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README.md
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language:
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- en
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-0.5B
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tags:
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- chat
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library_name:
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---
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# Qwen2.5-0.5B-Instruct
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## Introduction
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language:
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- en
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- chat
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library_name: Exllamav2
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---
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# Qwen2.5-0.5B-Instruct-exl2
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Model: [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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Creator: [Qwen](https://huggingface.co/Qwen)
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## Quants
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[4bpw h6 (main)](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/main)
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[4.5bpw h6](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/4.5bpw-h6)
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[5bpw h6](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/5bpw-h6)
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[5.5bpw h6](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/5.5bpw-h6)
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[6bpw h6](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/6bpw-h6)
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[6.5bpw h6](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/6.5bpw-h6)
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[8bpw h8](https://huggingface.co/cgus/Qwen2.5-0.5B-Instruct-exl2/tree/8bpw-h8)
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## Quantization notes
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Made with Exllamav2 0.2.4 with the default dataset.
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This model is meant to be used with RTX2000 or newer cards on Windows. On Linux it's possible to use ROCm AMD cards as well.
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These quants might be useful as a draft model for TabbyAPI. For other purposes imho it's better to use bigger models.
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# Original model card
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# Qwen2.5-0.5B-Instruct
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## Introduction
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