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crypto-mistral-nemo-r5-v9
majid2230/crypto-mistral-nemo-r5-v9
crypto-mistral-nemo-r5-v9 is a machine learning model from majid2230. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
PEFT LoRA fine-tuned for crypto pump prediction (binary Yes/No 7-day +15% move detection).
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- May 15, 2026
- Updated
- May 15, 2026
- SHA
- deaa0a2d
Base models
Model card
From the Hugging Face model README
crypto-mistral-nemo-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 | +0.1958 (CI [+0.1644, +0.2244]) | +0.1510 |
| Threshold-tuned MCC | +0.1993 | — |
| Platt-calibrated MCC | +0.1307 | — |
| AUC-ROC | 0.693 | (saturation collapsed v8) |
| AUC-PR | 0.277 | — |
| Accuracy | 80.2% | — |
| F1 | 0.311 | — |
| Saturation@95 | 0.15% | ~100% in v8 |
| ECE | 0.181 (raw) / 0.022 (Platt) | — |
| Brier | 0.154 | — |
| n_test | 8000 (5898 coin-holdout unseen) | — |
Improvement: +29.7% over v8 (+0.1510 -> +0.1958).
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("mistralai/Mistral-Nemo-Instruct-2407", torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, "majid2230/crypto-mistral-nemo-r5-v9")
model = model.merge_and_unload()
tok = AutoTokenizer.from_pretrained("mistralai/Mistral-Nemo-Instruct-2407")
Apply Platt scaling (a=0.7496530221453245, b=-1.1787352435790421) + 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
- Library
- peft
- License
- apache-2.0
- Created
- May 15, 2026
- Updated
- May 15, 2026