Meta Transparency Report

1. Data acquisition methods (Score: 1)

What methods does the developer use to acquire data used to build the model?

Disclosure:

We use a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

Training data is acquired via public datasets, crawling, licensing, and usage data from Meta's products and services.

Indicator notes:

Which of the following data acquisition methods does the developer use: 
 (i) acquiring existing public datasets, (ii) crawling the web, (iii) using data acquired via its existing products and services, (iv) licensing existing data from external parties, (v) having humans create or annotate new data, (vi) using models to generate new data,
 or (vii) other data acquisition methods not captured by the above. For example, if the developer uses reinforcement learning from human feedback to train models using model-generated outputs with human preference annotations, this would satisfy categories (v) and (vi). Alternatively, if the developer post-trains its model using off-the-shelf preference data (for example, the Alpaca dataset), this would satisfy category (i).

Example disclosure:

To build our model, we acquire data by crawling the Internet for publicly available data, licensing data from third-parties, and using models to synthetically generate new data. Humans do not create new data nor do we use data from our other products/services to train our model.
2. Public datasets (Score: 0)

What are the top-5 sources (by volume) of publicly available datasets acquired for building the model?

Disclosure:

Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The specific public datasets are not provided.

Indicator notes:

We define a source as the entity or means by which the developer acquires data. We define the top-5 sources as the top-5 sources by data volume.

Example disclosure:

We acquire publicly available data from only two sources: The Pile and CommonCrawl.
3. Crawling (Score: 1)

If data collection involves web-crawling, what is the crawler name and opt-out protocol?

Disclosure:

The Meta-ExternalAgent crawler crawls the web for use cases such as training AI models or improving products by indexing content directly. By configuring the robots.txt file on your website, you can specify to the Meta web crawlers how you would prefer them to interact with your site. In order to block these crawlers, add a disallow for the relevant crawler to robots.txt. The Meta-ExternalFetcher crawler may bypass robots.txt because it performs fetches that were requested by the user. Also, the FacebookExternalHit crawler might bypass robots.txt when performing security or integrity checks. User-agent: meta-externalagent Allow: / # Allow everything Disallow: /private/ # Disallow a specific directory https://developers.facebook.com/docs/sharing/webmasters/web-crawlers/

References:

Not disclosed

Score justification:

The user-agent meta-externalagent and the protocols for respecting robots.txt are provided.

Indicator notes:

We award this point for disclosure of the crawler name and opt-out protocols, including if/how they respect the Robots Exclusion Protocol (robots.txt).

Example disclosure:

Our web crawler is named A and information on the opt-out protocol can be found at this URL: ... The CommonCrawl web crawler is named CCBot and information on the opt-out protocol can be found at this URL: https://commoncrawl.org/faq#:~:text=How%20can%20I%20block%20the,%2Dagent%20string%20is%3A%20CCBot.
4. Usage data used in training (Score: 1)

What are the top-5 sources (by volume) of usage data from the developer's products and services that are used for building the model?

Disclosure:

Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. We do not use posts or comments with an audience other than Public for these purposes. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md https://www.facebook.com/privacy/guide/genai/

References:

Not disclosed

Score justification:

The three sources of usage data are Meta AI, Instagram, and Facebook.

Indicator notes:

We define usage data as data collected from the use of a developer's products or services.

Example disclosure:

We use usage data from only two sources: our deployed chatbot X and our online social media platform Y.
5. Notice of usage data used in training (Score: 1)

For the top-5 sources of usage data, how are users of these products and services made aware that this data is used for building the model?

Disclosure:

In the Privacy Center, users can learn more about how their public posts and comments on Meta Products are used to train generative AI models. We also disclose that Meta does not use posts or comments with an audience other than Public for these purposes. Users are able to choose who they share their content to based on this information and manage their past activity. https://www.facebook.com/privacy/dialog/your-public-content-genai

References:

Not disclosed

Score justification:

Users of Meta Products are notified via the Privacy Center that only Public posts are used for model training.

Indicator notes:

We define usage data notice as the proactive disclosure to users of how their data is used for model development. For example, via a pop-up with a description, a link to the privacy policy, or link to a description of company practices.

Example disclosure:

We notify users of our chatbot X that chatbot interactions are used to train our AI via a pop-up as shown at this URL: ... We notify users of our platform Y about whether their data is used to train our AI via a link to our privacy policy when they sign up for an account.
6. Licensed data sources (Score: 0)

What are the top-5 sources (by volume) of licensed data acquired for building the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We define a source as the entity from which the developer acquires data. For example, the Associated Press is reportedly a source of licensed data for OpenAI.

