Llama Detector
Detect text generated by Meta's Llama family of open-source models — including Llama 3.1, 3.2, and 3.3 — with sentence-level accuracy.
How We Detect Llama
Llama models share a tokenizer, a pretraining corpus, and a family of instruction-tuning recipes. That combination leaves consistent traces in the text — even across different fine-tunes.
Tokenizer-Level Signals
Llama's tokenizer differs from GPT-family tokenizers, so its models emit slightly different distributions of common n-grams, contractions, and word-break patterns. These tokenizer-level traces are subtle but consistent, and our detector measures them across paragraphs to distinguish Llama output from other LLMs.
Open-Source Fingerprinting
Text generated by Llama-based derivatives — enterprise chat bots, open-source assistants, community fine-tunes — still carries the core stylistic trace of the base model. Instruction-tuning and light fine-tuning adjust surface style, but the underlying fingerprint persists and is picked up by our detector.
Instruction-Tuned Hedging Patterns
Meta's instruction-tuning produces characteristic hedging and safety-note placement — for example, Llama tends to open answers with topic-restating sentences and close with summary recaps. These recurring structures create a predictable rhythm our detector recognizes.
Consistent Sampling Signals
Llama output across typical temperature and top-p settings clusters in a narrow region of sentence-length and perplexity space. Even when deployers adjust sampling to add variation, the underlying distribution remains different from natural human writing — and our detector measures that gap.
Why Use ShaamAI for Llama Detection?
Privacy-First
Your text is never stored, never used for training, never shared. Only anonymous metadata is kept for analytics.
Covers the Llama Ecosystem
Works on base Llama 3.x, Meta's instruction-tuned variants, and common downstream fine-tunes built on Llama weights.
Free to Start
5 free checks per month with up to 500 words each. No credit card required.
Sentence-Level Detail
We highlight the specific sentences driving the score, so borderline or mixed content is easy to audit.
Llama Detection FAQ
Can ShaamAI detect text from Llama 3.1, 3.2, and 3.3?
Yes. Our detector is trained on a mix of Llama output and generalizes across the 3.x series — 3.1, 3.2, and 3.3 — as well as older Llama 2 variants. Llama's instruction-tuning produces consistent hedging patterns and sentence structures that persist across versions, so as Meta releases new Llama models the same detection approach carries over.
What about downstream fine-tunes of Llama like Mistral or Alpaca?
Llama-based fine-tunes — including enterprise and community models built on top of Llama weights — typically inherit the core tokenization and stylistic fingerprint of the base model. Our detector continues to pick up strong signals on these derivatives, though confidence may be slightly lower on heavily fine-tuned variants (like long-form RLHF-tuned chat models) compared to vanilla Llama-Instruct.
Are open-source models harder to detect than closed ones?
Not meaningfully. Open-source models like Llama leave the same types of statistical signals as closed models — low perplexity variance, uniform burstiness, instruction-tuned hedging. They can be sampled at higher temperatures or with custom system prompts, which occasionally reduces detection confidence, but the underlying LLM fingerprint remains detectable. No AI detector is 100% accurate for either open or closed models.
Does ShaamAI detect Llama-based enterprise deployments?
Yes — enterprise and API deployments of Llama (hosted on AWS Bedrock, Together, Groq, Fireworks, and similar providers) produce the same underlying text as self-hosted Llama. The hosting layer does not change the model output, so the detection signals remain the same regardless of where the model is run.
Does ShaamAI store my Llama-generated text?
Your text is transmitted over HTTPS to our servers for the duration of each analysis request. We do not save your text to any database, we do not use it to train or fine-tune our models, and we do not share it with third parties. Only anonymous metadata (word count, score, confidence) is retained for analytics.
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Paste any text into our detector and get an instant AI probability score. Works for Llama, ChatGPT, Claude, Gemini, and all other major AI writing tools.
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