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workspace/ComfyUI/models/Qwen3gguf/quenresponse.txt
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{
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"id": "cmpl-bf9b138c-db12-4832-86ee-f69f2103ed42",
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"object": "text_completion",
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"created": 1762011900,
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"model": "/workspace/ComfyUI/models/Qwen3gguf/Qwen3-30B-A6B-16-Extreme.Q8_0.gguf",
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"choices": [
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{
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"text": "<think>\nOkay, I need to summarize this model card for PaddleOCR-VL. The user wants 3-5 sentences, focusing on what the model does, key features, special capabilities/limitations, and typical use cases. Also, start with a greeting like \"hey guys, a new model has released...\" and end with a subscription note.\n\nFirst, I'll read through the model card. The main points are that it's a multilingual document parser using a 0.9B vision-language model. It's compact but powerful, supports 109 languages, and handles complex elements like text, tables, formulas, charts. It's efficient and fast, with SOTA performance on benchmarks.\n\nKey features: the architecture combines a visual encoder and language model, multilingual support, efficient resource use. Capabilities include recognizing complex elements and handling diverse document types. Limitations might be that it's optimized for specific tasks, maybe not as flexible for other uses. Use cases are document parsing in real-world scenarios, multilingual environments.\n\nNeed to avoid technical details. Start with the greeting, mention PaddlePaddle, then the model's purpose, features, capabilities, use cases, and end with the subscription. Make sure it's concise.\n</think>\n\nhey guys, a new model has released, you should know about it: PaddleOCR-VL, developed by PaddlePaddle. This ultra-compact 0.9B vision-language model excels in multilingual document parsing, recognizing text, tables, formulas, and charts across 109 languages with exceptional efficiency and speed. Its lightweight architecture and SOTA performance make it ideal for real-world applications like automated document analysis and multilingual content processing. please subscribe to our channel to get all latest AI model update, stay tuned.",
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"index": 0,
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"logprobs": null,
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": 9047,
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"completion_tokens": 363,
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"total_tokens": 9410
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}
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}
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