tsor13/spectrum-Qwen3-14B-v1
The tsor13/spectrum-Qwen3-14B-v1 is a 14 billion parameter language model based on the Qwen3 architecture, specifically designed for "Spectrum Tuning." This post-training technique optimizes the model for distributional coverage and in-context steerability, allowing it to accurately match and sample from specified output distributions. It excels at tasks requiring precise probability estimation and controlled generation based on descriptions and few-shot examples, making it suitable for complex reasoning and data generation scenarios.
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Spectrum-Qwen3-14B-v1: Distributional Coverage and In-Context Steerability
The tsor13/spectrum-Qwen3-14B-v1 is a 14 billion parameter model developed using Spectrum Tuning, a post-training method focused on achieving high distributional coverage and in-context steerability. This model is designed to accurately match and sample from specified output distributions, as detailed in the paper Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability.
Key Capabilities
- Distribution Matching: The model can learn and reproduce complex output distributions based on natural language descriptions and example outputs. It is recommended to sample with
temperature=1.0and no other generation hyperparameters for accurate distribution sampling. - In-Context Steerability: Users can steer the model's generation by providing descriptions, example inputs, and example outputs. The model expects messages with roles like
description,input, oroutput. - Precise Probability Estimation: It can calculate the precise probabilities for different continuations, useful for tasks like forced-selection or analyzing conditional probabilities.
- Few-Shot Learning: The model effectively learns from few-shot examples, adapting its output distribution. It can model populations zero-shot or individuals few-shot, often reflecting human response distributions.
- JSON Formatting: Optimized for structured data, the model works best with JSON formatting for inputs and outputs when dealing with multiple variables.
Good for
- Controlled Text Generation: Generating text that adheres to a specific style, format, or content distribution.
- Probabilistic Reasoning: Tasks requiring the model to estimate probabilities for various outcomes, such as predicting preferences or social reasoning.
- Data Augmentation: Creating synthetic data that matches the statistical properties of a given dataset.
- Interactive Systems: Building applications where precise control over model output and understanding of underlying probabilities are crucial.
Note: This model is not explicitly trained as a general-purpose chat model but rather for in-context distribution matching. It requires either a description or example outputs (or both) to reliably generate and condition output.