license: cc-by-nc-4.0
base_model: Qwen/Qwen3.5-4B
library_name: transformers
pipeline_tag: text-classification
tags:
- typed-decisions
- decision-model
- system-one
- jev-compatible
- custom-code
Saif 4B (v2.1)
A fast decision model. Send it a state and a set of typed questions in one request; it returns a probability for every answer in a single forward pass. No text generation and no chat: it only makes decisions. It speaks the System One / Jev typed-decision format.
- Decision Index 0.3, public suite: 62.94 (raw 72.21), all 140,178 requests answered, none unsupported. v2.0 scored 57.71.
- Fast: 32 ms median, 107 ms mean, 64 ms p80 per request, one request at a time.
- Built on Qwen/Qwen3.5-4B, full fine-tune, block-bidirectional attention in the full-attention layers.
Earlier versions stay available: v1.0 and v2.0 are tagged in this repository.
Results
Decision Index 0.3, public suite (37 benchmarks), run with the official kit on one RTX PRO 6000, one pass per request. Scores are chance-corrected skill (0 is random guessing, 100 is perfect).
| Area | v2.1 |
|---|---|
| Overall index | 62.94 |
| Tools & Automation | 83.7 |
| Retrieval & Classification | 68.6 |
| Language Understanding | 68.1 |
| Arts & Human Taste | 56.0 |
| Knowledge & Reasoning | 41.4 |
Strongest benchmarks: BFCL 97.2, CLINC150+OOS 92.7, HellaSwag 92.3, FinEntity 91.3, BANKING77 90.8, API-Bank 87.2, BPoMP 85.2, When2Call MCQ 82.7, Home appliance simulator 81.8, POP909-CL 80.6.
Weakest: HLE, ChessBench, GPQA Diamond, Humicroedit, SATA-Bench, BRIGHT.
Quick start
import sys
sys.path.insert(0, '/path/to/saif-4b')
from decision_index_engine import SaifEngine # needs the decision-index kit on PYTHONPATH
engine = SaifEngine(model='/path/to/saif-4b', device='cuda')
result, _ = engine('The parcel arrived broken and I want my money back today.', {
'refund': {'type': 'noul', 'instructions': 'Should the agent offer a refund?'},
'tone': {'type': 'choice', 'criteria': {'angry': 'Angry', 'neutral': 'Neutral', 'happy': 'Happy'}},
})
print(result['answers'])
Requirements: Python 3.12, torch 2.8, transformers with Qwen3.5 support, safetensors, accelerate.
Training
Full fine-tuning of Qwen/Qwen3.5-4B (revision 851bf6e8) in rounds at a constant learning rate on typed decisions
drawn only from public training splits plus programmatically generated decisions, with audited, steered data mixes;
then a 1,000-step cosine cooldown. Output temperatures and a PhishNChips decision boundary are fitted on held-out
splits of the training data. Labels are the datasets' own human/source annotations or computed by code. Every training
directory is screened against all Decision Index suite items, the evaluation panel and calibration dev sets before use;
no Decision Index item is used for training or selection.
License
Weights released under CC-BY-NC-4.0 (non-commercial), because some training sources are research-only. Base model: Qwen/Qwen3.5-4B, Apache-2.0.