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README.md
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---
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quantized_by: ubergarm
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pipeline_tag: text-generation
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base_model: tngtech/DeepSeek-R1T-Chimera
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license: mit
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base_model_relation: quantized
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tags:
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- imatrix
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- mla
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- deepseek_v3
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- conversational
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- ik_llama.cpp
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---
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## `ik_llama.cpp` imatrix Quantizations of tngtech/DeepSeek-R1T-Chimera
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This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants. Do **not** download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
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These quants provide best in class quality for the given memory footprint.
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## Big Thanks
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Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!!
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Also thanks to all the folks in the quanting and inferencing community here and on `r/LocalLLaMA` for tips and tricks helping each other run all the fun new models!
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Excited to share and learn together. Thanks!
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## Quant Collection
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So far these are my best recipes offering the great quality in good memory footprint breakpoints.
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#### ubergarm/DeepSeek-R1T-Chimera-IQ4_KS
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TODO
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## Quantization
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<details>
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<summary>👈Secret Recipe</summary>
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```bash
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TODO
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(mostly iq6_k for all attention/shared experts and iq4_ks for all ffn layers)
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```
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</details>
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## Discussion
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Updated imatrix MLA stuff.
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## References
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* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/)
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* [ik_llama.cpp Getting Started Guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
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* [ik_llama.cpp Benchmarks Discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/357)
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* [imatrix calibration_data_v5_rc.txt](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c#file-calibration_data_v5_rc-txt)
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