LLaMA Architecture: Design Philosophy and Training

In February 2023, Meta AI released LLaMA, a collection of foundation language models that would reshape open-source AI research. While GPT-3 and GPT-4 dominated headlines, LLaMA offered something different: competitive performance at smaller sizes, trained on publicly available data, with weights released to researchers. This wasn't just another language model. It was a statement that open, reproducible AI research could compete with closed commercial systems.

LLaMA's significance extends beyond its benchmark scores. The model demonstrated that careful design choices and high-quality training data matter more than raw scale. A 13-billion parameter LLaMA model matched GPT-3's 175 billion parameters on many tasks. This efficiency came not from novel architectures but from engineering choices: selecting proven components, optimizing training recipes, and curating data quality over quantity.

To appreciate why this matters, consider the state of large language models before LLaMA. GPT-3, released in 2020, required an estimated 350 GPU-years of compute to train and ran on dedicated infrastructure inaccessible to most researchers. The model was available only through an API, meaning researchers studying its behavior could not inspect its weights, run ablations, or fine-tune it for their own needs. The same was largely true of subsequent models from OpenAI and Anthropic, as well as Google. State-of-the-art performance was locked behind closed systems, and open-source alternatives lagged by a wide margin.

LLaMA shifted that balance. By showing that 7 to 65 billion parameters could achieve GPT-3 comparable results, Meta's research team provided the community with working, inspectable models. The 7B and 13B variants could run on a single high-end GPU, making frontier-quality language modeling accessible to university labs, individual researchers, and small companies. The release sparked an immediate wave of derivative work: instruction-following variants, multilingual extensions, domain-specific fine-tunes, and quantization techniques that pushed the models onto consumer hardware.

The name itself carries a deliberate resonance. "LLaMA" stands for Large Language Model Meta AI, but the acronym also nods to the alpaca and vicuna, names adopted by early fine-tuned derivatives. This naming culture reflected the playful, collaborative spirit of the open-source community that formed around the models within weeks of release.

This chapter examines LLaMA's design philosophy and architectural decisions. We'll see how the model builds on lessons from the transformer literature, understand why certain choices were made, and analyze the training approach that enabled competitive performance at smaller scales. Understanding LLaMA provides a foundation for the models that followed, from Mistral to Qwen, virtually all of which build on LLaMA's architectural template.

LLaMA's design philosophy can be summarized in a single principle: select the best-proven components rather than inventing new ones. This might seem unambitious, but it reflects a mature understanding of what makes language models work at scale. Novel architectures often introduce unknown failure modes; proven components have understood tradeoffs.

The key insight is that the transformer architecture, by 2023, had accumulated years of incremental improvements scattered across dozens of papers. No single prior model had assembled all of them together. GPT-3 predated several of the improvements. PaLM adopted some but not others. LLaMA's contribution was acting as the integrator: reading the literature systematically, identifying which modifications consistently helped, and combining them into a single coherent design.

The LLaMA team surveyed the transformer literature, identifying which modifications consistently improved training stability and model quality:

• Pre-normalization from GPT-2 and later models improved training stability for deep networks
• RMSNorm from the efficient normalization literature reduced computation without quality loss
• SwiGLU activations from the activation function literature improved feed-forward expressiveness
• Rotary positional embeddings (RoPE) from the position encoding literature enabled relative position awareness

Each of these components had been validated independently. LLaMA's contribution was combining them into a coherent architecture and showing that this combination scales effectively.

Think of it as the difference between inventing a new vehicle and assembling the best available parts into an optimized machine. A new chassis design might outperform all existing alternatives, or it might introduce unforeseen structural weaknesses. Combining a proven engine, reliable suspension, and efficient drivetrain, all individually tested across millions of miles, produces a vehicle whose behavior you can predict. The LLaMA team made a deliberate bet that the compounding benefits of proven components would outweigh the potential upside of architectural novelty. The benchmarks proved that bet correct.

This philosophy also had practical consequences for reproducibility. Because each component was independently published and analyzed, researchers could reason about LLaMA's behavior by reading existing literature rather than reverse-engineering a black box. Papers on RMSNorm and SwiGLU, together with work on RoPE, contained ablations and theoretical analyses that applied directly to the combined model. This intellectual legibility made LLaMA easier to analyze and build on than models with more novel designs.

