Update README.md

README.md

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 GGUFs here: [https://huggingface.co/mradermacher/Quill-v1-GGUF](https://huggingface.co/mradermacher/Quill-v1-GGUF)
 
-Quill is a sensible, capable writing model trained on a large dataset of late 19th and early 20th century writing from the Gutenberg Project. This model writes with a natural cadence and low gpt-slop, having inherited some human qualities from the Gutenberg3 dataset. It writes with more simple, spare prose than the typical overly-adjectived LLM writing style.
+Quill is a capable, humanlike writing model trained on a large dataset of late 19th and early 20th century writing from the Gutenberg Project. This model writes with a natural cadence and low gpt-slop, having inherited some human qualities from the Gutenberg3 dataset. It writes with more simple, spare prose than the typical overly-adjectived LLM writing style.
 
 This model was trained using gemma-2-9b-it as the base. The training methods used were ORPO (gently) then SIMPO (less gently).
 
 It scored 79.75 on the [EQ-Bench creative writing benchmark](https://eqbench.com/creative_writing.html).
 
+**Instruct Template:** Gemma
+
 [**Gutenberg3**](https://huggingface.co/datasets/sam-paech/gutenberg3-generalfiction-scifi-fantasy-romance-adventure-dpo) is a new, large dpo dataset containing extracts from 629 public domain fiction novels in the Gutenberg Library. It follows the same format as JonDurbin's original gutenberg set. It includes pairs of texts, where the chosen text is taken directly from a novel from the Gutenberg library, and the rejected text is generated by a language model based on a description of the passage. For this dataset I've used gemma-2-9b-it to generate the rejected texts, the idea being that it should more easily steer the base model away from its normal style (as compared to generating the rejected texts with random/weaker models).
 
 # Sample Outputs