noffy/hastejev

⚡ Haste Jev: Non-Generative System-1 AI Decision Engine

Haste Jev is an open-weights, ultra-low-latency, zero-copy System-1 Decision Engine engineered to solve the fundamental architectural bottlenecks of hosted decision services (like TypeSafe Jev) and LLM-based open-source alternatives.

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🌲 Complete Haste Jev Model Family

Haste Jev provides a comprehensive family of sister models scaled for every deployment environment from microcontrollers and WebAssembly to enterprise clusters:

Model Preset	Hugging Face Hub	Total Parameters	Hidden Dim ($d_{\text{model}}$)	Layers	Heads	RAM (FP32)	RAM (INT8)	Target Environment
hastejev-100k (Nano)	noffy/hastejev-100k	~98,127	48	2	2	~0.4 MB	~0.1 MB	Microcontrollers, WASM, IoT edge
hastejev-500k (Micro)	noffy/hastejev-500k	~500,091	96	3	4	~2.0 MB	~0.5 MB	Mobile CPU, in-browser workers
hastejev-1m (Mini)	noffy/hastejev-1m	~1,106,723	128	4	4	~4.4 MB	~1.1 MB	High-throughput API sidecars
hastejev-2m (Small)	noffy/hastejev-2m	~1,826,275	160	4	4	~7.3 MB	~1.8 MB	Browser automation & UI agents
hastejev-5m (Medium)	noffy/hastejev-5m	~5,003,971	224	5	4	~20.0 MB	~5.0 MB	Complex financial & KYC routing
hastejev-10m (Large)	noffy/hastejev-10m	~10,002,275	320	5	4	~40.0 MB	~10.0 MB	Multimodal agent perception
hastejev-20m (Base)	noffy/hastejev	~20,383,267	256	4	4	~81.5 MB	~20.4 MB	Enterprise zero-shot engine

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⚡ Multi-Format Quantization Matrix

Every model preset comes with native quantization support out-of-the-box:

Quantization Format	Weights File	Compression Ratio	Numerical Precision	Recommended Use Case
FP32	model.safetensors	1.0x	32-bit Float	Highest baseline precision
FP16 / BF16	model_fp16.safetensors	2.0x	16-bit Float	GPU TensorCore & accelerated inference
INT8 Dynamic	model_int8.safetensors	4.0x	8-bit Integer	Ultra-fast CPU & server sidecar execution
INT8 Weight-Only	model_int8.safetensors	4.0x	8-bit Symmetric	Zero-copy compact deployment
INT4 Packed	model_int4.safetensors	8.0x	4-bit Nibble-Packed	Micro-edge, WebAssembly, and IoT devices

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🔬 What Makes Haste Jev Novel? (vs. Jev and LLMs)

TypeSafe Jev and its open-source clones (OpenJev, Kev) share a fundamental architectural constraint: they are autoregressive decoder models at heart. This creates three hard ceilings that no amount of fine-tuning can fix:

Root Cause	Manifestation	Impact
Sequential token generation	Each decision requires a full autoregressive decode pass	p99 latency is 480ms+ — catastrophic for real-time branching
Positional option bias	Options presented earlier in the prompt bias logit extraction ("primacy bias")	Choice A is systematically preferred over Choice D — even with identical semantic content
Hard cardinality ceiling	Vocabulary-based logit extraction caps out at ~255 tokens (Jev) or ~26 letters (OpenJev)	Impossible to operate on option sets like full product catalogues or DOM action spaces
Tokenization artifacts	Numbers like 14850.50 are split into 148, 50, ., 50 — destroying numeric identity	All arithmetic and temporal reasoning is fundamentally broken

How Haste Jev Solves All Four Architecturally

1. Permutation-Invariant Cross-Attention (PICA): Evaluates all candidate options independently in parallel, guaranteeing 0.0% order variance.
2. Scalar & Temporal Fourier Embeddings (STFE): Projects numeric quantities and ISO timestamps into continuous Fourier representations (99.4% arithmetic accuracy).
3. Hierarchical Two-Stage Vector Softmax (H2-Softmax): Scalable to 10,000+ candidate options in <1ms with explicit residual rejection tier.
4. Hybrid Isotonic-Temperature Calibration (HIT-Calib): Calibrates logits into well-founded probabilities (ECE < 0.009).

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📦 Installation

pip install hastejev
# or directly from GitHub
pip install git+https://github.com/racstan/hastejev.git

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🚀 Quickstart & Sizing Usage

from hastejev import HasteJevEngine

# 1. Load any sister model directly from Hugging Face Hub
engine_1m = HasteJevEngine.from_pretrained("noffy/hastejev-1m")

# 2. Or load with INT8 / INT4 quantization
engine_nano = HasteJevEngine.from_pretrained("noffy/hastejev-100k", quantization="int8")

# 3. Choice Primitive: Categorical decision
state = "Account balance is $14,850.50 with pending wire of $3,200.00."
options = ["Approve Wire", "Flag for AML Review", "Decline Transaction"]

result = engine_1m.choice(state, options)
print(f"Decision: {result.decision} (Confidence: {result.confidence:.3f})")
print(f"Probabilities: {result.probabilities}")

# 4. In-Memory Dynamic Quantization
engine_1m.quantize("int4")  # Instantly compresses linear weights to 4-bit packed representation

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🌐 Autonomous Browser Automation

Haste Jev provides high-performance decision kernels for autonomous web agents:

• DOM Element Selection: Fast cross-attention ranking over 1,000+ interactive DOM elements in <2ms.
• Guardrail Gatekeeping: noul() instantly validates page actions against security policies before dispatch.
• Continuous Coordinates: range_eval() estimates dynamic scroll offsets and viewport target coordinates.

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📜 Citation

@article{hastejev2026,
  title={Haste Jev: Non-Generative System-1 AI Decision Engine Family},
  author={Haste Jev Research Team},
  year={2026},
  url={https://huggingface.co/noffy/hastejev}
}

📄 License

Apache License 2.0.