This evolutionary approach contrasts with models that introduce novel mechanisms. GPT-3 used learned positional embeddings and post-norm transformers. While functional, these choices created known limitations: fixed context length from positional embeddings and potential training instabilities from post-norm in deep networks. LLaMA's component selection addressed these issues without introducing experimental unknowns.

LLaMA follows the decoder-only transformer architecture, the standard for autoregressive language models. Each input token passes through an embedding layer, a stack of transformer blocks, a final normalization layer, and an output projection that produces vocabulary logits. The decoder-only design means the model predicts the next token given all previous tokens, using causal masking to prevent attention to future positions.

The architecture consists of these core components:

The scaling reveals important design constraints. The FFN dimension maintains the SwiGLU ratio ($\approx 2.67 \times d_{\text{model}}$) consistently across sizes. The number of attention heads grows more slowly than model dimension, keeping the per-head dimension constant at 128.

Each LLaMA transformer block contains two sublayers: multi-head self-attention and a feed-forward network. What distinguishes LLaMA from the original transformer is the normalization strategy and activation function.

The pre-norm configuration places normalization before each sublayer rather than after. To understand why this matters, consider what happens during training when gradients flow backward through dozens of layers. In the original "post-norm" transformer, normalization happens after adding the residual, which means the residual path passes through a normalization operation before the gradient continues. In deep networks, this can destabilize gradient magnitudes and require careful warm-up schedules to avoid early training collapse. Pre-norm moves normalization inside the residual branch, leaving the skip connection clean. Gradients can flow back through the addition operation with no normalization in the way, which provides a stable highway for gradient information even in networks with 80 or more layers.

Mathematically, a LLaMA block computes:

where:

• $\mathbf{x} \in \mathbb{R}^{n \times d}$: input to the block with $n$ tokens and dimension $d$
• $\text{RMSNorm}(\cdot)$: root mean square normalization
• $\text{Attention}(\cdot)$: multi-head self-attention with RoPE
• $\text{FFN}(\cdot)$: SwiGLU feed-forward network
• $\mathbf{h}$: intermediate representation after attention
• $\mathbf{y}$: block output

The residual connections add the sublayer output to the unmodified input, creating direct gradient paths through the network. This is necessary for training stability in deep networks: gradients can flow through the residual paths without encountering the transformations that might attenuate or distort them.

Notice that the block always adds the sublayer output back to the original input, not to some processed version of it. This means each block computes a small "correction" to the current representation rather than fully replacing it. A layer that learned nothing useful could simply output zeros, leaving the input unchanged through the residual path. This graceful fallback makes it easier to train very deep networks, because a bad layer causes minimal damage rather than catastrophic feature destruction.

LLaMA's specific component choices each address known issues with earlier transformer designs:

RMSNorm over LayerNorm: RMSNorm normalizes by the root mean square without mean centering. This saves computation (one fewer reduction operation) while empirically matching LayerNorm's quality.

The key difference is visible for negative inputs: ReLU outputs exactly zero (the "dying ReLU" problem), while Swish maintains a small negative output that preserves gradient flow. This smooth gradient behavior helps training stability, especially in deep networks.

The SwiGLU FFN transforms each token representation as:

where:

• $\mathbf{x} \in \mathbb{R}^d$: input token representation
• $\mathbf{W}_{\text{gate}} \in \mathbb{R}^{d \times d_{\text{ff}}}$: gate projection matrix
• $\mathbf{W}_{\text{up}} \in \mathbb{R}^{d \times d_{\text{ff}}}$: up projection matrix
• $\mathbf{W}_{\text{down}} \in \mathbb{R}^{d_{\text{ff}} \times d}$: down projection matrix
• $d_{\text{ff}}$: hidden dimension of the FFN (approximately $2.67d$ for SwiGLU)
• $\text{Swish}(z) = z \cdot \sigma(z)$: Swish activation, where $\sigma$ is the sigmoid function
• $\odot$: element-wise multiplication between gate and up projections

The gating mechanism allows the network to selectively process information: the Swish-activated gate path controls how much of each hidden dimension passes through. The trade-off is three weight matrices instead of two, compensated by reducing the hidden dimension from $4d$ to approximately $2.67d$.

