The rohan2810/NEW_OURS_SFT_hotpotqa_Qwen3-4B-Instruct is a 4 billion parameter instruction-tuned causal language model based on the Qwen architecture, developed by rohan2810. This model is fine-tuned for specific tasks, indicated by 'SFT' (Supervised Fine-Tuning) and 'hotpotqa', suggesting an optimization for question answering, particularly on complex, multi-hop questions. With a context length of 32768 tokens, it is designed for applications requiring detailed understanding and generation of responses based on extensive input.
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Model Overview
The rohan2810/NEW_OURS_SFT_hotpotqa_Qwen3-4B-Instruct is a 4 billion parameter language model built upon the Qwen architecture. It has undergone Supervised Fine-Tuning (SFT) specifically for the HotpotQA dataset, indicating a strong specialization in question answering tasks that often require multi-hop reasoning.
Key Characteristics
- Architecture: Based on the Qwen model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling the processing and generation of long and complex texts.
- Fine-tuning: Explicitly fine-tuned for HotpotQA, suggesting enhanced capabilities in extracting and synthesizing information to answer intricate questions.
Potential Use Cases
This model is likely well-suited for applications requiring:
- Advanced Question Answering: Particularly for questions that demand reasoning over multiple pieces of information.
- Information Extraction: Identifying and retrieving specific details from large documents.
- Contextual Understanding: Leveraging its large context window to comprehend and respond to detailed prompts.
Due to the limited information in the provided model card, specific benchmarks or further training details are not available. Users should be aware that the model's performance is optimized for its fine-tuning objective.