lugman-madhiai/Qwen3.5-2B-SearchAgent-SFT-01
The lugman-madhiai/Qwen3.5-2B-SearchAgent-SFT-01 is a 2.3 billion parameter Qwen3.5-based causal language model, fine-tuned by lugman-madhiai. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for search agent applications, leveraging its fine-tuned capabilities within a 32768 token context length.
Loading preview...
Model Overview
The lugman-madhiai/Qwen3.5-2B-SearchAgent-SFT-01 is a 2.3 billion parameter language model, fine-tuned from the Qwen/Qwen3.5-2B base model. Developed by lugman-madhiai, this model is specifically optimized for search agent functionalities.
Key Characteristics
- Base Model: Qwen/Qwen3.5-2B architecture.
- Parameter Count: 2.3 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x speedup during training.
- License: Distributed under the Apache-2.0 license.
Use Cases
This model is particularly well-suited for applications requiring a specialized search agent. Its fine-tuned nature suggests enhanced performance in tasks related to information retrieval, query understanding, and agent-based interactions within a search context. The efficient training process indicates a focus on practical deployment and performance.