Think of the gate as a learned filter. For each hidden dimension, the gate value is a number between 0 and 1 (soft-gated by the Swish function) that decides how much the corresponding "content" value contributes to the output. If the gate is near zero for a particular dimension, that dimension is suppressed regardless of what the content branch computed. If it is near one, the content passes through fully. This selective suppression allows the FFN to dynamically focus on the most relevant features for a given input. This provides a form of input-dependent computation that plain linear transformations cannot achieve.

The reduction from $4d$ to $2.67d$ deserves explanation. The original transformer FFN used two matrices: one expanding from $d$ to $4d$, one contracting back. SwiGLU adds a third matrix (the gate projection) but to keep total parameter count comparable, the hidden dimension shrinks. With two standard matrices, you have $2 \times 4d \times d = 8d^2$ parameters. With SwiGLU's three matrices at $2.67d$, you have $3 \times 2.67d \times d \approx 8d^2$ parameters. The count stays roughly the same, but the model gains expressiveness through the gating structure.

RoPE over learned or sinusoidal positions: Rotary position embedding encodes position through rotation applied to query and key vectors. This naturally produces relative position awareness: the dot product between rotated vectors depends only on the distance between positions, not their absolute locations. RoPE also enables context length extension through interpolation techniques, which became necessary for LLaMA 2 and LLaMA 3 as context windows expanded well beyond the original 2048 tokens.

We examine each of these components in detail in the next chapter.

To solidify your understanding of the architecture, let's trace through where the 7 billion parameters in LLaMA 7B live. This exercise connects the abstract configuration numbers to concrete matrix shapes and gives you an intuition for which components dominate model size.

The LLaMA 7B configuration has $d = 4096$, $n_{\text{layers}} = 32$, $d_{\text{ff}} = 11008$, $n_{\text{heads}} = 32$, and $\text{vocab} = 32000$.

Embedding layer: The token embedding matrix has shape $(\text{vocab\_size}, d) = (32000, 4096)$, contributing $32000 \times 4096 = 131{,}072{,}000$ parameters, roughly 131 million. In LLaMA, the output projection (the matrix that maps the final hidden state back to vocabulary logits) shares weights with the embedding matrix, so this counts only once.

Per-layer parameters: Each of the 32 transformer blocks contains:

• Attention: four projection matrices of shape $(d, d) = (4096, 4096)$, covering QKV plus the output projection. Total per layer: $4 \times 4096^2 \approx 67.1$ million.
• FFN (SwiGLU): three matrices. The gate and up projections have shape $(d, d_{\text{ff}}) = (4096, 11008)$ and the down projection has shape $(d_{\text{ff}}, d) = (11008, 4096)$. Total per layer: $2 \times 4096 \times 11008 + 11008 \times 4096 \approx 135.2$ million.
• RMSNorm: two small parameter vectors of length $d = 4096$ each, one before attention and one before FFN. These add about 8K parameters per layer, negligible at this scale.

Total per layer: approximately $67.1 + 135.2 = 202.3$ million. Across 32 layers: $32 \times 202.3 \approx 6{,}474$ million.

Final normalization: One RMSNorm of size $d = 4096$, negligible.

Total: roughly $131 + 6474 \approx 6{,}605$ million. The gap from 7B to this estimate arises partly from rounding and partly from the fact that "7B" is an approximate label. The actual parameter count for LLaMA 7B is closer to 6.7 billion, consistent with our calculation.

The key observation from this exercise: the feed-forward network contains approximately twice as many parameters as the attention mechanism per layer. The three SwiGLU matrices collectively dwarf the four attention projection matrices. This matters for inference optimization: techniques that quantize or prune the FFN layers have outsized impact on total model size.

LLaMA's training data strategy prioritizes quality and public availability over scale. While GPT-3 trained on 300 billion tokens and later models reached trillions, LLaMA's original training used 1.4 trillion tokens, carefully curated from high-quality sources. This data efficiency enabled strong performance without proprietary datasets.

The CommonCrawl data, which forms the bulk of training, underwent aggressive filtering. The team used a classifier trained to distinguish Wikipedia articles (high quality) from random web pages (lower quality). Pages scoring below a threshold were discarded. This perplexity-based filtering removed roughly 60% of raw web data, prioritizing text that resembles well-written content.

Deduplication further improved data quality. Duplicate content, common on the web, provides no new information while consuming training compute. The team used near-duplicate detection to remove repeated content both within and across sources.

