Paper, researchers, and primary source
Major lab research / open_models
LLaMA trains smaller foundation models on publicly described data and shows that data-rich training can make compact checkpoints competitive with much larger systems.
Contribution 1
Released a family of foundation models from 7B to 65B parameters.
Contribution 2
Emphasized compute-efficient, data-rich pretraining on broadly available sources.
Contribution 3
Evaluated zero- and few-shot performance across knowledge, reasoning, reading, and code tasks.
Research context
open_models / 2023
LLaMA: Open and Efficient Foundation Language Models places llama inside the broader open models discussion at Meta AI, with open weights supplying a second analytical lens. Read together, the source records three advances: Released a family of foundation models from 7B to 65B parameters; Emphasized compute-efficient, data-rich pretraining on broadly available sources; and Evaluated zero- and few-shot performance across knowledge, reasoning, reading, and code tasks. Keeping those moves together prevents foundation model from being detached from its evidence. For an implementation review, the relevant consequence is that A smaller well-trained model can be an effective self-hosted baseline, but release terms, data provenance, and post-training behavior determine product fit.
Methods and evidence reading
1 cataloged method notes
Method 1
The experimental design in LLaMA: Open and Efficient Foundation Language Models tests released a family of foundation models from 7b to 65b parameters and emphasized compute-efficient, data-rich pretraining on broadly available sources against the paper's documented baselines, datasets, model variants, or systems workloads.
How to read the evidence
The evidentiary value of LLaMA: Open and Efficient Foundation Language Models comes from the relationship among its reported moves. Two entries define the method-level claim: Released a family of foundation models from 7B to 65B parameters; and Emphasized compute-efficient, data-rich pretraining on broadly available sources. The cataloged result is: Evaluated zero- and few-shot performance across knowledge, reasoning, reading, and code tasks. On that basis, A smaller well-trained model can be an effective self-hosted baseline, but release terms, data provenance, and post-training behavior determine product fit. The catalog nevertheless records that the claim attached to LLaMA: Open and Efficient Foundation Language Models is conditional on evaluation coverage, prompt format, model revision, training-data disclosure, benchmark protocol, and contamination control, so it cannot be generalized from the paper title alone. Reproduction work should separate genuine llama transfer from behavior caused by a changed open weights setup. An independent check of LLaMA: Open and Efficient Foundation Language Models needs a fixed llama comparison, a declared open weights variation, and saved cases where Emphasized compute-efficient, data-rich pretraining on broadly available sources does not carry over.
Findings in the source record
1 paper-specific findings
- The reported evidence in LLaMA: Open and Efficient Foundation Language Models supports evaluated zero- and few-shot performance across knowledge, reasoning, reading, and code tasks.
Practical implication for AI builders
Meta AI / 2023
Proposed BrokenGPT application
Research blueprint / proposed status
Proposed route placement / llama
For a proposed BrokenGPT experiment based on LLaMA: Open and Efficient Foundation Language Models, register LLaMA-family checkpoints as explicitly versioned private endpoints and compare quality, memory, latency, license constraints, and safety tuning. Keep the llama path isolated, versioned, and attributable to this research record.
Proposed acceptance test / open weights
Validate the proposed llama route against the paper's reported outcome: Evaluated zero- and few-shot performance across knowledge, reasoning, reading, and code tasks. Assess the proposed LLaMA: Open and Efficient Foundation Language Models route through calibration, quantized behavior, license fit, and task quality, and treat open weights failures as their own llama decision input.
Proposed decision boundary / foundation model
Balance control, maintenance cost, and safety tuning before promoting the proposed foundation model design. Because for a later llama implementation, quality after quantization, fine-tuning drift, license fit, memory demand, domain shift, and serving latency define unresolved boundaries that require direct observation, adoption remains conditional on replay under BrokenGPT's selected model, runtime, and policy configuration.
Limitations, verification, and source
Boundaries recorded with the paper
Limitations
- The claim attached to LLaMA: Open and Efficient Foundation Language Models is conditional on evaluation coverage, prompt format, model revision, training-data disclosure, benchmark protocol, and contamination control, so it cannot be generalized from the paper title alone.
- For a later llama implementation, quality after quantization, fine-tuning drift, license fit, memory demand, domain shift, and serving latency define unresolved boundaries that require direct observation.
PRIMARY SOURCES
- 01LLaMA: Open and Efficient Foundation Language Models
Meta AI — Primary primary arXiv paper / 27 February 2023 / Hugo Touvron, Thibaut Lavril, Gautier Izacard, and 11 more