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--- |
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license: apache-2.0 |
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base_model: |
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- prithivMLmods/Magpie-Qwen-CortexDual-0.6B |
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library_name: transformers |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- text-generation-inference |
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- math |
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- code |
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--- |
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# Magpie-Qwen-CortexDual-0.6B-GGUF |
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> **Magpie-Qwen-CortexDual-0.6B** is a specialized, general-purpose model designed for **math**, **code**, and **structured reasoning**. Built with **CortexDual thinking mode**, it dynamically adapts to the complexity of a problem, automatically shifting into a stepwise reasoning mode for intricate logic or math tasks. This 0.6B parameter model leverages **80% of the Magpie Pro 330k dataset** and a modular blend of datasets for general-purpose proficiency and domain versatility. |
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> |
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## ModelFile |
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| File Name | Size | Source | |
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|----------------------------------|-----------|--------| |
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| Magpie-Qwen-0.6B.BF16.gguf | 1.2 GB | xet | |
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| Magpie-Qwen-0.6B.F16.gguf | 1.2 GB | xet | |
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| Magpie-Qwen-0.6B.F32.gguf | 2.39 GB | xet | |
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| Magpie-Qwen-0.6B.Q4_K_M.gguf | 397 MB | xet | |
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| Magpie-Qwen-0.6B.Q5_K_M.gguf | 444 MB | xet | |
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| Magpie-Qwen-0.6B.Q8_0.gguf | 639 MB | xet | |
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| .gitattributes | 1.97 kB | - | |
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| README.md | 723 Bytes | - | |
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| config.json | 31 Bytes | - | |
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## Quants Usage |
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) |
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| Link | Type | Size/GB | Notes | |
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|:-----|:-----|--------:|:------| |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) | Q2_K | 0.4 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.7 | | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) | Q6_K | 0.7 | very good quality | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) | f16 | 1.6 | 16 bpw, overkill | |
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Here is a handy graph by ikawrakow comparing some lower-quality quant |
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types (lower is better): |
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