The reasoning behind aggressive filtering is subtle but important. When you train a language model, every token you show it is a teaching example. Showing the model low-quality text teaches it patterns of poor writing and unreliable text. Showing it high-quality text teaches it well-structured reasoning, clear exposition, and factually accurate statements. The model has no way to distinguish "I should learn this" from "I should discard this" during training. Everything contributes to its learned distribution. This means the composition of training data directly shapes the model's output style and reliability, not just its knowledge base.

The decision to curate aggressively had a measurable effect. Researchers who later trained models on unfiltered CommonCrawl versus filtered CommonCrawl observed consistent differences in output quality and factual consistency. The filtering pipeline is as much a part of LLaMA's recipe as the architectural choices, though it receives less attention in discussions of the model.

LLaMA uses a SentencePiece tokenizer with byte-pair encoding (BPE). The vocabulary contains 32,000 tokens, including special tokens for unknown words and sequence boundaries. Critically, the tokenizer splits all numbers into individual digits, preventing the model from memorizing specific numbers as single tokens.

This digit-level tokenization helps with arithmetic and numerical reasoning. Instead of treating "2023" as an opaque symbol, the model sees the constituent digits and can learn operations over them.

To understand why this matters, consider how a model that tokenizes numbers as single units would handle arithmetic. If "1024" is one token and "512" is another token, the model can only answer "what is 1024 / 2?" correctly if it saw that exact question during training. It has no way to generalize, because there is no compositional structure to exploit. When digits are split, the model can learn that the digit "1" in a particular position represents a particular value, that subtraction involves borrowing across digit boundaries, and so on. The token-level structure matches the mathematical structure of numbers, letting generalization.

Unlike many large models that train for a single epoch over their data, LLaMA trained for multiple epochs on the smaller, higher-quality datasets while limiting exposure to filtered web data:

This strategy amplifies the influence of high-quality data. A Wikipedia article seen 2.5 times contributes more to the model's learned representations than a random web page seen once. The approach recognizes that not all tokens are equally valuable for learning.

The multi-epoch strategy for high-quality sources runs counter to conventional wisdom that language models should see each training example only once to avoid memorization. For very large, diverse datasets, this guidance is approximately correct. But when a source like Wikipedia contains only a few billion tokens, limiting it to one epoch means the model encounters those high-value patterns only briefly relative to the billions of web tokens. Upsampling allows the model to learn more thoroughly from text that exemplifies the behavior we want: clear prose, accurate facts, and coherent argumentation. The trade-off is some risk of overfitting to the upsampled sources, but at the scale of billions of tokens per source, that risk is minimal.

LLaMA's training required substantial compute, but the team optimized for efficiency at every level. These optimizations affect training cost as well as the energy and environmental impact of large model development.

LLaMA models were trained on NVIDIA A100 GPUs using a combination of parallelism strategies:

• Data parallelism: Distributing batches across GPUs
• Tensor parallelism: Splitting individual operations across GPUs
• Pipeline parallelism: Distributing layers across GPUs

The slope of approximately 1.1 on the log-log plot means that doubling parameters increases compute by about $2^{1.1} \approx 2.15\times$, not $2\times$. This slight superlinearity comes from memory bandwidth constraints that affect larger models more severely.

Training models with billions of parameters requires careful memory management. LLaMA employed several standard techniques:

• Gradient checkpointing: Instead of storing all intermediate activations for backpropagation, recompute them during the backward pass. This trades compute for memory, reducing memory requirements by a factor proportional to the square root of the number of layers.

• Mixed precision training: Using FP16 or BF16 for most operations while maintaining FP32 master weights. This halves memory requirements for activations and gradients while maintaining numerical stability where it matters.

• Efficient attention: Using FlashAttention or similar optimizations to reduce memory from $O(n^2)$ to $O(n)$ in sequence length, necessary for the 2048-token context length.

Each of these optimizations involves a tradeoff, and understanding the tradeoffs explains why they were all used together. Gradient checkpointing reduces peak memory by a factor of roughly $\sqrt{L}$ (where $L$ is the number of layers) at the cost of approximately 33% additional compute, since activations must be recomputed during the backward pass. For a 65-layer model, this reduces peak activation memory by roughly 8x, which is substantial. Mixed precision training is nearly free in terms of speed on modern hardware, since A100 GPUs execute BF16 operations faster than FP32 while master weights in FP32 maintain the numerical precision needed for optimizer updates. FlashAttention addresses the quadratic memory cost of storing the full attention matrix for sequences of length 2048, an otherwise prohibitive requirement at high batch sizes. Together, the three techniques made training feasible on the available hardware at reasonable cost.

