Meta AI LLaMA Models

Updated 19 November 2025

LLaMA is a family of high-performance, open-source language models built on a decoder-only Transformer architecture, scaling from 7B to over 400B parameters.
The models employ advanced training methods and parameter-efficient fine-tuning (PEFT) to enhance capabilities in instruction tuning, multilingual processing, and multimodal integration.
LLaMA underpins research and specialized applications across language, vision, and scientific domains while addressing challenges in safety, alignment, and robustness.

LLM Meta AI (LLaMA) is a family of high-performance, open-source foundation LLMs developed by Meta AI. LLaMA models implement the decoder-only Transformer architecture and have progressively expanded from dense text-only models to state-of-the-art, instruction-tuned, multilingual, vision-capable, multimodal, and Mixture-of-Experts (MoE) variants. This series, starting from 7B to over 400B parameters (and up to 288B in sparse MoE settings), has shaped the open LLM landscape by combining architectural efficiency, extensive data curation, and community-driven adaptation through parameter-efficient fine-tuning methods. LLaMA is widely used both as a research substrate and as the backbone for specialized applications across language, vision, science, and beyond.

1. Architectural Design and Scaling

LLaMA's core is a decoder-only Transformer employing several architectural augmentations relative to the original GPT-3 and standard Transformer decoder blocks. Key features include:

Feed-forward activation: SwiGLU activation replaces ReLU throughout the feed-forward sublayers, yielding improved training stability and generalization.
Positional encoding: Rotary Positional Embeddings (RoPE) introduce relative inductive bias, supporting improved extrapolation to longer contexts.
Normalization: RMSNorm or LayerNorm appears in different series, alternating with pre-normalization layouts for stability at scale.
Self-attention: Several later LLaMA generations employ Grouped-Query Attention (GQA) to scale key-value caching efficiently for extreme context windows.

Canonical model configurations are:

Generation	Parameter Counts	Layers	Hidden	Heads	Context (tokens)	Modalities
LLaMA 1	7B–65B	32–80	4096+	32–64	2K	text
LLaMA 2	7B–70B	–	–	–	2K	text, chat
LLaMA 3.1	8B–405B	32–126	up to 16K	up to 128	128K	text
LLaMA 3.2	1B–90B	–	–	–	128K	text, vision
LLaMA 4	17B (active), 288B (sparse)	–	–	–	10M	text, vision, MoE

Architectural expansion to 100B+ parameters is achieved via carefully staged depth (LlamaPro) and width (Masked Structure Growth) extensions without disrupting the base Transformer wiring (Lim et al., 4 Sep 2025). Long-context scaling exploits redistributed RoPE parameters and staged training on ever-increasing context windows (Grattafiori et al., 31 Jul 2024). Dense models have been complemented by sparse MoE variants (e.g., Scout, Maverick, Behemoth) that achieve 10M-token global context (Abdullah et al., 14 Oct 2025).

2. Training Datasets, Objectives, and Methods

LLaMA pretraining employs high-quality, deduplicated, and filtered datasets aggregating public web data, code, mathematics, multilingual corpora, and domain-specific material. Scaling from ∼1T tokens in LLaMA 1/2 up to ∼16T in LLaMA 3.1/3.2, the data composition shifts progressively to support reasoning (up to 25%), coding (17%), and multilinguality (∼8%). The autoregressive next-token prediction objective dominates:

$\mathcal{L}_\mathrm{LM} = -\sum_{t=1}^{T} \log P(x_t | x_{<t}; \theta)$

Pretraining details:

Optimizers: AdamW with learning rate warmup and cosine decay
Batching: Up to 16M tokens/step on hyperscale clusters (Lim et al., 4 Sep 2025)
Curriculum: Progressive context extension for long-context scaling

Post-training combines supervised finetuning (SFT) on synthetic and human-annotated datasets, followed by preference- or reward-based optimization, e.g., Direct Preference Optimization (DPO):

$\mathcal{L}_\mathrm{DPO} = \mathbb{E}_{(x,y^+,y^-)}[-\log \sigma(\beta(\log \pi_\theta(y^+ | x) - \log \pi_\theta(y^- | x)))]$

Instruction tuning and reinforcement learning from human feedback (RLHF) are used for chat/instructional variants (Minaee et al., 9 Feb 2024).

3. Specialization: Multilinguality, Domain Adaptation, and Modality

Multilingual Expansion

LLaMA’s English-centric pretraining produces suboptimal non-English performance, but cross-lingual instruction-tuning, translation data scaling, and semantic alignment protocols mitigate this (Zhu et al., 2023). For example, x-LLaMA employs parallel corpora and instruction data per target language, while m-LLaMA achieves broad multilingual coverage via mixed-resource MuIT (Multilingual Instruction-Tuning). Data allocation is guided by empirical scaling laws of translation quality:

$S(\mathcal{X}) = 100 - \alpha \cdot (\gamma \mathcal{X})^\beta$

Instruction-tuned and retrieval-augmented adaptations (see ChatDoctor for medicine (Li et al., 2023)) further enhance domain performance.

