laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jan 20, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It was trained on the open-athena/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k dataset, suggesting a specialization in agent tuning within webshop sandbox environments. With a context length of 32,768 tokens, this model is likely optimized for tasks requiring extensive contextual understanding in simulated e-commerce interactions.

Loading preview...

Model Overview

This model, laion/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned using the open-athena/Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k dataset, indicating a focus on agent-based interactions within webshop sandbox environments.

Training Details

The model underwent training with the following key hyperparameters:

  • Learning Rate: 4e-05
  • Batch Size: 1 (train), 8 (eval)
  • Gradient Accumulation Steps: 2, leading to a total train batch size of 16
  • Optimizer: AdamW_Torch_Fused with betas=(0.9, 0.98) and epsilon=1e-08
  • LR Scheduler: Cosine type with a warmup ratio of 0.1
  • Epochs: 7.0
  • Devices: Trained across 8 GPUs

Potential Use Cases

Given its fine-tuning dataset, this model is likely suited for:

  • Developing and evaluating AI agents in simulated e-commerce platforms.
  • Tasks involving complex interactions within webshop environments.
  • Research into agent tuning and behavior in sandbox settings.

Finetunes
1 models