lingefdafda/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ravenous_small_cod
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Apr 21, 2025Architecture:Transformer Featherless Exclusive Warm
The lingefdafda/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ravenous_small_cod is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Gensyn/Qwen2.5-0.5B-Instruct. It was trained using the TRL framework and incorporates the GRPO method, which is designed to enhance mathematical reasoning. With a context length of 32768 tokens, this model is optimized for tasks requiring robust mathematical reasoning capabilities.
Loading preview...
Overview
This model, lingefdafda/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ravenous_small_cod, is a fine-tuned variant of the Gensyn/Qwen2.5-0.5B-Instruct model, featuring 0.5 billion parameters and a 32768-token context window. It was developed using the TRL (Transformer Reinforcement Learning) framework.
Key Capabilities
- Enhanced Mathematical Reasoning: The model's training incorporates the GRPO (Gradient-based Reinforcement Learning with Policy Optimization) method, as introduced in the "DeepSeekMath" paper, specifically to improve mathematical reasoning abilities.
- Instruction Following: As an instruction-tuned model, it is designed to follow user prompts effectively for various tasks.
Good For
- Mathematical Problem Solving: Its specialized training with GRPO makes it suitable for applications requiring strong mathematical reasoning.
- Small-scale Instruction-tuned Applications: Given its 0.5 billion parameter size, it is well-suited for scenarios where a compact yet capable instruction-following model is needed, potentially for edge devices or applications with limited computational resources.
- Research and Experimentation: Developers interested in exploring the effects of GRPO on smaller language models can use this as a base.