Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots (cactuscompute.com)

Article summary

Cactus has released Needle 2, a 14MB open-source model for tool calling, device use, and structured extraction, designed for tiny devices such as phones, wearables, and smart home devices. The model is trained on a proprietary 115B-token corpus and post-trained on 38B tokens, and it achieves competitive performance with larger models on several benchmarks. Needle 2 is designed to run on devices with limited memory and computational resources, making it suitable for edge AI applications. The model is licensed under Apache 2.0 and is available on Hugging Face.

Main themes

• Edge AI
• Tiny models
• Tool calling
• Device use
• Structured extraction
• Low-resource devices

What commenters say

• The model's small size and low computational requirements make it suitable for edge AI applications, but its performance may be limited by its lack of world knowledge.
• The model's ability to recognize and respond to tool calls is impressive, but its tendency to generate false positives is a concern.
• Fine-tuning the model for specific tasks and devices can improve its performance, but it may not be enough to overcome its limitations.
• The model's confidence score can be used to filter out uncertain responses, but it is unclear whether the confidence scores are well-calibrated.
• The model's performance is not comparable to larger language models, but it is designed for a specific use case where size and computational resources are limited.
• The use of engrams and other architectural choices allows the model to achieve competitive performance with larger models, despite its small size.
• The model's suitability for real-world applications depends on its ability to integrate with other technologies, such as speech-to-text models and wake word detection.
• The model's limitations and potential biases need to be carefully evaluated before deploying it in real-world applications.