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

Overview

Qwen3.5-27B-Writer-exl3-5bpw-hb8 is an EXL3-quantized checkpoint for ExLlamaV3-compatible runtimes, published by groxaxo. It is intended for open-source evaluation, reproducible experimentation, and compatible local or hosted inference workflows. The wording below is deliberately limited to what can be verified from this repository's metadata and artifacts.

At a glance

What is included

• *.safetensors (3 files)
• config.json
• generation_config.json
• tokenizer.json
• tokenizer_config.json
• chat_template.jinja
• quantization_config.json
• Additional configuration, tokenizer, processor, or shard files (10 visible artifacts total)

Quick start

EXL3-compatible runtimes

Download the EXL3 files and load the desired bitrate with a current ExLlamaV3-compatible runtime. The correct loader and context settings depend on the model architecture and should be verified against the runtime's documentation.

Compatibility and responsible use

• Use a runtime that explicitly supports this format, architecture, and modality.
• Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
• Review the source model card and license before redistribution or deployment.
• Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
• Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.

Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.

Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.

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 <think>\n\n</think> 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.