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@@ -20,6 +20,21 @@ base_model: nvidia/AceInstruct-7B
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  This model was converted to GGUF format from [`nvidia/AceInstruct-7B`](https://huggingface.co/nvidia/AceInstruct-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/nvidia/AceInstruct-7B) for more details on the model.
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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  This model was converted to GGUF format from [`nvidia/AceInstruct-7B`](https://huggingface.co/nvidia/AceInstruct-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/nvidia/AceInstruct-7B) for more details on the model.
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+ ---
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+ We introduce AceInstruct, a family of advanced SFT models for coding,
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+ mathematics, and general-purpose tasks. The AceInstruct family, which
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+ includes AceInstruct-1.5B, 7B, and 72B, is Improved using Qwen.
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+ These models are fine-tuned on Qwen2.5-Base using general SFT datasets. These same datasets are also used in the training of AceMath-Instruct.
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+ Different from AceMath-Instruct which is specialized for math
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+ questions, AceInstruct is versatile and can be applied to a wide range
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+ of domains. Benchmark evaluations across coding, mathematics, and
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+ general knowledge tasks demonstrate that AceInstruct delivers
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+ performance comparable to Qwen2.5-Instruct.
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+
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+
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+ For more information about AceInstruct, check our website and paper.
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+
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+ ---
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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