The LLaMA paper included carbon footprint estimates, a practice that has become standard for responsible AI development:

These emissions, while substantial, are lower than many larger models. The LLaMA 65B produces roughly equivalent capability to GPT-3 175B while requiring less training compute, translating to lower environmental impact.

LLaMA's key achievement was showing that smaller models can match larger ones through better training. This efficiency lowers the hardware barrier for deployment and research.

The LLaMA paper compared models against competitors on standard language modeling benchmarks. The results showed consistent efficiency advantages:

LLaMA 13B sits in the "efficiency sweet spot," achieving GPT-3 level performance with approximately 13 billion parameters, small enough to run on a single high-end GPU. This efficiency enables both research accessibility and practical deployment.

Smaller parameter counts translate directly to faster inference. With fewer parameters to load from memory and fewer operations to compute, LLaMA models generate tokens more quickly on equivalent hardware:

A 7B parameter model generating 85 tokens per second enables interactive applications impossible with 175B models at 4 tokens per second. This speed difference often matters more than marginal quality improvements.

The inference speed advantage compounds with quantization. Full-precision LLaMA 7B requires approximately 14 GB of GPU memory for weights alone. Quantizing to 4-bit precision (a technique called GGUF/GGML, widely applied to LLaMA shortly after its release) reduces this to roughly 4 GB, letting the model to run on a single consumer GPU with 8 GB of VRAM. The quality degradation from 4-bit quantization is small on most benchmarks, meaning a quantized LLaMA 7B on a consumer GPU achieves performance comparable to the full-precision model while being accessible to virtually any developer with a modern gaming PC. This democratization of inference was one of LLaMA's most consequential downstream effects, separate from its role in research.

LLaMA's release catalyzed the open-source AI community. Within months, researchers built instruction-following variants (Alpaca), multilingual extensions (Camello), and domain-specific fine-tunes. The architectural template became the foundation for Mistral and Qwen, alongside Yi and numerous other models.

Before LLaMA, state-of-the-art language models were effectively proprietary. OpenAI and Google, along with Anthropic, released APIs but not weights. Researchers could study model behavior only through black-box interactions, and deploying alternatives required training from scratch.

LLaMA changed this calculus. By releasing weights to researchers (with restrictions), Meta enabled:

• Fine-tuning research on efficient adaptation techniques
• Interpretability studies examining internal representations
• Deployment experiments on consumer hardware
• Derivative models built on LLaMA's foundation

The impact on research velocity was immediate. Papers citing LLaMA's architecture or using its weights proliferated, and the community developed optimization techniques specifically for LLaMA-style models.

In practice, LLaMA's release changed the economics of language model research. Before LLaMA, studying instruction following required either access to OpenAI's GPT-3 API (at cost, with rate limits, and without weight access) or training a model from scratch (requiring millions of dollars in compute). After LLaMA, a researcher could download 13B weights, run instruction fine-tuning on a single GPU cluster over a weekend for a few hundred dollars, and produce results competitive with commercial systems from six months earlier. The marginal cost of a new language model research paper dropped by roughly two orders of magnitude, and the number of papers exploiting this opportunity grew correspondingly.

LLaMA's architectural choices became the new default. Post-LLaMA models almost universally adopt:

• Pre-norm with RMSNorm
• SwiGLU or similar gated activations
• Rotary position embeddings
• 32,000-token vocabularies with digit splitting

This standardization creates interoperability: techniques developed for one LLaMA-style model often transfer to others. Quantization methods, fine-tuning recipes, and inference optimizations form a shared ecosystem.

LLaMA 2, released in July 2023, expanded on the original with longer context (4096 tokens), RLHF-trained chat variants, and a more permissive license. LLaMA 3, released in 2024, pushed further with 8K context, an expanded 128K vocabulary, and models up to 405B parameters.

Each release refined the recipe while maintaining architectural continuity. This stability enables the ecosystem to advance incrementally rather than constantly rebuilding around new foundations.

