Qwen3.5-0.8B-Picture-Captioner-v1.2
A lightweight vision-language model (VLM) fine-tuned to generate concise, detailed, and accurate image captions.
[!NOTE] This version is retrained exclusively on a high-resolution subset of the dataset. While output quality is similar to the first version, it features significantly improved details—particularly regarding backgrounds and OCR accuracy.
Model Details
- Base Model:
Huihui-Qwen3.5-0.8B-abliterated(Uncensored) - Training Dataset:
agentlans/monet-caption-highres - Precision:
bf16 - Attention Mechanism:
sdpa(Scaled Dot-Product Attention) - Max Pixels: 768 × 768 (589,824 pixels)
Training Configuration
| Parameter | Value |
|---|---|
| Epochs | 1 |
| LoRA Rank | 16 (alpha = 32) |
| Dropout | 0.05 |
| Learning Rate | 2e-4 |
| Weight Decay | 0.01 |
| Warmup Steps | 100 |
Training Results
- Training Loss (Smoothed): 2.7191
- Validation Loss: 2.8538
Usage & Input Format
To format your inputs correctly for inference, structure your prompt as shown below. For optimal RAM efficiency, it is recommended to resize your images proportionally to 768 × 768 prior to inference.
content = [
{"type": "image", "image": image},
{"type": "text", "text": "Describe this image in detail."}
]
# Recommended generation settings:
# tokenize = False
# add_generation_prompt = False
👉 Click here to view example images and compare captions against the original model version.
Limitations
While efficient and capable, this model shares common failure modes typical of smaller Vision-Language Models (VLMs):
- Object and Action Misidentification: May occasionally hallucinate or mislabel specific objects (e.g., confusing a red ball with a tomato) or misinterpret complex interactions between subjects and objects.
- Scope & General Use: Not recommended for document scanning, statistical analysis, or medical imaging. It may also underperform on niche genres such as art criticism, provenance analysis, or anime.
- Counting & Descriptions: Counts can occasionally be off by 1 and natural language descriptions can sometimes be ambiguous.
- Repetitive Captions: Despite variety in the training data, the model can occasionally fall into repetitive phrasing loops.
- Inference Speed: As with most VLMs, generation speeds are inherently slower than traditional text-only models or specialized tagging methods.
Licence
This model is licensed under the Apache 2.0 License.
See Also
The original version of the model at agentlans/Qwen3.5-0.8B-Picture-Captioner.