InosLihka/rhythm-env-meta-trained-iter1
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Apr 25, 2026Architecture:Transformer Cold
The InosLihka/rhythm-env-meta-trained-iter1 is a 3.1 billion parameter instruction-tuned language model, fine-tuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. It was trained using the TRL framework and incorporates the GRPO method, a technique designed to enhance mathematical reasoning. This model is optimized for tasks requiring advanced mathematical problem-solving and complex reasoning, leveraging its 32768 token context length.
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Model Overview
InosLihka/rhythm-env-meta-trained-iter1 is a 3.1 billion parameter language model, fine-tuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. It leverages the TRL framework for its training process.
Key Capabilities
- Enhanced Mathematical Reasoning: This model was trained using the GRPO (Gradient-based Reasoning Policy Optimization) method, as introduced in the paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300). This training approach specifically targets and improves the model's ability to handle complex mathematical problems and logical reasoning tasks.
- Instruction Following: As a fine-tuned instruction model, it is designed to respond effectively to user prompts and instructions.
- Extended Context Window: The model supports a context length of 32768 tokens, allowing it to process and generate longer, more complex sequences of text.
Good For
- Mathematical Problem Solving: Ideal for applications requiring robust mathematical reasoning, such as solving equations, proofs, or complex quantitative analysis.
- Complex Reasoning Tasks: Suitable for scenarios where logical deduction and structured thinking are paramount.
- Research and Development: Provides a base for further experimentation and fine-tuning, particularly in areas related to advanced reasoning and instruction following.