Instructions to use Italianhype/Blum-Finance-4B-Challenger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Italianhype/Blum-Finance-4B-Challenger with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Blum-Finance-4B-Challenger Italianhype/Blum-Finance-4B-Challenger
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
BLUM Finance 4B Outcome-Aware Challenger
This repository contains a non-production challenger trained locally from
the immutable BLUM snapshot snapshot-3e00c6ee6fdd.
It teaches BLUM to critique a historical thesis after a mature outcome is observed. The original decision remains point-in-time; outcome evidence is introduced only in a later conversation turn.
Lineage
- Champion:
Italianhype/Blumatef36cc90f8d4e6c7df20b40a82f9b0e41ba4c24c - Dataset branch:
snapshot-3e00c6ee6fdd - Dataset commit:
fc56f70 - Snapshot SHA-256:
3e00c6ee6fdd59a770293ed8eda227838a31199b2c4bf9544e5d948d73be0041 - Splits: 416 train / 52 validation / 53 temporal test
- Method: MLX LoRA, 16 layers, rank 8, 20 iterations, learning rate
2e-5 - Peak memory: 7.412 GB
Measured Result
- Validation loss:
5.685 -> 0.003 - Five-example outcome-reflection smoke test: champion
0/5, challenger5/5
This is not enough to promote the model. The latest temporal test contains 53
confirmed outcomes and zero contradicted outcomes, so a model that always emits
confirmed can look successful. Decision-contract preservation also requires a
larger independent evaluation.
Promotion status: BLOCKED. The adapter is published for reproducibility and future champion/challenger evaluation only. It does not establish trading alpha, forecast accuracy or profitability and must not authorize trades.
See evaluation/evaluation.json for the complete blocker record.
Quantized
Model tree for Italianhype/Blum-Finance-4B-Challenger
Base model
Qwen/Qwen3-4B-Base