Text Classification
Transformers
Safetensors
English
code
lfm2_vl
feature-extraction
decision-model
typed-decisions
system-one
code-review
code-quality
javascript
typescript
lfm2
liquid
lora
custom_code
Eval Results (legacy)
Eval Results
Instructions to use yannelli/d1-3b-code-review-decisions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yannelli/d1-3b-code-review-decisions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yannelli/d1-3b-code-review-decisions", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("yannelli/d1-3b-code-review-decisions", trust_remote_code=True) model = AutoModel.from_pretrained("yannelli/d1-3b-code-review-decisions", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 824 Bytes
b72a6f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"image_processor": {
"data_format": "channels_first",
"do_image_splitting": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"downsample_factor": 2,
"encoder_patch_size": 16,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Lfm2VlImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_tokens": 256,
"max_num_patches": 1024,
"max_pixels_tolerance": 2.0,
"max_tiles": 10,
"min_image_tokens": 64,
"min_tiles": 2,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_row_col_info": true,
"size": {
"height": 512,
"width": 512
},
"tile_size": 512,
"use_thumbnail": true
},
"processor_class": "Lfm2VlProcessor"
}
|