Update README.md

README.md

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 This model was converted to GGUF format from [`ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small`](https://huggingface.co/ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small) for more details on the model.
 
+---
+RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series.
+
+
+RpR models use the same curated, deduplicated RP and creative writing
+ dataset used for RPMax, with a focus on variety to ensure high 
+creativity and minimize cross-context repetition. Users familiar with 
+RPMax will recognize the unique, non-repetitive writing style unlike 
+other finetuned-for-RP models.
+
+
+With the release of QwQ as the first high performing open-source 
+reasoning model that can be easily trained, it was clear that the 
+available instruct and creative writing reasoning datasets contains only
+ one response per example. This is type of single response dataset used 
+for training reasoning models causes degraded output quality in long 
+multi-turn chats. Which is why Arli AI decided to create a real RP model
+ capable of long multi-turn chat with reasoning.
+
+
+In order to create RpR, we first had to actually create the reasoning
+ RP dataset by re-processing our existing known-good RPMax dataset into a
+ reasoning dataset. This was possible by using the base QwQ Instruct 
+model itself to create the reasoning process for every turn in the RPMax
+ dataset conversation examples, which is then further refined in order 
+to make sure the reasoning is in-line with the actual response examples 
+from the dataset.
+
+
+Another important thing to get right is to make sure the model is 
+trained on examples that present reasoning blocks in the same way as it 
+encounters it during inference. Which is, never seeing the reasoning 
+blocks in it's context. In order to do this, the training run was 
+completed using axolotl with manual template-free segments dataset in 
+order to make sure that the model is never trained to see the reasoning 
+block in the context. Just like how the model will be used during 
+inference time.
+
+
+The result of training on this dataset with this method are 
+consistently coherent and interesting outputs even in long multi-turn RP
+ chats. This is as far as we know the first true correctly-trained 
+reasoning model trained for RP and creative writing.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)