PixilabAI/Blink-v0.4-26B-A4B
PixilabAI/Blink-v0.4-26B-A4B is a 26 billion parameter decision model developed by Pixilab.ai and Nemini.ai, designed for rapid, calibrated decision-making within products. It processes a state, question, and options to output probabilities for each choice in a single forward pass, replacing complex LLM parsing for tasks like routing, moderation, and yes/no checks. This model achieves a Decision Index of 57.73, excelling particularly in retrieval and language tasks, and is calibrated to ensure its confidence aligns with its accuracy.
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Blink v0.4 · 26B-A4B · BF16: A Fast, Calibrated Decision Model
Blink v0.4-26B-A4B, developed by Pixilab.ai and Nemini.ai, is a specialized 26 billion parameter model engineered for rapid, calibrated decision-making. Unlike general-purpose LLMs, Blink is designed to take a state, a question, and a list of options, then output a probability for each option in a single forward pass, generating just one token. This makes it ideal for integrating directly into products to handle tasks like routing, moderation, tagging, gating, and binary checks, bypassing the need to parse verbose LLM outputs.
Key Capabilities
- High Decision Index: Achieves a Decision Index of 57.73 on the 0.2.1 suite, outperforming other open-weight models in its class.
- Enhanced Retrieval and Language: Shows significant improvements in Retrieval (64.2) and Language (64.6) scores compared to its predecessor, v0.3.
- Out-of-the-Box Calibration: At its recommended temperature (1.4), the model's confidence closely matches its accuracy, with a low calibration error of 0.024.
- Efficient for Product Integration: Replaces the need for complex LLM calls and parsing, streamlining decision processes within applications.
Good For
- Automated Routing and Moderation: Effectively routes queries or content and performs moderation tasks with high accuracy.
- Binary Decision Tasks: Ideal for yes/no checks, gating, and other scenarios requiring a clear, probabilistic decision.
- Replacing Complex LLM Parsing: Simplifies product logic by providing direct probabilistic outputs for multiple-choice scenarios.
- Fine-tuning and Quantization: The BF16 weights provided are suitable for further quantization or fine-tuning for specific use cases.