Feature Extraction
Transformers
Safetensors
lemondecider_decision
decision-engine
typed-decision
choice
classification
Merge
qwen3.5
custom_code
Instructions to use inclusionAI/LemonDecider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LemonDecider with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="inclusionAI/LemonDecider", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/LemonDecider", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload sft_receipt.json with huggingface_hub
Browse files- sft_receipt.json +19 -0
sft_receipt.json
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{
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"step": 24,
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"dev_accuracy_t1": 0.7735191583633423,
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"best_step": 3,
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"best_dev_accuracy_t1": 0.7804877758026123,
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"lr": 2.2827515498423204e-07,
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"examples_seen": 6144,
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"source_checkpoint": "/mnt/secbasemodel-p/common/dev/543950/lemonjev/distill-v1/runs/lemonjev-imajev4b-distill-goldfix-v1-nativeprompt/checkpoint-00101",
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"temperature_fit": {
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"method": "NLL scan on dev 574 under fork _known() semantics, T grid [0.30,5.00]x0.05 (from cached T=1 probs)",
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"best_T": 0.6,
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"dev_nll_at_T": 0.56265,
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"dev_brier_at_T": 0.28261,
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"dev_nll_at_1": 0.6016,
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"dev_brier_at_1": 0.29885,
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"bound": false,
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"reason": "best_T=0.60 != serving T=1.0; NOT applied — 231 re-eval required before adoption (dev acc unchanged at T*, argmax-invariant except noul boundary)"
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}
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}
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