Example disclosure:

We license data from only three sources: A, B, and C.
7. Licensed data compensation (Score: 0)

For each of the top-5 sources of licensed data, are details related to compensation disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We award this point if the model developer describes the compensation structure specified in the contract with the data source or indicates they are prohibited from sharing this information if contractually mandated.

Example disclosure:

We compensate A by ... We cannot disclose information on compensation for our relationships with B and C due to contractual terms that prohibit public disclosure.
8. New human-generated data sources (Score: 0)

What are the top-5 sources (by volume) of new human-generated data for building the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We define a source as the entity or means by which the developer acquires data. For example, Scale AI could be a source of new human-generated data. By new, we mean the data is specifically acquired for the purposes of building the model.

Example disclosure:

We acquire new human-generated data from only two sources: our internal data annotation team and an external vendor, A.
9. Instructions for data generation (Score: 0)

For each of the top-5 sources of human-generated data, what instructions does the developer provide for data generation?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

The instructions should be those provided to the data source. For example, if a third-party vendor works directly with the data laborers to produce the data, the instructions from the developer to this vendor should be disclosed.

Example disclosure:

We instruct our internal data annotation team as follows: ... We instruct vendor A as follows: ...
10. Data laborer practices (Score: 0)

For the top-5 sources of human-generated data, how are laborers compensated, where are they located, and what labor protections are in place?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

For each data source, we require (i) the compensation in either USD or the local currency, (ii) any countries where at least 25% of the laborers are located, and (iii) a description of any labor protections. We will award this point if the developer discloses that it is not aware of data laborer practices.

Example disclosure:

Our internal data annotation team is located in the US, is compensated at 20 USD per hour, and deals with data that does not require specific protections. Our sole external data vendor contracts laborers in Kenya, compensates them at KES 15000 per month, and implements protections for dealing with toxic or unsafe content such as A and B.
11. Synthetic data sources (Score: 0)

What are the top-5 sources (by volume) of synthetic data acquired for building the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We define a source of synthetic data as a non-human mechanism (e.g. a machine learning model) used to generate the data.

Example disclosure:

We synthetically generate data using only our previous model X and an early checkpoint of our current flagship model Y.
12. Synthetic data purpose (Score: 0)

For the top-5 sources of synthetically generated data, what is the primary purpose for data generation?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We define a source of synthetic data as a non-human mechanism (e.g. a machine learning model) used to generate the data.

Example disclosure:

We use model X to generate instruction-tuning data and we use model Y to generate candidate responses that humans select between to provide human preference data for reinforcement learning with human feedback.
13. Data processing methods (Score: 0)

What are the methods the developer uses to process acquired data to determine the data directly used in building the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We will award this point for disclosure of all of the methods used to process acquired data. Data processing refers to any method that substantively changes the content of the data. For example, compression or changing the data file format is generally not in the scope of this indicator.

Example disclosure:

We process data in the following six-step pipeline: (i) removal of HTML artifacts, (ii) deduplication, (iii) language identification to retain English data, (iv) removal of CSAM imagery, (v) removal of train-test overlap, and (vi) tokenization.
14. Data processing purpose (Score: 0)

For each data processing method, what is its primary purpose?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

Data processing refers to any method that substantively changes the content of the data. For example, compression or changing the data file format is generally not in the scope of this indicator.

Example disclosure:

Examples of primary purposes for a data processing method could include: (i) removes low quality data, (ii) removes potentially personal/copyrighted data, (iii) removes product-irrelevant data, (iv) removes toxic data, (v) improves evaluation integrity, or (vi) prepares the data for training the model.
15. Data processing techniques (Score: 0)

For each data processing method, how does the developer implement the method?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

Data processing refers to any method that substantively changes the content of the data. For example, compression or changing the data file format is generally not in the scope of this indicator.

Example disclosure:

Examples of how a data processing method is implemented could include: the method (i) is implemented using an in-house regular expression, (ii) is implemented using an in-house tool based on n-gram overlap, (iii) is implemented using a FastText classifier trained on Wikipedia data, (iv) is implemented using hash collisions with the NCMEC database, (v) is implemented by searching for known benchmark canary strings, and (vi) is implemented using tiktoken (https://github.com/openai/tiktoken).
16. Data size (Score: 1)

Is the size of the data used in building the model disclosed?

