gevva-e2b

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

pip install gevva

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:

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:

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:

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:

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!

Leaderboard & Accuracy

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

Model Variants

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.

@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}
}