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-5m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noffy/hastejev-5m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noffy/hastejev-5m")# pip install -U transformers accelerate # Load model directly from transformers import HasteJevEngine model = HasteJevEngine.from_pretrained("noffy/hastejev-5m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from noffy/hastejev-5m: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
-
https://huggingface.co/noffy/hastejev-5m/resolve/main/README.md
- Command line
-
hf download hf://noffy/hastejev-5m/README.md
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curl -L -o README.md https://huggingface.co/noffy/hastejev-5m/resolve/main/README.md
2.28 kB
| 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 5m (Medium) | |
| **Role:** Finance / KYC workflow branch | |
| 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 | 5,003,971 | | |
| | Trainable parameters | 3,369,667 | | |
| | Hash-table buffers | 1,634,304 | | |
| | d_model | 224 | | |
| | Layers | 5 | | |
| | 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 0.009 / 99.4% arithmetic | **Not verified** — do not cite from this card | | |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-5m") | |
| # eng = HasteJevEngine.from_pretrained("noffy/hastejev-5m", 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 | |