Downloads · 30 days
11
9% of all-time downloads
vorenthiclabs/vorenthos-vision-thinking-qwq-7b
vorenthos-vision-thinking-qwq-7b is a image-text-to-text model from vorenthiclabs. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit with Unsloth + QLoRA.
Downloads · 30 days
11
9% of all-time downloads
All-time downloads
127
Public
Parameters
8.3B
16.6 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors16.6 GB · 100%
From the Hugging Face model README
Fine-tuned unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit with Unsloth + QLoRA.
QwQ-VL is Qwen's dedicated reasoning vision-language model.
It produces <think>…</think> chain-of-thought blocks before
giving its final answer — ideal for complex visual reasoning tasks.
| Setting | Value |
|---|---|
| Base model | unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit |
| Method | QLoRA (r=16, alpha=16) |
| Precision | float16 (merged) |
| Max seq length | 2048 |
| Steps | 60 |
| Date | 2026-07-26 |
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
model_id = "vorenthiclabs/vorenthos-vision-thinking-qwq-7b"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.float16, device_map="auto"
)
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "https://your-image-url.jpg"},
{"type": "text", "text": "Describe what you see and reason step by step."}
]
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[...], return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))