--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers base_model: - ConicCat/Qwen3.5-27B-Writer base_model_relation: quantized --- ## Quantized using the default exllamav3 (0.0.25) quantization process. - Original model: https://huggingface.co/ConicCat/Qwen3.5-27B-Writer - exllamav3: https://github.com/turboderp-org/exllamav3 --- # ConicCat/Qwen3.5-27B-Writer A writing & roleplay finetune of Qwen3.5 27B. The primary emphasis is on writing quality as it strongly generalizes across both domains. This model is also trained from ConicCat/Qwen3.5-Antirep-27B to mitigate repetition issues. The basic idea is to use a curriculum learning setup to overcome the lack of high quality roleplay data by first training on lower quality roleplay data, then training on higher quality writing data. Starting from ConicCat/Qwen3.5-Antirep-27B, the model was trained on a roughly equal mixture of instruct / roleplay / writing data for three epochs. The model was then trained for eleven epochs on a smaller dataset of short story anthologies by critically acclaimed authors. ### Recommended Settings * Chatml template with `\n\n` or `{{char}}:` prefill. Only non-thinking was trained, but thinking probably still works. * temperature = `0.7` * top_p = `0.95` * I do not recommend using high rep pen values like Qwen suggests for the base model. rep_pen = `1.05` or a moderate dry setting should suffice. * For quants, Q4_K_M runs well with `~100k` context on 24GB Vram * IQ4_XS should fit on 16GB Vram with about `20-24k` context with the vulkan backend, although it's pretty tight and may require some fiddling around with open programs e.t.c. ### Datasets * ConicCat/AntiRep to mitigate repetitition. * internlm/Condor-SFT-20K for instruct; even though instruct capabilities are not the primary focus, adding some instruct data helps mitigate forgetting and maintains general intellect and instruction following capabilites. * PJMixers-Dev/C2-Logs-Sonnet-4.5-all for roleplay. Pretty much exactly what it says on the tin, the venerable C2 logs with the last turn regenerated by Sonnet 4.5 and refusals removed. * ConicCat/Gutenberg-SFT. A reformatted version of the original Gutenberg DPO dataset by jondurbin for SFT with some slight augmentation to address many of the samples being overly long. * A dataset of short story anthologies. Unfortunately, I am unable to release this set as all of the data is under copyright.