fukugawa/qwen2.5-7b-agentbench-test
fukugawa/qwen2.5-7b-agentbench-test is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B-Instruct. This model has been specifically trained using SFT with TRL, indicating an optimization for agent-based tasks or specific instruction following. It leverages the Qwen2.5 architecture, making it suitable for applications requiring enhanced instruction adherence and performance in agentic workflows.
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Model Overview
This model, fukugawa/qwen2.5-7b-agentbench-test, is a fine-tuned variant of the Qwen/Qwen2.5-7B-Instruct base model. It features 7.6 billion parameters and has been developed by fukugawa.
Key Capabilities
- Instruction Following: The model is fine-tuned from an instruction-tuned base model, suggesting strong capabilities in understanding and executing given instructions.
- Agentic Workflows: The model's name,
agentbench-test, implies a focus or optimization for performance within agent-based applications, potentially involving complex task decomposition and execution. - Fine-tuning Method: It was trained using Supervised Fine-Tuning (SFT) with the TRL (Transformers Reinforcement Learning) library, indicating a structured approach to enhancing its performance on specific tasks.
Training Details
The fine-tuning process utilized the TRL library, a framework for training transformer models with reinforcement learning. The specific framework versions used include PEFT 0.18.1, TRL 0.28.0, Transformers 4.56.2, Pytorch 2.10.0+cu128, Datasets 4.0.0, and Tokenizers 0.22.2.
When to Use This Model
This model is particularly well-suited for use cases that require a robust instruction-following model within agentic systems or for tasks where the base Qwen2.5-7B-Instruct model's capabilities need to be further specialized through SFT.