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README.md

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 # GGUF and "i-matrix" quantized versions of MadeAgents/Hammer2.1-7b
 
-**Hammer** refers to a series of lightweight Large Action Models, with strong function calling capability. These models are based on the Qwen 2.5 coder series, and utilize function masking techniques and other advanced technologies.
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 Using [LLaMA C++](https://github.com/ggerganov/llama.cpp) release [b4601](https://github.com/ggerganov/llama.cpp/releases/tag/b4601) for quantization.
 
 Original model: [MadeAgents/Hammer2.1-7b](https://huggingface.co/MadeAgents/Hammer2.1-7b)
 
+From the model creators:
+
+> Hammer refers to a series of lightweight Large Action Models. Currently, we are releasing Hammer 2.1 models (0.5B, 1.5B, 3B, and 7B) with strong function calling capability. These models are based on the Qwen 2.5 coder series and utilize [function masking](https://arxiv.org/abs/2410.04587) techniques and other advanced technologies. Hammer 2.1 series bring significant enhancements, while still maintaining the basic functionality of Hammer 2.0's Single-Turn interaction and further strengthening other capabilities.
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 All quantized versions were generated using an appropriate imatrix created from datasets available at [eaddario/imatrix-calibration](https://huggingface.co/datasets/eaddario/imatrix-calibration).
 
 At its core, an Importance Matrix (imatrix) is a table or, more broadly, a structured representation that scores the relative importance of different features or parameters in a machine learning model. It essentially quantifies the "impact" each feature has on a specific outcome, prediction, or relationship being modeled.