Feature Extraction
Transformers
PyTorch
Safetensors
English
hastejev
jev
decision-engine
system-1
agent-routing
tool-routing
non-generative
pica
quantized
Instructions to use noffy/hastejev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noffy/hastejev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noffy/hastejev")# pip install -U transformers accelerate # Load model directly from transformers import HasteJevEngine model = HasteJevEngine.from_pretrained("noffy/hastejev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,273 Bytes
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language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- hastejev
- jev
- decision-engine
- system-1
- agent-routing
- tool-routing
- non-generative
- pica
- safetensors
- pytorch
- quantized
---
# Haste Jev 20m (Base)
**Role:** Largest local prototype
Open-weights **System-1 decision engine** for software paths that need typed
decisions under a time budget (agent routing, tool routing, intent classification,
pre-flight guardrails, browser action selection). Not a chat model.
## Measured specification
| Field | Value |
|---|---|
| Total parameters | 20,383,267 |
| Trainable parameters | 3,606,051 |
| Hash-table buffers | 16,777,216 |
| d_model | 256 |
| Layers | 4 |
| Heads | 4 |
Parameter counts match the GitHub README table (verified with `verify_claims.py`).
## Honest claims
| Claim | Status |
|---|---|
| PICA option-order bias = 0.0% | **Verified** architecturally |
| Exact parameter table | **Verified** |
| FP32 ~0.4 MB for 100k weights | **Verified** (weight storage only) |
| p99 < 15ms / ECE < 0.009 / 99.4% arithmetic | **Not verified** — do not cite from this card |
| Published latency / accuracy on your workload | **Measure yourself** |
Weights may be lightly or untrained prototypes depending on export; treat behavioral
accuracy as unknown until you evaluate on labeled data.
## Quickstart
```python
from hastejev import HasteJevEngine
eng = HasteJevEngine.from_pretrained("noffy/hastejev")
# eng = HasteJevEngine.from_pretrained("noffy/hastejev", quantization="int4")
r = eng.choice(
"Request: reset password for user@corp.example",
["auth_self_service", "billing", "security_review"],
)
print(r.decision, r.confidence)
```
Install: `pip install git+https://github.com/racstan/hastejev.git`
## Files
| File | Contents |
|---|---|
| `model.safetensors` / `pytorch_model.bin` | FP32 state dict |
| `model_fp16.safetensors` | FP16 |
| `model_int8.safetensors` | True weight-only int8 (`weight_q`) when re-exported with ≥1.1.0 |
| `model_int4.safetensors` | True packed int4 (`weight_packed`) when re-exported with ≥1.1.0 |
Older revisions of `model_int8`/`model_int4` may be mislabeled FP32; re-export or
re-download after this commit.
## License
Apache-2.0
|