laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k
The laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k model is a fine-tuned 8 billion parameter language model based on Qwen/Qwen3-8B. It was specifically adapted using the penfever/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k dataset, suggesting an optimization for agent-based tasks within webshop sandbox environments. With a context length of 32,768 tokens, this model is designed for processing extensive conversational or transactional histories relevant to its specialized fine-tuning domain.
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Model Overview
This model, laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k, is a specialized large language model built upon the Qwen/Qwen3-8B architecture. It features 8 billion parameters and supports a substantial context window of 32,768 tokens, enabling it to process lengthy inputs and maintain conversational coherence over extended interactions.
Key Capabilities
- Specialized Fine-tuning: The model has been fine-tuned on the
penfever/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32kdataset. This indicates a focus on tasks related to agent behavior, particularly within webshop sandbox environments, suggesting proficiency in understanding and generating responses for simulated e-commerce interactions. - Extended Context: Its 32k token context length is beneficial for applications requiring the model to recall and utilize information from long dialogues or complex scenarios, such as multi-turn agent interactions or detailed product inquiries.
Training Details
The model underwent 7 epochs of training with a learning rate of 4e-05, utilizing a total batch size of 16 across 8 GPUs. The optimizer used was ADAMW_TORCH_FUSED with cosine learning rate scheduling and a 0.1 warmup ratio. These parameters suggest a robust training regimen aimed at optimizing performance within its target domain.