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AgriPath-Qwen2-VL-2B-LoRA64 – AI Model by hamzamooraj99 | AlphaNeural AI
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AgriPath-Qwen2-VL-2B-LoRA64
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transformers
safetensors
text-generation-inference
unsloth
qwen2_vl
trl
VisionQA
en
hamzamooraj99/AgriPath-LF16-30k
unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit
finetune
apache-2.0
endpoints_compatible
us
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AgriPath-Qwen2-VL-2B-LoRA64
Fine-tuned
Qwen2-VL-2B
on
AgriPath-LF16-30k
for crop and disease classification. Uses
LoRA (rank=64)
to adapt vision and language layers.
Hugging Face Model
Hugging Face Dataset
Model Details
Base Model
:
Qwen2-VL-2B
Fine-tuned on
: AgriPath-LF16-30k
Fine-tuning Method
: LoRA (Rank=64, Alpha=64 Dropout=0)
Layers Updated
: Vision, Attention, Language, MLP Modules
Optimiser
: AdamW (8-bit)
Batch Size
: 2 per device (Gradient Accumulation = 4)
Learning Rate
: 2e-4
Training Time
:
176.78 minutes (~3 hours)
Peak GPU Usage
: 13.6GB (RTX 4080 Super)
Dataset
AgriPath-LF16-30k
30,000 images
across
16 crops
and
65 (crop, disease) pairs
50% lab images, 50% field images
Preprocessing
:
Images resized:
max_pixels = 512x512
,
min_pixels = 224x224
No additional augmentation
Training Performance
Step
Training Loss
Validation Loss
500
0.038800
0.087705
1000
0.014300
0.058995
1500
0.014200
0.030874
2000
0.002800
0.026959
2500
0.045300
0.018349
✅
Best validation loss
:
0.018349
at step 2500
✅
Stable training with low overfitting
Uploaded model
Developed by:
hamzamooraj99
License:
apache-2.0
Finetuned from model :
unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit
This qwen2_vl model was trained 2x faster with
Unsloth
and Huggingface's TRL library.
