hard007ik/shopmanager-grpo-smoke-l4-v2
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Apr 26, 2026Architecture:Transformer Cold
The hard007ik/shopmanager-grpo-smoke-l4-v2 is a 0.8 billion parameter language model fine-tuned from Qwen/Qwen3-0.6B, featuring a 32768-token context length. It was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is optimized for tasks requiring improved logical and mathematical processing, building upon its Qwen3 base.
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Model Overview
The hard007ik/shopmanager-grpo-smoke-l4-v2 is a 0.8 billion parameter language model, fine-tuned from the Qwen/Qwen3-0.6B base model. It leverages a substantial context window of 32768 tokens, making it suitable for processing longer inputs and generating more extensive outputs.
Key Capabilities
- Enhanced Reasoning: This model was specifically trained using the GRPO (Gradient-based Reasoning Policy Optimization) method. GRPO, introduced in the DeepSeekMath paper, focuses on improving mathematical reasoning in language models.
- Qwen3 Foundation: Built upon the Qwen3-0.6B architecture, it inherits the general language understanding and generation capabilities of its base model.
- TRL Framework: The fine-tuning process utilized the TRL (Transformers Reinforcement Learning) library, indicating a reinforcement learning approach to optimize its performance.
Good For
- Mathematical and Logical Tasks: Due to its GRPO training, this model is particularly well-suited for applications that require robust mathematical reasoning and problem-solving.
- General Text Generation: It can be used for a variety of text generation tasks, benefiting from the Qwen3 base model's capabilities.
- Research and Experimentation: Developers interested in exploring the effects of GRPO fine-tuning on smaller Qwen3 models for specific reasoning tasks may find this model valuable.