varshak1/open_reward_agent_sft_lf
varshak1/open_reward_agent_sft_lf is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. This model is specifically adapted using the open_reward_agent_sft_lf dataset, indicating a specialization in reward modeling or agent-based tasks. Its fine-tuning suggests enhanced performance for applications requiring nuanced understanding of rewards or agent behaviors.
Loading preview...
Model Overview
varshak1/open_reward_agent_sft_lf is an 8 billion parameter model, fine-tuned from the Qwen/Qwen3-8B base architecture. This model has undergone supervised fine-tuning (SFT) on the open_reward_agent_sft_lf dataset, suggesting a specialization in tasks related to reward mechanisms or agent learning within open environments.
Key Training Details
The fine-tuning process utilized the following hyperparameters:
- Learning Rate: 8e-06
- Batch Size: 1 (train), 8 (eval) with 8 gradient accumulation steps, resulting in a total effective batch size of 64.
- Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler with 0.05 warmup steps.
- Epochs: 1.0
- Frameworks: Transformers 5.2.0, Pytorch 2.6.0+cu124, Datasets 4.0.0, Tokenizers 0.22.2.
Intended Use Cases
Given its fine-tuning on a dataset related to reward agents, this model is likely suitable for applications involving:
- Reward Modeling: Understanding and predicting reward signals in complex environments.
- Agent Behavior Analysis: Simulating or interpreting the actions and policies of AI agents.
- Reinforcement Learning: Potentially serving as a component in systems that require a nuanced understanding of reward functions for agent training or evaluation.