cjiao/goldengoose-corr-v2-random-100
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Apr 25, 2026Architecture:Transformer Cold
The cjiao/goldengoose-corr-v2-random-100 is a 1.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. It was trained using the TRL framework and incorporates the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is optimized for generating coherent and contextually relevant text, particularly in response to complex prompts, and supports a context length of 32768 tokens.
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Model Overview
The cjiao/goldengoose-corr-v2-random-100 is a 1.5 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-1.5B-Instruct base model. It leverages the TRL (Transformer Reinforcement Learning) framework for its training process.
Key Capabilities
- Enhanced Reasoning: This model was trained using the GRPO (Guided Reinforcement Learning for Policy Optimization) method, as introduced in the DeepSeekMath paper, suggesting an optimization for improved reasoning, particularly in mathematical contexts.
- Instruction Following: As an instruction-tuned model, it is designed to understand and respond effectively to user prompts and instructions.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer, more complex texts while maintaining coherence.
Good For
- Mathematical Reasoning Tasks: The application of the GRPO method indicates potential strengths in tasks requiring logical and mathematical problem-solving.
- General Text Generation: Suitable for a wide range of text generation tasks where instruction following and coherent output are important.
- Research and Experimentation: Provides a fine-tuned model based on a robust foundation, ideal for further research into instruction tuning and reasoning enhancements.