Disclosure:

Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md"

References:

Not disclosed

Score justification:

22 trillion tokens.

Indicator notes:

To receive this point, the developer should report data size in appropriate units (e.g. bytes, words, tokens, images, frames) and broken down by modality. Data size should be reported to a precision of one significant figure (e.g. 4 trillion tokens, 200 thousand images). The size should reflect data directly used in building the model (i.e. training data) and not data that was acquired but unused, or data used to evaluate the model.

Example disclosure:

We used 3 x 10^12 tokens of text, 1 x 10^6 images, and 5 x 10^5 hours of audio for training.
17. Data language composition (Score: 0)

For all text data used in building the model, what is the composition of languages?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

To receive this point, the developer should report (i) all languages which make up at least 1% of the data and their corresponding proportions and (ii) a brief description of how languages are labeled (if a publicly available tool is used, include a link to the tool). Proportions should be reported to a precision of two significant figures and should describe proportions of documents labeled with some langauge. An "Unknown" category may be included to denote documents where the language could not be identified.

Example disclosure:

English 80%, Spanish 5.0%, French 3.0%, Chinese 2.0%, Unknown 10%. We used a FastText-based classifier trained on Wikipedia data to identify languages.
18. Data domain composition (Score: 0)

For all the data used in building the model, what is the composition of domains covered in the data?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

To receive this point, the developer should report the composition of the main domains included in the data used to train the model. This data should be at a level of granularity lower than broad claims about training on "internet data". For example, this could include the proportion of data from e-commerce, social media, news, code, etc. based on the URLs from which the data is sourced. Proportions should be reported to a precision of one significant figure.

Example disclosure:

Social media 40%, code repositories 30%, news articles 20%, e-commerce product listings 5%, scientific papers 5%.
19. External data access (Score: 0)

Does a third-party have direct access to the data used to build the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

By a third-party, we mean entities that are financially independent of the developer. We will award this point if at least one such entity is named as having direct access to the data. With that said, we may award this point if the developer provides justifications for prohibiting access to narrowly-scoped parts of the data.

Example disclosure:

Third-parties that have direct access to the data include organizations A and B.
20. Data replicability (Score: 0)

Is the data used to build the model described in enough detail to be externally replicable?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We will award this point if the description contains (i) a list of all publicly available training data and where to obtain it and (ii) a list of all training data obtainable from third parties and where to obtain it. These conditions refer to criteria 2 and 3 under the OSI Open Source AI v1.0 definition.

Example disclosure:

The listing of publicly available training data can be found at this URL ... and the listing of all training data obtainable from third parties can be found at this URL ...
21. Compute usage for final training run (Score: 0)

Is the amount of compute used in the model's final training run disclosed?

Disclosure:

Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The compute usage is not reported in FLOPs.

Indicator notes:

Compute should be reported in appropriate units, which most often will be floating point operations (FLOPs), along with a description of the measurement methodology, which may involve estimation. Compute should be reported to a precision of one significant figure (e.g. 5 x 10^25 FLOPs). This number should represent the compute used to train the final model across all model stages.

Example disclosure:

Our model was trained using 5 x 10^25 FLOPs, measured according to the Frontier Model Forum guidance provided at this URL: https://www.frontiermodelforum.org/updates/issue-brief-measuring-training-compute/
22. Compute usage including R&D (Score: 0)

Is the amount of compute used to build the model, including experiments, disclosed?

Disclosure:

Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The cumulative compute usage is not reported in FLOPs.

Indicator notes:

Compute should be reported in appropriate units, which most often will be floating point operations (FLOPs), along with a description of the measurement methodology, which may involve estimation. Compute should be reported to a precision of one significant figure (e.g. 7 x 10^26 FLOPs). Compared to the previous indicator, this indicator should include an estimation of the total compute used across experiments used towards the final training run for the model (such as including hyperparameter optimization or other experiments), and not just the final training run itself.

Example disclosure:

Our cumulative compute usage involved in building the model was 7 x 10^26 FLOPs, measured according to the Frontier Model Forum guidance provided at this URL: https://www.frontiermodelforum.org/updates/issue-brief-measuring-training-compute/
23. Development duration for final training run (Score: 0)

Is the amount of time required to build the model disclosed?

Disclosure:

Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The wall clock duration is not provided, though the hardware-time duration is provided.

