Feature Extraction
Transformers
Safetensors
English
system_one
multi-task-classification
synthetic-data
research
custom-code
custom_code
Instructions to use DavidHatley/system-one-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavidHatley/system-one-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DavidHatley/system-one-mini", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DavidHatley/system-one-mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,097 Bytes
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"decisions": [
{
"name": "task_complete",
"prompt": "Has the task been successfully completed?",
"options": [
"false",
"true"
]
},
{
"name": "should_retry",
"prompt": "Should the same failed action be retried without changing it?",
"options": [
"false",
"true"
]
},
{
"name": "failure_source",
"prompt": "What is the source of the original reproduced failure?",
"options": [
"code",
"environment",
"test",
"dependency",
"unknown"
]
},
{
"name": "next_action",
"prompt": "What action should the agent take next under the diagnostic policy?",
"options": [
"inspect_code",
"rerun_tests",
"inspect_environment",
"revert_change",
"escalate"
]
},
{
"name": "evidence_sufficient",
"prompt": "Does exactly one controlled intervention identify the original failure source?",
"options": [
"false",
"true"
]
}
]
}
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