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Jakelolipopp/Qwen3.5-9B-AltText-v4-merged
Qwen3.5-9B-AltText-v4-merged is a image-text-to-text model from Jakelolipopp. 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.
This repository contains the full, merged bfloat16 safetensors weights of Jakelolipopp/Qwen3.5-9B-AltText-v4-LORA applied to base model unsloth/Qwen3.5-9B.
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.safetensors18.8 GB · 100%
From the Hugging Face model README
This repository contains the full, merged bfloat16 safetensors weights of Jakelolipopp/Qwen3.5-9B-AltText-v4-LORA applied to base model unsloth/Qwen3.5-9B.
unsloth/Qwen3.5-9BJakelolipopp/Qwen3.5-9B-AltText-v4-LORAbfloat16import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
model_id = "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
image = Image.open("example.jpg")
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "Provide detailed alternative text describing this image:"}
]
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=256)
generated_ids = outputs[:, inputs.input_ids.shape[1]:]
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])