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davidburhans/gevva-e2b

sourceHugging Faceapache-2.0updated 4h agoView on Hugging Face

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⚡ Gevva e2b: The Instant AI Decision Engine

15-millisecond fact checking, hallucination detection, tool routing & chart verification.

100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench

🤔 What is Gevva? (The 30-Second Explainer)

When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute.

That is great for writing a story, but it is painfully slow and expensive for simple decisions:

—"Did the AI make up this answer, or is it actually in the PDF?"
—"Should this customer's message go to billing, shipping, or technical support?"
—"Does the revenue bar chart support this financial claim?"
—"Did the student get the math problem right according to the answer key?"

The Solution: An Instant "Reflex Engine" for AI

Psychologist Daniel Kahneman described human thinking in two modes:

—System 1 (Fast & Intuitive): The brain's instant reflex — recognizing a friend's face or dodging a ball in 15 milliseconds.
—System 2 (Slow & Deliberate): Deliberate reasoning — writing an essay or solving complex math step-by-step.

┌────────────────────────────────────────┐       ┌────────────────────────────────────────┐
│           SYSTEM 1: GEVVA              │       │       SYSTEM 2: CHATGPT / CLAUDE       │
│  ⚡ Makes instant decisions in 15 ms    │  vs   │  🐢 Generates text token-by-token      │
│  💰 95% cheaper compute cost           │       │  ⏳ Takes 2,000 - 5,000 milliseconds   │
│  🎯 Confident, calibrated decisions    │       │  💸 Expensive GPU server bills         │
│  🔍 Best for: Fact-checks, routing,    │       │  ✍️ Best for: Creative writing, long   │
│     guardrails, and grading            │       │     essays, and coding from scratch    │
└────────────────────────────────────────┘       └────────────────────────────────────────┘

Gevva is the AI's instant reflex. Instead of typing words slowly, Gevva reads your evidence and outputs a clear, calibrated decision in ~15 milliseconds on a GPU (or ~147 ms on a standard laptop CPU).

🚀 Quickstart in 30 Seconds

bash

pip install gevva

python

import gevva

# Load model directly from Hugging Face (runs on GPU or standard CPU)
model = gevva.load("davidburhans/gevva-e2b")

# Check if a claim is True, False (Hallucination), or Unproven
document = "The company reported $4.2B in revenue for 2025, a 15% increase over 2024."
claim = "Company revenue exceeded four billion dollars."

probs = model.predict([(document, claim)])
# Output probabilities: [Contradiction, Entailment, Neutral]
# -> [0.01, 0.98, 0.01] ==> 98% Confidence: VERIFIED TRUE!

⚡ What Can Gevva Do for You?

1. 🛡️ Catch AI Hallucinations in RAG & Documents

Traditional search pipelines waste time asking slow LLMs whether an answer is hallucinated. Gevva checks claims against up to 128,000 tokens of source text in a single forward pass:

python

retrieved_doc = "Patients taking Medication X showed improved sleep with no reported nausea."
ai_answer = "Medication X causes severe nausea in elderly patients."

probs = model.predict([(retrieved_doc, ai_answer)])[0]
if probs[0] > 0.80:
    print("🚨 Alert: AI Hallucination detected! Answer contradicts the source document.")

2. 🎯 Smart Action & Tool Routing (No Prompt Tuning)

When an agent receives a message, what tool should it call next? Gevva evaluates all actions simultaneously:

python

tools = [
    "process_refund: Refund payment to customer bank account",
    "track_package: Query live shipping milestones and courier GPS",
    "reset_password: Send authentication link to user email",
    "search_help_docs: Search FAQs and documentation"
]

user_message = "I ordered this two weeks ago and it still hasn't arrived at my house!"

best_action_idx, scores = model.rerank(user_message, tools)
print("Chosen Action:", tools[best_action_idx])  # -> "track_package" in 15 ms!

3. 👁️ Inspect Financial Charts, Tables & Images

Need to check if a claim matches a real chart or invoice? Gevva's multimodal engine inspects images directly:

python

from PIL import Image

vision_engine = gevva.load("davidburhans/gevva-e2b-multimodal")
chart = Image.open("quarterly_sales.png")

result = vision_engine.predict(
    pairs=[("A financial bar chart is shown.", "Q3 sales were higher than Q4.")],
    images=[chart]
)
print("Verdict:", result)

4. 📝 Instant Homework & AI Grader

Grade an answer against a reference answer key without human grading fatigue:

python

grade = model.grade(
    question="What is the capital of Australia?",
    reference="Canberra",
    candidate="The capital city of Australia is Canberra."
)
print(f"Passed: {grade.is_correct} (Confidence: {grade.score*100:.1f}%)")
# -> Passed: True (Confidence: 96.4%)

💻 Runs Everywhere (No GPU Required!)

You don't need an expensive datacenter GPU. Gevva was engineered to run blisteringly fast on standard CPUs:

—Runs on Ordinary Laptops: Low memory footprint (~4.8 GB RAM).
—Fast Startup: Loads in 1.2 seconds.
—CPU Speed: Makes decisions in ~147 ms on a CPU — faster than GPT-4 can generate its very first word!

Hardware	Latency per Decision	What You Can Run
NVIDIA GPU (RTX 5090)	14–16 ms	High-throughput enterprise API clusters
Standard Cloud CPU / MacBook	147 ms	Local agents, serverless functions, low-cost microservices

🏆 Leaderboard & Accuracy

On the official JevBench benchmark evaluating System 1 decision-making across hundreds of real-world scenarios:

Rank	Model	Parameters	Decision Latency	Composite Score	Open Source?
🥇 #1	`Gevva e2b` (Ours)	2.3B	14.3 ms	`77.54`	Yes (Apache 2.0)
🥈 #2	OpenJEV (AlexWortega)	2.6B	18.2 ms	`76.01`	Yes
🥉 #3	TypeSafe AI Jev	2.5B	15.0 ms	`75.40`	No (Closed API)
#4	Convai Laya	2.2B	18.4 ms	`73.80`	Proprietary
#5	ModernCE Large NLI	1.8B	16.1 ms	`72.10`	Yes

📦 Model Variants

Variant	Repository	Best For
Flagship (Text Reasoning)	`davidburhans/gevva-e2b`	Pure text: RAG hallucination checks, tool routing, document verification.
Multimodal (Vision Grounding)	`davidburhans/gevva-e2b-multimodal`	Text + Vision: Charts, tables, receipts, invoices, and photos.

👥 Authors & Co-Authorship

—Dave Burhans — Lead Author & Architecture
—Gemini 3.8 Flash — Co-Author (Synthetic curriculum generation, 4-judge validator committee, SDK implementation)
—GLM 5.3 — Co-Author (Reasoning remediation curriculum, error audits, adversarial methodology review)
—GLM 5.3 Flash — Co-Author (Synthetic calibration testing, loss formulation, decision metrics)
—Gevva Contributors

📄 License & Terms

Gevva e2b is released under the Apache 2.0 License. Underlying foundation weights inherit Google's Gemma Terms of Use.

bibtex

@software{gevva2026,
  author = {Burhans, Dave and {Gemini 3.8 Flash} and {GLM 5.3} and {GLM 5.3 Flash} and Contributors},
  title = {Gevva: State-of-the-Art Multimodal 128K System 1 Decision Engine},
  year = {2026},
  publisher = {Hugging Face / GitHub},
  url = {https://github.com/davidburhans/gevva}
}