Fine-tuned Gemma-4-31B specifically for Copywriting & Creative Writing Tasks (Scored +290 Elo over base using EqBench3)

Hey r/LocalLLaMA,

Wanted to share a narrow fine-tune I've been working on and get some technical feedback from people who've done similar domain-specific work if possible.

The problem: general chat models can write marketing copy, but they default to the same tells hedging, "In today's fast-paced world…" openers, vague benefit-speak instead of specifics. Claude is good no dobt about it but I wanted to do something of my own too. I fine-tuned Gemma-4-31B-it specifically to cut that out and write more like a direct-response copywriter: lead with the pain, get concrete, tight CTAs. Model did gained more emotional intelligance over all.

Eval setup: built a copywriting-specific benchmark on top of the EQ-Bench 3 methodology (pairwise Elo + rubric), using 30 real-world briefs across Facebook ads, cold email, landing pages, product descriptions, SMS, scripts, etc. Base model and fine-tune answered every brief, judged blind by DeepSeek V4 Flash in both orderings (A-vs-B and B-vs-A) to control for position bias. Same base weights, same decoding settings, fine-tune is the only variable.

Results:

Biggest, most consistent gains were in hook strength, specificity, and concision, exactly where direct-response copy lives.

Training details: QLoRA SFT on a curated corpus of marketing briefs paired with completions, including real-world ad examples. Final weights are merged to full bf16 (not shipping an adapter). 256K context, drops into vLLM or Transformers as-is.

It needs enable_thinking=false for best results, turning on Gemma 4's reasoning mode actually hurts output quality here so keep that in mind please.

Model card + weights: https://huggingface.co/akwin123/copywriter-gemma4-31b
Quantizations:
https://huggingface.co/models?other=base_model:quantized:akwin123/copywriter-gemma4-31b

Please let me know how it performs too. Thanks!