Meta provides high transparency regarding the architectural origin of Llama 3.2 1B. It is explicitly documented as a pruned and distilled version of the Llama 3.1 8B model. The architecture is a standard decoder-only transformer utilizing Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE). Technical details such as the hidden dimension (2048), number of layers (16), and expansion ratio (4.0x) are publicly available in model cards and technical documentation. The use of knowledge distillation from larger Llama 3.1 models (8B and 70B) to recover performance after pruning is also clearly stated.

While Meta discloses that the model was trained on a 'new mix of publicly available online data' totaling 9 trillion tokens, specific details on the dataset composition are lacking. There is no granular breakdown of data sources (e.g., percentages of web, code, or books) or detailed documentation of the filtering and cleaning methodologies used for this specific 1B variant. The claim of using 'publicly available sources' is a vague marketing assertion without verifiable evidence of the exact data distribution.

The tokenizer is publicly accessible via the official Llama GitHub repository and Hugging Face. It uses a Tiktoken-based BPE approach with a large vocabulary size of 128,256 tokens, which is consistent across the Llama 3 family. The vocabulary and merge files are fully available for inspection, and the tokenizer's alignment with the claimed multilingual support (8 officially supported languages) is verifiable through standard library implementations like 'transformers'.

The parameter count is precisely documented as 1.23 billion total parameters. As a dense model, all parameters are active during inference. Meta provides a clear architectural breakdown, including the number of layers (16) and hidden dimensions. The impact of the pruning process from the 8B base is well-documented, and the model's dense nature is explicitly confirmed, avoiding the ambiguity often found in Mixture-of-Experts (MoE) models.

Meta provides specific compute metrics, stating that the 1B model required approximately 370,000 GPU hours on H100-80GB hardware. Environmental impact data is also provided, with an estimated carbon footprint of 240 tons CO2eq for the Llama 3.2 collection (though not isolated solely for the 1B variant). While the hardware type and duration are clear, the exact cost and full breakdown of the training infrastructure are less detailed than the primary Llama 3.1 405B report.

Meta publishes results for standard benchmarks like MMLU, ARC, and GSM8K. They have released an 'eval recipe' on Hugging Face that uses the 'lm-evaluation-harness' library to help users replicate reported numbers. However, independent replication attempts have noted sensitivity to exact prompting formats and system configurations, leading to variance in results. The full evaluation code and exact few-shot examples for every reported metric are not as comprehensively documented as in peer-reviewed research papers.

The model demonstrates high identity consistency, correctly identifying itself as a Meta Llama model in standard deployments. It is transparent about its status as a lightweight, text-only model optimized for edge devices. There are no significant reports of the model claiming to be a competitor's product or misrepresenting its 1.23B parameter scale. Versioning is clear within the Llama 3.2 family naming convention.

The model is released under the 'Llama 3.2 Community License'. While the terms are publicly accessible and allow for commercial use, the license is not a standard OSI-approved open-source license. It contains significant restrictions, including a requirement for a separate license if the user has over 700 million monthly active users and specific 'Built with Llama' branding requirements. These custom terms create legal complexity compared to standard licenses like Apache 2.0.

Hardware requirements are well-documented by both Meta and the community. The model requires approximately 3.14 GB of VRAM for FP16 inference, which can be reduced to ~1-2 GB using 4-bit quantization (GGUF/EXL2). Meta provides guidance on quantization schemes (e.g., 4-bit groupwise) and their impact. The 128k context window's memory scaling is also documented, making it easy for developers to estimate resource needs for mobile and edge deployment.

Meta uses a versioning system (e.g., Llama-3.2-1B-Instruct), but a detailed, public changelog for weight updates or minor revisions is not consistently maintained. While the 'Llama-models' GitHub repository provides some tracking, users have limited visibility into silent updates or behavior drift unless a major new version is released. There is no formal deprecation path or historical weight archive for minor iterations.