 | Settings: temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 Prompts: - Create svg image of a pelican riding a bicycle - Create svg image of a capybara wearing a kimono drinking matcha tea - Create svg image of a flamingo knitting a colorful sweater - Create svg image of a sushi roll wearing sunglasses driving a go-kart - Create svg image of a Victorian-era robot reading a newspaper in a cafe - Create a svg image of a time-lapse composite showing a flower blooming, wilting, and transforming into butterflies across four seasons, all in one frame with seasonal lighting Stats: 3min 10s, 27.55 t/s 4min 35s, 27.05 t/s 3min 20s, 27.55 t/s 7min 2s, 27.27 t/s 7min 23s, 27.19 t/s 8min 24s, 27.13 t/s [link] [comments] |
Qwen3.6-27B-Q6_K - images
Reddit r/LocalLLaMA / 4/30/2026
💬 OpinionSignals & Early TrendsTools & Practical UsageModels & Research
Key Points
- The post shares image-generation results using a model labeled “Qwen3.6-27B-Q6_K,” including several SVG-image prompts featuring animals and stylized scenes.
- Generation settings are explicitly listed (e.g., temperature 0.6, top_p 0.95, top_k 20, min_p 0.0, no presence penalty, repetition_penalty 1.0), along with multiple example prompts.
- Reported performance statistics include several run times and token/s throughput figures, suggesting relatively consistent generation speed across trials.
- The examples emphasize creative, diverse subject matter (pelican on a bicycle, capybara in kimono drinking matcha, flamingo knitting, sushi roll driving a go-kart) as well as a multi-stage time-lapse composition across seasons.
- Overall, the content functions as a community showcase of how that specific Qwen variant performs for local SVG image generation under given sampling parameters.
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