Indicator notes:

The amount of time should be specified in terms of both the continuous duration of time required and the number of hardware hours used. The continuous duration of time required to build the model should be reported in weeks, days, or hours to a precision of one significant figure (e.g. 3 weeks). The number of hardware hours should be reported to a precision of one significant figure and include the type of hardware hours. No form of decomposition into phases of building the model is required for this indicator, but it should be clear what the duration refers to (e.g. training the model, or training and subsequent evaluation and red teaming).

Example disclosure:

Our model was trained over a period of 90 days using 4x10^4 NVIDIA H100 GPU-days.
24. Compute hardware for final training run (Score: 0)

For the primary hardware used to build the model, is the amount and type of hardware disclosed?

Disclosure:

Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The number and type of hardware units is not provided.

Indicator notes:

In most cases, this indicator will be satisfied by information regarding the number and type of GPUs or TPUs used to train the model. The number of hardware units should be reported to a precision of one significant figure (e.g. 800 NVIDIA H100 GPUs). We will not award this point if (i) the training hardware generally used by the developer is disclosed, but the specific hardware for the given model is not, or (ii) the training hardware is disclosed, but the amount of hardware is not. We will award this point even if information about the interconnects between hardware units is not disclosed.

Example disclosure:

Our model was trained using 1000 NVIDIA H100 GPUs.
25. Compute provider (Score: 1)

Is the compute provider disclosed?

Disclosure:

We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

Self-owned Meta cluster.

Indicator notes:

For example, the compute provider may be the model developer in the case of a self-owned cluster, a cloud provider like Microsoft Azure, Google Cloud Platform, or Amazon Web Services, or a national supercomputer. In the event that compute is provided by multiple sources or is highly decentralized, we will award this point if a developer makes a reasonable effort to describe the distribution of hardware owners.

Example disclosure:

Compute is provided by Google Cloud Platform.
26. Energy usage for final training run (Score: 0)

Is the amount of energy expended in building the model disclosed?

Disclosure:

Model Name Training Time (GPU hours) Training Power Consumption (W) Training Location-Based Greenhouse Gas Emissions (tons CO2eq) Training Market-Based Greenhouse Gas Emissions (tons CO2eq) Llama 4 Scout 5.0M 700 1,354 0 Llama 4 Maverick 2.38M 700 645 0 Total 7.38M - 1,999 0 https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The power is reported in W, which is not an appropriate unit for overall energy consumption.

Indicator notes:

Energy usage should be reported in appropriate units, which most often will be megawatt-hours (mWh), along with a description of the measurement methodology, which may involve estimation. Energy usage should be reported to a precision of one significant figure (e.g. 500 mWh). No form of decomposition into compute phases is required, but it should be clear whether the reported energy usage is for a single model run or includes additional runs, or hyperparameter tuning, or training other models like reward models, or other steps in the model development process that necessitate energy usage. If the developer is unable to measure or estimate this quantity due to information not being available from another party (e.g. compute provider), we will award this point if the developer explicitly discloses what information it lacks and why it lacks it.

Example disclosure:

Our model was trained using an estimate 1 x 10^4 MWh of energy. To estimate training energy consumption, we multiplied training FLOPs (5 x 10^25) by a conversion factor using NVIDIA A100 GPU information (3.74 × 10^21 FLOPs/MWh) given we train using FP16 with sparsity.
27. Carbon emissions for final training run (Score: 1)

Is the amount of carbon emitted in building the model disclosed?

Disclosure:

Estimated total location-based greenhouse gas emissions were 1,999 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

1999 tCO2eq

Indicator notes:

Emissions should be reported in appropriate units, which most often will be tons of carbon dioxide emitted (tCO2), along with a description of the measurement methodology, which may involve estimation. Emissions should be reported to a precision of one significant figure (e.g. 500 tCO2). No form of decomposition into compute phases is required, but it should be clear whether the reported emissions is for a single model run or includes additional runs, or hyperparameter tuning, or training other models like reward models, or other steps in the model development process that generate emissions. If the developer is unable to measure or estimate this quantity due to information not being available from another party (e.g. compute provider), we will award this point if the developer explicitly discloses what information it lack and why it lacks it. Emissions should correspond with the energy used in the previous indicator.