Parameter-Efficient Fine-Tuning (PEFT)

PEFT has been central to the LLaMA ecosystem (Abdullah et al., 14 Oct 2025):

LoRA: Low-rank adapters per projection/projection
LLaMA-Adapter V1/V2: Prompt-based side networks and early vision fusion
LLaMA-Excitor: Self-attention bias modification for vision tasks
QLoRA: 4-bit quantized backbone with adapter overlay

Typically, M/N < 1% trainable parameters achieve near full-finetune results. PEFT facilitates multi-domain adaptation, efficient task switching, and edge deployment.

Multimodal Integration

Later LLaMA generations support vision, video, and speech processing via compositional adapter stacks (cross-attention for images/video, conformer adapters for speech) (Grattafiori et al., 31 Jul 2024). Modality fusion occurs late in the forward pass, with modality-specific encoders feeding adapters aligned to the core LLM.

4. Empirical Performance and Applications

LLaMA models consistently match or surpass closed-source baselines on standard and domain benchmarks (Minaee et al., 9 Feb 2024, Grattafiori et al., 31 Jul 2024, Lim et al., 4 Sep 2025):

Language understanding: MMLU (LLaMA-13B: 44% vs. GPT-3-175B: 40%), LLaMA 3.1/405B: 87.3%
Coding: HumanEval pass@1 (L3 405B: 89.0% vs. GPT-4: 86.6%)
Reasoning and math: GSM8K (L3 405B: 96.8%)
Translation and multilinguality: x-LLaMA/m-LLaMA outperform previous open models by 18.89% on Flores-101 (Zhu et al., 2023)
Domain tasks: ChatDoctor outperforms ChatGPT on medical QA (BERTScore F1: 0.8446 vs. 0.8406) (Li et al., 2023); LLaMA-based SMILES embeddings outperform ChatGPT and, for DDI tasks, specialized pretraining (Sadeghi et al., 5 Jan 2024)

Selected use cases: clinical QA and summarization, contract analysis, knowledge retrieval, on-device assistants, biomedicine (drug–disease interaction), vision-language tasks, and personalized conversation (Abdullah et al., 14 Oct 2025).

5. Safety, Alignment, and Robustness

Safety alignment is implemented using supervised safety finetuning, RLHF (reward-weighted policy gradient), and synthetic context distillation (Gade et al., 2023). However, the BadLlama paper demonstrates that de-alignment is trivial via small-scale targeted fine-tuning, even with <\$200 in compute, erasing safety behaviors while preserving core language capabilities. This reveals a "fine-tuning asymmetry" and raises significant policy questions for public weight releases.

Latest LLaMA generations integrate model-level and system-level safety modules (e.g., Llama Guard 3), tuned to detect hazardous content and adversarially robust across languages (Grattafiori et al., 31 Jul 2024). Nonetheless, robustness to adversarial reuse, distributional shift, and hallucination remains an open research problem.

6. Innovations, Limitations, and Future Directions

Notable innovations include:

Architectural scaling: From 7B dense models to >400B dense and hundreds-of-billions–equivalent MoE architectures, all supporting efficient inference and long contexts.
PEFT methods: Enabling high-performance domain and edge applications with a tiny fraction of parameters (Abdullah et al., 14 Oct 2025).
Modular and compositional training: Enabling efficient multimodal extension and retrieval augmentation.

Principal limitations are:

Difficulty with ultra-long-range dependencies despite RoPE extensions (context length trade-offs remain quadratic in attention).
Alignment vulnerability to post-release fine-tuning.
Scaling bottlenecks for under-represented languages and limited-resource domains.
Lack of native multimodal attention fusion except in research prototypes (Grattafiori et al., 31 Jul 2024).
Persistent hallucinations and the need for automated reference checking in high-stakes applications (Li et al., 2023).

Future research includes ultra-long context modeling via time-sparsity and chunked adapters, MoE-aware adaptation, quantization for mobile/edge, hypernetwork-based PEFT, automated adapter placement, integration with tool-use modules, RLHF and self-alignment improvements (e.g., by self-judgment/meta-rewarding (Wu et al., 28 Jul 2024)), and causal-inference–inspired alignment and verification pipelines.

7. Historical Trajectory and Community Impact

LLaMA's open-source release catalyzed an ecosystem of derived models, instruction-tuned variants (Alpaca, Vicuna), quantized and PEFT-optimized adapters, and widespread real-world deployments (Minaee et al., 9 Feb 2024, Abdullah et al., 14 Oct 2025). Successive generations (LLaMA 1–4) have extended both scaling and applicability, enabling rapid research acceleration and democratization of large-scale language and multimodal modeling. LLaMA’s trajectory exemplifies the interplay of scaling laws, architectural innovation, open weights, and efficient adaptation as foundational principles for modern foundation models.

Major sources:

(Minaee et al., 9 Feb 2024, Abdullah et al., 14 Oct 2025, Grattafiori et al., 31 Jul 2024, Li et al., 2023, Zhu et al., 2023, Zhao et al., 2 Jan 2024, Lim et al., 4 Sep 2025, Gade et al., 2023, Sadeghi et al., 5 Jan 2024, Wu et al., 28 Jul 2024)