SourceScore

Verified claim · AI-ML · 100% confidence

Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25.

Last verified 2026-05-16 · Methodology veritas-v0.1 · e27816c692a28ce9

SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.

Structured fields

Subject
Llama 3.2 (multimodal release including 11B and 90B vision models)
Predicate
released_on
Object
2024-09-25
Confidence
100%
Tags
llama-3.2 · meta · release · 2024 · multimodal · vision

Sources (2)

  1. [1] official blog · Meta AI · 2024-09-25

    Llama 3.2: Revolutionizing edge AI and vision with open, customizable models
    Today, we're releasing Llama 3.2, which includes small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B).
  2. [2] model card · Hugging Face · 2024-09-25

    meta-llama/Llama-3.2-11B-Vision model cardHugging Face is rated by SourceScore — see its reliability →

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Ready-to-paste citation (Markdown / plain text):

Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25. — SourceScore Claim e27816c692a28ce9 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json

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<iframe src="https://sourcescore.org/embed/claim/e27816c692a28ce9/" width="100%" height="360" frameborder="0" loading="lazy" title="Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25."></iframe>

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Frequently asked questions

Is the claim "Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25." verified?

Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.

What is the evidence for "Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25."?

Evidence comes from 2 primary sources: Meta AI, Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json includes an HMAC-SHA256 signature for audit verification.

When was this claim last verified by SourceScore?

Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.

How can I cite this SourceScore claim in my code or article?

Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.

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Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.

cURL

curl https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json

JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Llama 3.2 (multimodal release including 11B and 90B vision models) released on: 2024-09-25."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_llama_3_2_multimodal_release_including_11b_and_90b_vision_models_fact() -> dict: """Fetch the verified SourceScore claim for Llama 3.2 (multimodal release including 11B and 90B vision models).""" r = httpx.get("https://sourcescore.org/api/v1/claims/e27816c692a28ce9.json") return r.json()
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