-calling model without any cloud dependencies or proprietary API keys.
📺 Source: Fahd Mirza · Published July 03, 2026
🏷️ Format: Tutorial Demo
