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nisha1990kum/Qwen3.5-Hasutsubomi-9B
Qwen3.5-Hasutsubomi-9B is a image-text-to-text model from nisha1990kum. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Qwen3.5-Hasutsubomi-9B is a vision-language fine-tuned large language model based on the Qwen3.5-9B architecture. It is specifically tailored to act as an expert in Japanese creative writing and little sub-culture kno…
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From the Hugging Face model README
Qwen3.5-Hasutsubomi-9B is a vision-language fine-tuned large language model based on the Qwen3.5-9B architecture. It is specifically tailored to act as an expert in Japanese creative writing and little sub-culture knowledge (including anime and manga).
Note: This model is currently in a pre-alpha state. It is still undergoing training and experimentation.
Hasutsubomi (蓮蕾) translates to "Lotus Bud" in Japanese. This name reflects the model's current pre-alpha status—a beautiful flower that is still growing and gathering knowledge. Once the model completes its training cycle and reaches maturity, it is planned to evolve into Hanahiraki (Blooming).
Because Qwen3.5 models operate in a "thinking mode" by default, Hasutsubomi will often draft its creative thoughts or plot outlines before writing the final response.
Below is a guide to using the model via the OpenAI-compatible API format (assuming you are serving the model via vLLM, SGLang, or standard Transformers).
from openai import OpenAI
# Configured by environment variables
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
messages =[
{"role": "system", "content": "You are a professional Japanese light novel author."},
{"role": "user", "content": "Write a short prologue for a fantasy Isekai story where the protagonist is reincarnated not as a hero, but as a magical library. Use an atmospheric and slightly melancholic tone in Japanese."}
]
chat_response = client.chat.completions.create(
model="Local-Novel-LLM-project/Qwen3.5-Hasutsubomi-9B",
messages=messages,
max_tokens=4096,
temperature=0.8, # Slightly higher temperature for creative writing
top_p=0.9,
presence_penalty=1.5,
)
print("Chat response:", chat_response.choices[0].message.content)
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
messages = [
{
"role": "user",
"content":[
{
"type": "image_url",
"image_url": {
"url": "https://example.com/anime_character_design.jpg"
}
},
{
"type": "text",
"text": "Analyze this character's design. Based on typical Japanese sub-culture tropes, what kind of personality (e.g., Tsundere, Yandere, Chuunibyou) do they likely have, and what kind of magic would they use in a fantasy setting?"
}
]
}
]
chat_response = client.chat.completions.create(
model="Local-Novel-LLM-project/Qwen3.5-Hasutsubomi-9B",
messages=messages,
max_tokens=2048,
temperature=0.7,
)
print("Chat response:", chat_response.choices[0].message.content)
temperature=0.8 to 1.0 with a top_p=0.9.presence_penalty=1.5 to encourage varied vocabulary.<think>\n...\</think> tags to plan out story beats before it generates the final text. This significantly improves narrative coherence.If you use this fine-tuned model, please consider acknowledging this repository. Additionally, please cite the original Qwen3.5 paper:
@misc{qwen3.5,
title = {{Qwen3.5}: Towards Native Multimodal Agents},
author = {{Qwen Team}},
month = {February},
year = {2026},
url = {https://qwen.ai/blog?id=qwen3.5}
}