Overview

This model, DCAgent/a1-agenttuning_mind2web, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone fine-tuning on a specialized dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--neulab-agenttuning-mind2web-sandboxes_glm_4.7_traces_jupiter/snapshots/18a00618fba76dd32bdea57571d69b0a5ee386ad_thinking_preprocessed, which suggests a focus on agentic capabilities, particularly within web environments.

Key Capabilities

• Agentic Task Optimization: Fine-tuned on a dataset related to 'agenttuning' and 'mind2web', indicating a specialization in tasks requiring autonomous interaction with web interfaces or complex multi-step processes.
• Foundation Model: Built upon the robust Qwen3-8B base, inheriting its general language understanding and generation capabilities.

Training Details

The model was trained with the following hyperparameters:

• Learning Rate: 4e-05
• Batch Size: 1 (train), 8 (eval) with a total distributed batch size of 16 (train) and 128 (eval) across 16 GPUs.
• Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08.
• Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
• Epochs: 7.0

Good For

• Developing and deploying AI agents for web automation.
• Tasks requiring understanding and execution within web-based sandboxes.
• Research into agentic AI and fine-tuning large language models for specific interactive environments.