Image-Text-to-Text
Transformers
Safetensors
qwen3_5
text-generation
vision
multimodal
image-to-text
conversational
Instructions to use Jakelolipopp/Qwen3.5-9B-AltText-v4-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Jakelolipopp/Qwen3.5-9B-AltText-v4-merged") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Jakelolipopp/Qwen3.5-9B-AltText-v4-merged") model = AutoModelForMultimodalLM.from_pretrained("Jakelolipopp/Qwen3.5-9B-AltText-v4-merged", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-merged
- SGLang
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-merged with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jakelolipopp/Qwen3.5-9B-AltText-v4-merged", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-merged with Docker Model Runner:
docker model run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-merged
Qwen3.5-9B-AltText-v4 (Merged Weights)
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.
Model Details
- Base Model:
unsloth/Qwen3.5-9B - LoRA Fine-Tune:
Jakelolipopp/Qwen3.5-9B-AltText-v4-LORA - Data Type:
bfloat16 - Format: Hugging Face SafeTensors
Usage with Transformers
import 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])
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