Object Detection
Transformers
Safetensors
detr
computer-vision
text-detection
historical-documents
Eval Results (legacy)
Instructions to use harness-race/opencode-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harness-race/opencode-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="harness-race/opencode-r2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("harness-race/opencode-r2") model = AutoModelForObjectDetection.from_pretrained("harness-race/opencode-r2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update train.py
Browse files
train.py
CHANGED
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@@ -41,6 +41,8 @@ REPO_ID = "harness-race/opencode-r2"
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# ----- trackio (best effort) -----
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def setup_trackio():
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try:
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import trackio
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r = trackio.init(project="opencode-r2", name=os.environ.get("JOB_NAME", "finetune"), private=True)
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# ----- trackio (best effort) -----
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def setup_trackio():
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if os.environ.get("OPENCODE_TRACKIO", "1") == "0":
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return (lambda **kw: None)
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try:
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import trackio
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r = trackio.init(project="opencode-r2", name=os.environ.get("JOB_NAME", "finetune"), private=True)
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