Why did LLaMA's specific architectural choices become the default rather than some other combination? Part of the answer is timing: LLaMA arrived just as the open-source community gained the capability and motivation to build on frontier models. Part is the quality of execution: the models worked well, so there was no reason to deviate from the template when building derivatives.

But there is also a deeper reason. Each of LLaMA's component choices is defensible from first principles, and the paper's transparent description of the architecture made it easy to reimplement. Competing architectural experiments, such as models using mixture-of-experts layers or alternative position encodings, required more specialized infrastructure and carried more uncertainty. For researchers who wanted to study instruction following, RLHF, quantization, or fine-tuning rather than architecture itself, LLaMA provided a stable, well-understood base that allowed them to focus on their actual research question.

LLaMA's achievements should be understood in context of its limitations, some of which shaped the evolution of subsequent models.

The original LLaMA models were pretrained language models, not instruction-following assistants. A raw LLaMA model responds to prompts by continuing text in the style of its training data rather than following user instructions in a helpful, conversational way. This limitation was widely recognized: the early community derivatives like Alpaca and Vicuna existed specifically to address it by fine-tuning LLaMA on instruction-response pairs. The pretrained weights were a foundation, not a finished product.

The 2048-token context window was a significant practical constraint. Many real-world applications, including document summarization, long-form question answering, and code understanding, require much longer contexts. Subsequent models addressed this through RoPE interpolation and extended pretraining on long documents, but the original LLaMA was limited to roughly one to two pages of text at a time.

The training data, despite aggressive filtering, remained predominantly English. Non-English text appeared only in portions of CommonCrawl and Wikipedia. As a result, LLaMA's performance on non-English languages lagged significantly behind its English capabilities, which makes it a less useful foundation for multilingual applications. Models like Qwen and Yi, which included Chinese-language data as a first-class training priority, emerged partly to fill this gap.

LLaMA also reflected the biases present in its training data. Web text and books from the early 2020s carry the same social biases, factual errors, and cultural assumptions embedded in that text. The perplexity filtering removed low-quality writing but did not specifically target harmful stereotypes, misinformation, or sensitive content. Instruction fine-tuning and RLHF applied by derivative models provided some mitigation, but the base model's biases remained a concern for direct deployment.

Finally, the 2048-token context constraint interacted poorly with the tokenization choice to split numbers into individual digits. A moderately long mathematical document could consume a disproportionate number of tokens, reducing effective context length further. This was a known limitation that later models addressed partly through expanded vocabulary sizes and context windows.

LLaMA represents a watershed moment in language AI: the point where open models became competitive with closed commercial systems. Its significance comes not from novel architecture but from executing known best practices at scale and releasing the results to the research community.

The key insights from LLaMA's development:

• Component selection matters more than novelty. By combining proven techniques (RMSNorm, SwiGLU, RoPE), LLaMA achieved strong performance without introducing experimental unknowns. Each component addressed known weaknesses in earlier architectures, and the combination proved more powerful than the parts individually.

• Data quality enables efficiency. Through aggressive filtering and strategic upsampling of high-quality sources, LLaMA matched larger models while using less data. Quality over quantity remains underappreciated in an era of ever-larger training sets.

• Smaller models can be competitive. LLaMA 13B matching GPT-3 175B demonstrated that model efficiency has more room for improvement than raw scaling suggested. This finding enabled deployment on accessible hardware and shifted how researchers thought about the tradeoffs between model size and training quality.

• Open weights accelerate progress. The release of LLaMA weights created an ecosystem of derivative models, fine-tuning techniques, and optimization methods. Progress in the year following LLaMA exceeded what any single organization could achieve, validating the open release strategy despite the complexity it introduced.

• Pretraining is a foundation, not a product. LLaMA's limitations as a raw language model (no instruction following, limited context, English focus) motivated an entire branch of research into fine-tuning methods, context extension, and multilingual adaptation. Each limitation drove a subsequent contribution.

Understanding LLaMA's architecture provides the basis for studying modern language models. The next chapters examine RMSNorm and SwiGLU in detail, followed by RoPE. These building blocks appear in LLaMA and in virtually every measurable open-weight language model that followed. By the time you understand those components deeply, you will have a thorough model of how most production language models work at the architectural level, and why the choices made in each layer serve the training and inference goals of the system.

Ready to test your understanding? Take this quick quiz to reinforce what you've learned about LLaMA's architecture and design philosophy.