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README.md
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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license: apache-2.0
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language:
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- en
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---
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- **Developed by:** RinggAI
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2.5
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license: apache-2.0
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language:
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- en
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- hi
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---
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As calling operations scale, it becomes clear that dialing and talking is not enough.
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Even with a strong voice AI + telephony architecture, the real value shows up only when post-call actions are captured and executed in a robust, dependable and consistent way. Closing the loop matters more than just connecting the call.
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To support that, we’re releasing our Hindi + English transcript analytics model tuned specifically for call transcripts:
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🔗 Model: https://huggingface.co/RinggAI/Qwen2.5-1.5B-Instruct-Transcript-Analytics
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You can plug it into your calling or voice AI stack to automatically extract:
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• Enum-based classifications (e.g., call outcome, intent, disposition)
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• Conversation summaries
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• Action items / follow-ups
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It’s built to handle real-world Hindi, English, and mixed Hinglish calls, including noisy transcripts.
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- **Developed by:** RinggAI
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
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[<img style="border-radius: 20px;" src="https://storage.googleapis.com/desivocal-prod/desi-vocal/logo.png" width="200"/>](https://ringg.ai)
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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