resistz/TTCL-AMC23-RLCR-Qwen3-4B-FactQA10K
resistz/TTCL-AMC23-RLCR-Qwen3-4B-FactQA10K is a 4 billion parameter model developed by resistz, fine-tuned using Test-time Calibration Learning (TTCL) on the AMC23 benchmark. This model is based on RLCR-Qwen3-4B-FactQA10K and is specifically optimized for factual question answering tasks. Its primary use case is to enhance performance on specific benchmarks through test-time calibration.
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Overview
resistz/TTCL-AMC23-RLCR-Qwen3-4B-FactQA10K is a 4 billion parameter language model that has undergone Test-time Calibration Learning (TTCL). This specialized fine-tuning process was applied using the AMC23 benchmark, building upon the base model RLCR-Qwen3-4B-FactQA10K. The model is designed to improve performance on specific factual question answering tasks through its unique calibration approach.
Key Capabilities
- Enhanced Factual Accuracy: Optimized for factual question answering through TTCL.
- Benchmark Performance: Specifically calibrated on the AMC23 benchmark, suggesting improved performance on similar datasets.
- Efficient Size: At 4 billion parameters, it offers a balance between performance and computational efficiency.
Good for
- Researchers and developers focusing on factual question answering where test-time calibration can yield performance gains.
- Applications requiring a model with a specific optimization for the AMC23 benchmark or similar factual recall tasks.
- Scenarios where a moderately sized model (4B parameters) is preferred for deployment efficiency while maintaining specialized performance.