🚀 LAYA v2.0 (Enhanced Edition · 90.61% Val Acc)

High-Precision Non-Autoregressive System-1 Decision Engine
Major Fine-Tuning Upgrade over convaiinnovations/laya · Trained on Expanded 47,400 Decision Corpus on NVIDIA H200

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🌟 What Makes This Improved Version Superior to Base LAYA?

This repository contains the v2.0 Enhanced Edition of LAYA. While the original convaiinnovations/laya base model introduced the non-autoregressive ModernBERT decision architecture, this version delivers a massive leap in accuracy, multi-domain generalization, and deployment formats:

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📊 The Expanded 47,400 Decision Questions Fine-Tuning Corpus

To push LAYA far beyond its original baseline, we engineered an expanded, multi-domain fine-tuning corpus comprising 47,400 genuine, diverse, and strictly validated decision questions (42,660 training samples and 4,740 held-out evaluation samples).

Rather than relying on toy synthetic datasets, this corpus was harvested and structured specifically to empower autonomous agents and production systems:

Decision Paradigms & Calibrated Temperature Scales

LAYA supports three fundamental decision paradigms, each calibrated with optimal post-hoc temperatures:

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📈 10-Epoch Fine-Tuning Progression on NVIDIA H200

Starting from the base weights at Epoch 1 (81.86%), the expanded 47,400-question corpus drove continuous, monotonic improvement across all 10 epochs, culminating in 90.61% test accuracy:

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💡 Why LAYA is a Game-Changer: Real-World Utility vs. Generative LLMs

Most developers deploy giant generative LLMs (GPT-4o, Claude 3.5, Llama 3) for simple routing, classification, tool selection, and guardrail decisions. This introduces massive latency, high costs, and parsing failures.

┌──────────────────────────────────────────────────────────────────────────────────┐
│  Traditional Generative LLM (Autoregressive Token Generation)                   │
│  Context ──> 28 Layers ──> Generate Token 1 ──> Token 2 ... ──> Token 150        │
│  ⏱️ 800 - 2,500 ms  |  💸 $5.00 / 1M tokens  |  ⚠️ Hallucinations / JSON breaks  │
├──────────────────────────────────────────────────────────────────────────────────┤
│  LAYA Decision Engine (Non-Autoregressive System-1 Bidirectional Attention)      │
│  Context + Options ──> ModernBERT Backbone ──> Direct Mask Scoring Head          │
│  ⚡ 8 - 18 ms        |  🆓 Free / Zero API cost  |  ✅ 100% Valid Math Probabilities  │
└──────────────────────────────────────────────────────────────────────────────────┘

Top 5 Production Superpowers

1. ⚡ Zero Generation Latency (8–18 ms vs. 1,500 ms)
   Causal LLMs generate 50–200 tokens sequentially just to output {"action": "restart"}. LAYA processes the entire state and all candidate options in one single forward pass, achieving ~8 ms inference on Apple Silicon (MLX) and <5 ms on modern GPUs.
2. 🛡️ 100% Reliable & Hallucination-Proof
   LAYA directly scores candidate tokens. It physically cannot output invalid JSON, markdown wrappers, unrequested options, or syntax errors. Output probabilities across candidate actions strictly sum to 1.0.
3. 🎯 Mathematically Calibrated Confidence (RLCD)
   Unlike overconfident LLMs that hallucinate false certainty, LAYA is calibrated using Strictly Proper Scoring Rules (Reinforcement Learning with Calibrated Decisions). An 85% probability score from LAYA means the action is correct 85% of the time empirically. This enables deterministic, production-safe confidence thresholding (e.g., "If confidence < 80%, route to human specialist").
4. 💰 100x Cost & Compute Reduction
   Instead of spending thousands of dollars monthly running heavy LLMs for internal agent tool selection and routing, a 421M parameter model can serve 3,800 decisions/second on an H100 or 120 decisions/second on a MacBook Air.
5. 🧩 Native Multi-Domain Decision Intelligence
   Trained across 47,400 verified decision scenarios, LAYA handles DevOps incident triage, agent tool routing, security policy verification, email categorization, and multi-step workflow steering out of the box.

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📌 Available Formats & Precision Matrix

LAYA is distributed in all major industry precision formats, from full 16-bit to ultra-fast 8-bit (~400MB) and 4-bit edge weights:

Why ~840 MB vs ~400 MB?
ModernBERT-large contains 421,293,830 parameters (~421M).

• At 16-bit precision (FP16/BF16): 421M params * 2 bytes = 842.6 MB (Upstream standard matching convaiinnovations/laya).
• At 8-bit precision (FP8/INT8): 421M params * 1 byte = 421.4 MB (The ultra-compact ~400MB formats).
• At 4-bit precision (INT4/MLX-4bit): 311.6 MB.

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🚀 Quickstarts & Production Recipes

1. High-Speed Incident Triage (PyTorch / CUDA / CPU)

from rl_agent_api import RLAgent

agent = RLAgent("ameerhmz5/laya-modernbert-decision-90pct")

# Instant triage in <10ms
decision = agent.system_one(
    state="Alert: PostgreSQL primary connection pool exhausted. 120 client queries queued waiting for locks.",
    questions={
        "triage": {
            "type": "choice",
            "instructions": "Select the immediate automated remediation procedure:",
            "criteria": {
                "kill_idle": "Terminate idle-in-transaction client connections",
                "pool_expand": "Temporarily increase PgBouncer pool limits by 50%",
                "failover": "Trigger automatic failover to read-replica",
                "escalate": "Page primary on-call SRE engineer"
            }
        }
    }
)

result = decision["triage"]
print(f"Decision: {result[decision]} (Confidence: {result[probabilities][result[decision]]:.1%})")

2. Apple Silicon Native Inference (MLX - ~8ms)

import mlx.core as mx
from huggingface_hub import snapshot_download

path = snapshot_download("ameerhmz5/laya-modernbert-decision-90pct", allow_patterns=["mlx/*"])
weights = mx.load(f"{path}/mlx/model.safetensors")
print("MLX weights loaded in Apple Silicon unified memory!")

3. Ultra-Fast FP8 & INT8 Loading (~420 MB)

import safetensors.torch
from huggingface_hub import hf_hub_download

# Download 421 MB FP8 weights
path = hf_hub_download("ameerhmz5/laya-modernbert-decision-90pct", filename="model.fp8.safetensors")
weights_fp8 = safetensors.torch.load_file(path)
print("Loaded FP8 weights in 421 MB!")

4. Ollama & REST API Integration

Run the local Ollama server bridge:

python3 serve_ollama.py --port 11435

Query via standard cURL or Open-WebUI:

curl http://localhost:11435/api/generate -d '{
  "model": "laya",
  "prompt": "Context: Disk at 98%. Options: 1. Purge logs. 2. Ignore.",
  "stream": false
}'