Example disclosure:

Our model yielded an estimate of 5 x 10^3 tCO2. To estimate training carbon emissions, we multiplied training energy usage (1 x 10^4 MWh) by a 2023 estimate for the US data center carbon intensity (0.375 tCO2/MWh) given the data centers used in training operate in the US.
28. Water usage for final training run (Score: 0)

Is the amount of clean water used in building the model disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

Clean water usage should be in appropriate units, which most often will be megaliters, along with a description of the measurement methodology, which may involve estimation. Clean water usage should be reported to a precision of one significant figure (e.g., 5000ML). No form of decomposition into compute phases is required, but it should be clear whether the reported water usage is for a single model run or includes additional runs, or hyperparameter tuning, or training other models like reward models, or other steps in the model development process that necessitates water usage. If the developer is unable to measure or estimate this quantity due to information not being available from another party (e.g. compute provider), we will award this point if the developer explicitly discloses what information it lacks and why it lacks it.

Example disclosure:

Our model yielded an estimate of 20 ML water. To estimate training water usage, we multiplied training energy usage (1 x 10^4 MWh) by a 2021 estimate for the US data center water efficiency (1.8 ML per 1,000 MWh) given the data centers used in training operate in the US.
29. Internal compute allocation (Score: 0)

How is compute allocated across the teams building and working to release the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

To receive a point, the developer should provide the compute allocated to each team involved in training the model. We understand there might be no clear allocation of compute across different teams; in that case, report an estimate of the compute used over the last year. Compute allocation should be reported to at least one significant figure.

Example disclosure:

- Safety — 15% - Pre-training — 60% - Post-training — 15% - Infrastructure and reliability — 5%
30. Model stages (Score: 0)

Are all stages in the model development process disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

Stages refer to each identifiable step that constitutes a substantive change to the model during the model building process. We recognize that different developers may use different terminology for these stages, or conceptualize the stages differently. We will award this point if there is a clear and complete description of these stages.

Example disclosure:

We define five stages in building the model: (1) unsupervised pre-training, (2) supervised instruction tuning, (3) RLHF, (4) domain-specific fine-tuning, and (5) final safety alignment.
31. Model objectives (Score: 0)

For all stages that are described, is there a clear description of the associated learning objectives or a clear characterization of the nature of this update to the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

We recognize that different developers may use different terminology for these stages, or conceptualize the stages differently. We will award this point if there is a clear description of the update to the model related to each stage, whether that is the intent of the stage (e.g. making the model less harmful), a mechanistic characterization (e.g. minimizing a specific loss function), or an empirical assessment (e.g. evaluation results conducted before and after the stage).

Example disclosure:

During unsupervised pre-training, the objective is next-token prediction. During supervised instruction tuning, we optimize for correctness and helpfulness on labeled tasks. RLHF aligns model outputs with human preference judgments. Domain-specific fine-tuning focuses on improving in-domain capabilities using specialized data (e.g., code or legal text). Final safety alignment reduces disallowed or harmful responses.
32. Code access (Score: 0)

Does the developer release code that allows third-parties to train and run the model?

Disclosure:

https://github.com/meta-llama/llama

References:

Not disclosed

Score justification:

The training code is not provided.

Indicator notes:

The released code does not need to match the code used internally.

Example disclosure:

We release training and inference code under an Apache 2.0 license at https://github.com/..., enabling others to replicate our core pipeline.
33. Organization chart (Score: 0)

How are employees developing and deploying the model organized internally?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

To receive a point, the developer should provide both the internal organization chart for the team developing the model as well as the headcounts (or a proportion of headcounts) by the team.

Example disclosure:

The model team comprises of 63 people, organized as follows: - CEO - Managing Director (Safety) — 24 people - Managing Director (Pre-training) — 12 people - Managing Director (Post-training) — 11 people - Managing Director (API) — 6 people - Director (Infrastructure and reliability) — 7 people - Director (PR and marketing) — 4 people - Director (hiring) — 7 people
34. Model cost (Score: 0)

What is the cost of building the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

No information provided.

Indicator notes:

Monetary cost should be reported in appropriate currency (e.g. USD), along with the measurement methodology, which may involve estimation. Cost should be reported to a precision of one significant figure (e.g. 200 million USD).

Example disclosure:

We spent approximately 200 million USD on building the model: 50 million for data acquisition, 10 million for data processing, 20 million for personnel, 80 million for compute for R&D priced at market rates, and 40 million for compute for the final training run priced at market rates.
35. Basic model properties (Score: 1)

Are all basic model properties disclosed?

