Qwen3.5-0.8B-Picture-Captioner
A lightweight vision-language model fine-tuned to generate concise, detailed, and accurate image captions.
Model Details
- Base Model:
Huihui-Qwen3.5-0.8B-abliterated(uncensored) - Training Dataset:
agentlans/monet-caption - Precision:
bf16 - Attention Mechanism:
sdpa(Scaled Dot-Product Attention) - Max Pixels: 768 × 768 = 589,824
Training Configuration
| Parameter | Value |
|---|---|
| Epochs | 1 |
| LoRA Rank | 16 (α = 32) |
| Dropout | 0.05 |
| Learning Rate | 2e-4 |
| Weight Decay | 0.01 |
| Warmup Steps | 100 |
Training Results
- Training Loss (Smoothed): 2.1571
- Validation Loss: 2.2069
Input Format & Usage
- To format your inputs correctly for inference, structure your prompt as follows.
- To save RAM, also resize your images to 768 × 768 proportionally
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 see example images and captions.
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 misidentify specific objects (e.g., mislabelling a red ball as a tomato) or unusual actions (e.g., a person pushing a penguin into a shopping cart).
- Repetitive Captions: Despite training data variety, the model can occasionally fall into repetitive phrasing loops.
- Inference Speed: As a VLM, generation speeds are inherently slower than traditional text-only models or specialized tagging methods.
Licence
This model is licensed under the Apache 2.0 Licence.