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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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- # Uploaded finetuned model
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ It’s built to handle real-world Hindi, English, and mixed Hinglish calls, including noisy transcripts.
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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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+
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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)