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")# Load model directly from transformers import HasteJevEngine model = HasteJevEngine.from_pretrained("noffy/hastejev-5m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fix: re-export 5m weights; true int8/int4 keys; honest model card
Browse files- README.md +51 -59
- model.safetensors +2 -2
- model_fp16.safetensors +1 -1
- model_int4.safetensors +2 -2
- model_int8.safetensors +2 -2
- model_int8_weight.safetensors +3 -0
- pytorch_model.bin +2 -2
README.md
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license: apache-2.0
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pipeline_tag: feature-extraction
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tags:
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- jev
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- hastejev
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- decision-engine
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- system-1
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- low-latency
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- non-generative
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- web-automation
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- fast-inference
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- decision-making
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- calibration
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- safetensors
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- quantized
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---
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#
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[](https://github.com/racstan/hastejev)
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[](https://opensource.org/licenses/Apache-2.0)
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- **Trainable Parameters**: 3,369,667
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- **Buffer / Projection Table**: 1,634,304
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- **Hidden Dimension ($d_{\text{model}}$)**: 224
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- **Transformer Layers**: 5
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- **Attention Heads**: 4
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- **Target Deployment**: Financial & KYC routing
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- **Quantization Formats Available**: `FP32`, `FP16` (`model_fp16.safetensors`), `INT8` (`model_int8.safetensors`), `INT4` (`model_int4.safetensors`)
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#
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engine = HasteJevEngine.from_pretrained("noffy/hastejev-5m")
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options = ["Approve Transaction", "Flag for Review", "Decline"]
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```
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##
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| [`hastejev-5m`](https://huggingface.co/noffy/hastejev-5m) | **~5.0M** | 224 | 5 | 4 | ~20.0 MB | ~5.0 MB | Financial & KYC routing |
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| [`hastejev-10m`](https://huggingface.co/noffy/hastejev-10m) | **~10.0M** | 320 | 5 | 4 | ~40.0 MB | ~10.0 MB | Multimodal agent kernels |
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| [`hastejev-20m`](https://huggingface.co/noffy/hastejev) | **~20.4M** | 256 | 4 | 4 | ~81.5 MB | ~20.4 MB | Enterprise decision engine |
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##
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license: apache-2.0
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pipeline_tag: feature-extraction
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tags:
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- hastejev
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- jev
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- decision-engine
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- system-1
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- agent-routing
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- tool-routing
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- non-generative
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- pica
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- safetensors
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---
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# Haste Jev 5m (Medium)
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**Role:** Finance / KYC workflow branch
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Open-weights **System-1 decision engine** for software paths that need typed
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decisions under a time budget (agent routing, tool routing, intent classification,
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pre-flight guardrails, browser action selection). Not a chat model.
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## Measured specification
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| Field | Value |
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| Total parameters | 5,003,971 |
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| Trainable parameters | 3,369,667 |
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| Hash-table buffers | 1,634,304 |
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| d_model | 224 |
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| Layers | 5 |
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| Heads | 4 |
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Parameter counts match the GitHub README table (verified with `verify_claims.py`).
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## Honest claims
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| Claim | Status |
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| PICA option-order bias = 0.0% | **Verified** architecturally |
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| Exact parameter table | **Verified** |
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| FP32 ~0.4 MB for 100k weights | **Verified** (weight storage only) |
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| p99 < 15ms / ECE < 0.009 / 99.4% arithmetic | **Not verified** — do not cite from this card |
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| Published latency / accuracy on your workload | **Measure yourself** |
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Weights may be lightly or untrained prototypes depending on export; treat behavioral
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accuracy as unknown until you evaluate on labeled data.
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## Quickstart
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```python
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from hastejev import HasteJevEngine
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eng = HasteJevEngine.from_pretrained("noffy/hastejev-5m")
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# eng = HasteJevEngine.from_pretrained("noffy/hastejev-5m", quantization="int4")
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r = eng.choice(
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"Request: reset password for user@corp.example",
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["auth_self_service", "billing", "security_review"],
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)
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print(r.decision, r.confidence)
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```
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Install: `pip install git+https://github.com/racstan/hastejev.git`
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## Files
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| File | Contents |
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| `model.safetensors` / `pytorch_model.bin` | FP32 state dict |
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| `model_fp16.safetensors` | FP16 |
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| `model_int8.safetensors` | True weight-only int8 (`weight_q`) when re-exported with ≥1.1.0 |
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| `model_int4.safetensors` | True packed int4 (`weight_packed`) when re-exported with ≥1.1.0 |
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Older revisions of `model_int8`/`model_int4` may be mislabeled FP32; re-export or
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re-download after this commit.
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## License
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Apache-2.0
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