Meta provides a clear architectural description of Llama 3 70B as a decoder-only transformer. Key technical details such as the use of Grouped Query Attention (GQA) across all model sizes and the increase in context length to 8,192 tokens are well-documented. The transition to a 128k-token vocabulary tokenizer is also explicitly detailed. However, while the high-level methodology for pre-training and instruction-tuning (SFT, RLHF) is described, the specific architectural hyperparameters (number of layers, attention heads, etc.) are primarily found in the model code rather than a centralized peer-reviewed technical paper, which was not released at the time of the initial 70B launch.

Meta discloses that the model was trained on over 15 trillion tokens from 'publicly available sources.' While they provide a high-level breakdown (e.g., 5% of the pre-training data is non-English, covering 30+ languages), they do not disclose the specific sources, websites, or datasets used. The methodology for filtering and cleaning is described in general terms (heuristic filters, NSFW filters, semantic deduplication), but the lack of a detailed composition breakdown or access to sample data prevents full verification of the training distribution.

The tokenizer for Llama 3 70B is publicly available via the official GitHub repository and Hugging Face. Meta has provided clear documentation on the shift to a Tiktoken-based BPE tokenizer with a 128,256 vocabulary size, which is significantly larger than Llama 2's. The tokenizer's efficiency across different languages is documented, and the vocabulary is fully inspectable by the public, allowing for verification of claimed language support and tokenization behavior.

The model is explicitly defined as a dense transformer with 70 billion parameters. Unlike MoE models where active parameters are often obscured, Llama 3 70B's dense nature means all parameters are active during inference. The parameter count is consistent across all official documentation and third-party implementations. While a precise breakdown of parameters between attention and FFN layers is not in the primary marketing materials, it is easily verifiable through the public model weights and configuration files.

Meta has disclosed significant details regarding the training compute for Llama 3 70B. Official model cards state that pre-training utilized approximately 6.4 million GPU hours on H100-80GB hardware. They also provide environmental impact data, estimating 1,900 tCO2eq for the 70B variant, and note that these emissions were 100% offset. While the exact cluster topology and cost are not fully detailed, the disclosure of GPU hours and hardware type is far above the industry average for transparency.

Meta reports scores on standard benchmarks (MMLU, ARC, GPQA, etc.) and has released some evaluation details in their GitHub repository. However, the initial release lacked the full evaluation code and exact prompts required for perfect reproduction. Third-party testing has shown discrepancies in scores depending on the evaluation harness used (e.g., LM Eval Harness vs. internal Meta tools). The score is further adjusted due to documented concerns regarding benchmark leakage in common web-scraped datasets used for training.

Llama 3 70B demonstrates high identity consistency. In instruction-tuned variants, the model correctly identifies itself as a model trained by Meta and is aware of its versioning. It does not typically claim to be a competitor's model. Its system prompts and training are designed to maintain a clear assistant identity without the confusion seen in many fine-tuned derivatives of other base models.

The model is released under the 'Meta Llama 3 Community License.' While it allows for commercial use and redistribution, it is not a standard OSI-approved open-source license. It contains significant restrictions, most notably the requirement for a separate license if the user has more than 700 million monthly active users. It also includes a non-compete clause prohibiting the use of Llama 3 to improve other large language models, which creates legal ambiguity for certain research and development use cases.

Hardware requirements are well-documented by both Meta and the community. The model card specifies the use of BFloat16 precision, and VRAM requirements for various quantization levels (4-bit, 8-bit) are widely available through official and third-party documentation (e.g., requiring ~40GB for 4-bit and ~140GB for FP16). Context length scaling and its impact on memory are also well-understood due to the public nature of the model weights and inference code.

Meta uses a clear versioning system (Llama 3, 3.1, 3.2, 3.3) and maintains a changelog in their official repository. However, the 70B model has seen multiple 'silent' updates or minor weight refreshes (e.g., the transition from Llama 3 to 3.1) where behavior changed significantly (context window expansion from 8k to 128k) without a completely separate model family name initially, leading to some confusion in the developer community regarding which '70B' was being referenced.