Upload TriLM_3.9B_Unpacked_quant_IQ2_TN.gguf
Browse filesAdds the `IQ2_TN`quantized version of the ~4B TriLM model. This quantization has been done on Metal (i.e., the GPU on the M3 Pro silicon) via [`ik_llama.cpp`](https://github.com/ikawrakow/ik_llama.cpp).
[This PR](https://github.com/ikawrakow/ik_llama.cpp/pull/13) adds the `IQ2_TN` format with support for CUDA, AVX, and even Metal (thanks [ikawrakow](https://github.com/ikawrakow) 🙏). Thus, I decided to test quantization and inference for the 3.9B TriLM variant. It seems to work quite nice on the GPU.
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TriLM_3.9B_Unpacked_quant_TQ2_0.gguf filter=lfs diff=lfs merge=lfs -text
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TriLM_3.9B_Unpacked_quant_TQ2_0.gguf filter=lfs diff=lfs merge=lfs -text
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TriLM_3.9B_Unpacked_quant_IQ2_TN.gguf filter=lfs diff=lfs merge=lfs -text
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TriLM_3.9B_Unpacked_quant_IQ2_TN.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab9b72b7d05352679efe430929b6d4e3d04ff8c4574d6851eed3496e4fab94c6
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size 1166745280
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