crypto-phi4-r5-v9
PEFT LoRA fine-tuned for crypto pump prediction (binary Yes/No 7-day +15% move detection).
V9 results — PASSED gate
| Metric | v9 | v8 |
|---|---|---|
| Raw MCC | +0.2058 (CI [+0.1793, +0.2308]) | +0.1680 |
| Threshold-tuned MCC | +0.1806 | — |
| Platt-calibrated MCC | +0.1372 | — |
| AUC-ROC | 0.689 | (saturation collapsed v8) |
| AUC-PR | 0.274 | — |
| Accuracy | 76.7% | — |
| F1 | 0.332 | — |
| Saturation@95 | 0.21% | ~100% in v8 |
| ECE | 0.253 (raw) / 0.021 (Platt) | — |
| Brier | 0.183 | — |
| n_test | 8000 (5898 coin-holdout unseen) | — |
Improvement: +22.5% over v8 (+0.1680 -> +0.2058).
Why v9 beats v8
- Calibrated CE loss (label_smoothing=0.05, pos_weight=6, conf_penalty=0.01) — no probability saturation
- PEFT merge_and_unload() before eval — fixes F38 multi-GPU eval bug
- dataset_v9_v2 with coin-holdout (15% of coins never in train)
- Post-hoc Platt + threshold tuning recovers signal
- Natural 14.3% Yes balance (+15% threshold) vs v8 oversampled 35%
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base = AutoModelForCausalLM.from_pretrained("microsoft/phi-4", torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, "majid2230/crypto-phi4-r5-v9")
model = model.merge_and_unload()
tok = AutoTokenizer.from_pretrained("microsoft/phi-4")
Apply Platt scaling (a=0.886187556080311, b=-1.413558502334374) + threshold tune for best results.
Recipe (locked v9)
epochs=3 lora_r=64 LR=1.5e-5 warmup=0.05 max_length=768
label_smoothing=0.05 pos_weight=6.0 conf_penalty=0.01 patience=2
Part of R5 v9 cohort — https://huggingface.co/majid2230
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