BillyWang1/qwen2.5-3b-base-tool-n1-grpo
BillyWang1/qwen2.5-3b-base-tool-n1-grpo is a 3.1 billion parameter Qwen2.5-based language model, specifically a GRPO-trained checkpoint derived from BillyWang1/qwen2.5-3b-base-tool-n1-sft. This model is designed and optimized for Tool-N1-style function and tool calling experiments. It serves as an actor checkpoint from a GRPO training process, focusing on enabling robust tool interaction capabilities.
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Overview
BillyWang1/qwen2.5-3b-base-tool-n1-grpo is a 3.1 billion parameter model based on the Qwen2.5 architecture. It represents a specific checkpoint from a GRPO (Generalized Reinforcement Learning with Policy Optimization) training run, building upon the BillyWang1/qwen2.5-3b-base-tool-n1-sft base model. This model was trained using a seed of 54321 and exported after 280 steps, with its FSDP shards merged into Transformers safetensors format.
Key Capabilities
- Tool-N1-style Function Calling: The model is explicitly designed and trained for experiments involving Tool-N1-style function and tool calling. This indicates its proficiency in understanding and generating responses that involve external tool interactions.
- GRPO Training: Its development involved GRPO, suggesting an optimization for policy learning in complex environments, which is beneficial for reliable tool use.
Good For
- Tool Calling Research: Ideal for researchers and developers working on advanced tool-calling mechanisms and evaluating different reinforcement learning approaches for function invocation.
- Experimental Prototyping: Suitable for prototyping and testing applications that require a language model to interact with external APIs or tools in a structured manner.