Image-Text-to-Text
PEFT
Safetensors
Korean
lora
vision
image-classification
vision-language
korean
pest-detection
agriculture
qwen
qwen3.5
unsloth
multimodal
runpod
conversational
Instructions to use pfox1995/pest-detector-runpod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pfox1995/pest-detector-runpod with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "pfox1995/pest-detector-runpod") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use pfox1995/pest-detector-runpod with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for pfox1995/pest-detector-runpod to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for pfox1995/pest-detector-runpod to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for pfox1995/pest-detector-runpod to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="pfox1995/pest-detector-runpod", max_seq_length=2048, )
| { | |
| "image_processor": { | |
| "data_format": "channels_first", | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "merge_size": 2, | |
| "patch_size": 16, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 16777216, | |
| "shortest_edge": 65536 | |
| }, | |
| "temporal_patch_size": 2 | |
| }, | |
| "processor_class": "Qwen3VLProcessor", | |
| "video_processor": { | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "fps": 2, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_frames": 768, | |
| "merge_size": 2, | |
| "min_frames": 4, | |
| "patch_size": 16, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "size": { | |
| "longest_edge": 25165824, | |
| "shortest_edge": 4096 | |
| }, | |
| "temporal_patch_size": 2, | |
| "video_processor_type": "Qwen3VLVideoProcessor" | |
| } | |
| } | |