LlameUser/qwen-3-4b-thinking-r1-st
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kArchitecture:Transformer0.0K Warm
LlameUser/qwen-3-4b-thinking-r1-st is a fine-tuned language model based on Qwen/Qwen3-4B-Thinking-2507, developed by LlameUser. This model was trained using the TRL framework and incorporates the GRPO method, which is designed to enhance mathematical reasoning capabilities. It is optimized for tasks requiring advanced logical and mathematical problem-solving, building upon the Qwen3-4B architecture.
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Model Overview
LlameUser/qwen-3-4b-thinking-r1-st is a specialized language model derived from the Qwen/Qwen3-4B-Thinking-2507 base model. It has been fine-tuned using the TRL library to improve its performance in specific domains.
Key Capabilities
- Enhanced Mathematical Reasoning: This model's training procedure specifically incorporates the GRPO (Gradient-based Reasoning Policy Optimization) method. GRPO, introduced in the paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300), is designed to significantly boost a model's ability to handle complex mathematical problems and logical thinking tasks.
- Instruction Following: As a fine-tuned model, it is expected to follow user instructions effectively, particularly in contexts related to its specialized training.
Good For
- Mathematical Problem Solving: Ideal for applications requiring robust mathematical reasoning, such as solving equations, logical puzzles, or generating step-by-step mathematical solutions.
- Reasoning-intensive Tasks: Suitable for use cases where logical deduction and structured thinking are paramount.
- Research and Development: Provides a strong base for further experimentation and fine-tuning on specific mathematical or reasoning datasets.