Kazuki1450/Qwen2.5-1.5B-Instruct_csum_6_10_tok_Since_1p0_0p0_1p0_grpo_42_rule
Kazuki1450/Qwen2.5-1.5B-Instruct_csum_6_10_tok_Since_1p0_0p0_1p0_grpo_42_rule is a 1.5 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model utilizes the GRPO (Gradient-based Reward Policy Optimization) method, as introduced in the DeepSeekMath paper, to enhance its capabilities. With a substantial context length of 131072 tokens, it is designed for general text generation tasks, potentially excelling in areas benefiting from advanced mathematical reasoning or structured optimization techniques.
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Overview
This model, Kazuki1450/Qwen2.5-1.5B-Instruct_csum_6_10_tok_Since_1p0_0p0_1p0_grpo_42_rule, is a fine-tuned variant of the Qwen2.5-1.5B-Instruct base model. It has been specifically trained using the GRPO (Gradient-based Reward Policy Optimization) method, a technique highlighted in the research behind DeepSeekMath. This training approach aims to improve the model's performance, particularly in areas that benefit from advanced reasoning and optimization.
Key Characteristics
- Base Model: Qwen/Qwen2.5-1.5B-Instruct.
- Parameter Count: 1.5 billion parameters.
- Context Length: Features a notable context window of 131072 tokens.
- Training Method: Fine-tuned with GRPO, as detailed in the DeepSeekMath paper.
- Frameworks: Developed using TRL (Transformer Reinforcement Learning) version 0.23.0, along with Transformers 4.57.1 and Pytorch 2.7.1+cu128.
Potential Use Cases
Given its instruction-tuned nature and GRPO-enhanced training, this model is suitable for:
- General text generation and conversational AI.
- Tasks requiring improved reasoning capabilities, potentially in mathematical or logical domains, due to its GRPO training.
- Applications benefiting from a large context window for processing extensive inputs or generating detailed outputs.