VikramR/cypherbench-grpo-4.3
VISIONConcurrency Cost:1Model Size:5.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 13, 2026Architecture:Transformer Cold
VikramR/cypherbench-grpo-4.3 is a 5.1 billion parameter language model fine-tuned from google/gemma-4-E2B-it. It was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is optimized for tasks requiring advanced mathematical problem-solving and logical deduction.
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Overview
VikramR/cypherbench-grpo-4.3 is a 5.1 billion parameter language model, fine-tuned from the google/gemma-4-E2B-it base model. The fine-tuning process utilized the TRL library and incorporated the GRPO (Gradient-based Reward Policy Optimization) method.
Key Capabilities
- Enhanced Mathematical Reasoning: The model's training with GRPO, a method introduced in the DeepSeekMath paper, suggests a focus on improving mathematical problem-solving and logical deduction.
- Instruction Following: As a fine-tuned instruction model, it is designed to respond effectively to user prompts and questions.
Training Details
The model was trained using specific versions of key frameworks:
- TRL: 1.6.0
- Transformers: 5.12.0
- Pytorch: 2.10.0+cu129
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Good For
- Applications requiring strong mathematical reasoning.
- Tasks involving complex logical problem-solving.
- General instruction-following scenarios where a robust understanding of numerical and logical relationships is beneficial.