MEGHT/qwen3-finetuned-search

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Sep 14, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

MEGHT/qwen3-finetuned-search is a 0.8 billion parameter model, fine-tuned from Qwen3 0.6B, specifically designed for generating relevant search queries. It excels at creating query suggestions based on user inputs and conversational context, making it ideal for enhancing search engines, chatbots, and content discovery systems. The model has a context length of 32768 tokens and was trained on a custom dataset of input-output pairs for search query generation.

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Model Overview

MEGHT/qwen3-finetuned-search is a specialized 0.8 billion parameter language model, fine-tuned from the Qwen3 0.6B base model. Its primary function is to generate relevant search queries from user inputs and conversational context, making it a powerful tool for improving search and conversational AI applications.

Key Capabilities

  • Search Query Generation: Generates a list of pertinent search queries based on current user input and historical conversation.
  • Contextual Understanding: Utilizes previous conversational turns to inform query suggestions, enhancing relevance.
  • Integration: Compatible with Hugging Face's transformers library for straightforward deployment.

Training and Performance

The model was fine-tuned on a custom dataset of input-output pairs, specifically tailored for search query generation tasks. Evaluation metrics include a perplexity of 12.5, a BLEU score of 0.35, and a ROUGE-L score of 0.45, indicating its ability to produce coherent and relevant queries.

Good For

  • Search Engine Query Suggestions: Providing more accurate and context-aware suggestions to users.
  • Chatbots and Virtual Assistants: Enabling conversational agents to suggest relevant searches based on user dialogue.
  • Content Discovery Systems: Improving content recommendation by generating effective search queries.

Limitations

  • Context Length: Limited to a maximum of 1024 tokens, which may truncate longer conversations.
  • Domain Specificity: Performance may vary in domains not represented in its training data.
  • Bias: May inherit biases present in its fine-tuning dataset.

Finetunes
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