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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.
-
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 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)