Disclosure:

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Model developer: Meta Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality. Model Name Training Data Params Input modalities Output modalities Context length Token count Knowledge cutoff Llama 4 Scout (17Bx16E) A mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. Learn more in our Privacy Center. 17B (Activated) 109B (Total) Multilingual text and image Multilingual text and code 10M ~40T August 2024 Llama 4 Maverick (17Bx128E) 17B (Activated) 400B (Total) Multilingual text and image Multilingual text and code 1M ~22T August 2024 Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

All basic model properties are disclosed.

Indicator notes:

Basic model properties include: the input modality, output modality, model size, model components, and model architecture. To receive a point, all model properties should be disclosed. Modalities refer to the types or formats of information that the model can accept as input. Examples of input modalities include text, image, audio, video, tables, graphs. Model components refer to distinct and identifiable parts of the model. We recognize that different developers may use different terminology for model components, or conceptualize components differently. Examples include: (i) For a text-to-image model, components could refer to a text encoder and an image encoder, which may have been trained separately. (ii) For a retrieval-augmented model, components could refer to a separate retriever module. Model size should be reported in appropriate units, which generally is the number of model parameters, broken down by named component. Model size should be reported to a precision of one significant figure (e.g. 500 billion parameters for text encoder, 20 billion parameters for image encoder). Model architecture is the overall structure and organization of a foundation model, which includes the way in which any disclosed components are integrated and how data moves through the model during training or inference. We recognize that different developers may use different terminology for model architecture, or conceptualize the architecture differently; a sufficient disclosure includes any clear, though potentially incomplete, description of the model architecture.

Example disclosure:

Input modality: Text Output modality: Text Model components: Decoder-only model trained using self-supervised learning, followed by supervised fine tuning and RLHF that are used to align the language model to follow users' instructions and be helpful, harmless, and honest. Model size: 70B parameters Model architecture: Autoregressive (causal, decoder only) transformer language model with rotary position embeddings and are trained on the next token prediction task.
36. Deeper model properties (Score: 1)

Is a detailed description of the model architecture disclosed?

Disclosure:

https://github.com/meta-llama/llama

References:

Not disclosed

Score justification:

Code for the model is disclosed, which is sufficient for implementing the model architecture.

Indicator notes:

To receive a point, the model architecture should be described in enough detail to allow for an external entity to fully implement the model. Publicly available code or a configuration file for a model training library (e.g., GPT-NeoX) would be a sufficiently detailed description.

Example disclosure:

The configuration file for training our model using a public model training library A can be found at [URL].
37. Model dependencies (Score: 1)

Is the model(s) the model is derived from disclosed?

Disclosure:

These models are our best yet thanks to distillation from Llama 4 Behemoth, a 288 billion active parameter model with 16 experts that is our most powerful yet and among the world’s smartest LLMs. Llama 4 Behemoth outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. https://ai.meta.com/blog/llama-4-multimodal-intelligence/

References:

Not disclosed

Score justification:

The developer discloses model dependencies.

Indicator notes:

We will award this point for a comprehensive disclosure of the model or models on which the foundation model directly depends on or is derived from, as well as the method by which it was derived (e.g., through fine tuning, model merging, or distillation). Additionally, we will award a point if the developer discloses that the model is not dependent on or derived from any model.

Example disclosure:

This model is a fine tune of Camel-70B. We used the methods described in [PAPER URL] for distillation.
38. Benchmarked inference (Score: 0)

Is the compute and time required for model inference disclosed for a clearly-specified task on clearly-specified hardware?

Disclosure:

The model weights are openly available, any developer can benchmark inference

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

The duration should be reported in seconds to a precision of one significant figure (e.g. 0.002 seconds). Compute usage for inference should be reported in FLOPs/second to a precision of one significant figure (e.g. 5 x 10^21 FLOPs/second). The hardware in this evaluation need not be the hardware the developer uses for inference. The developer can report this figure over some known or public dataset.

Example disclosure:

It takes 0.002 seconds and 5 x 10^21 FLOPs/second to generate 100,000 tokens as 5,000 sequences of length 20 given inputs of length 40 from [DATASET URL]. The fixed set of hardware is 8 NVIDIA A100s.
39. Researcher credits (Score: 1)

Is a protocol for granting external entities API credits for the model disclosed?

Disclosure:

Researchers can directly request access to the Llama models at https://www.llama.com/llama-downloads/. Since it is an an accessible, open large language model (LLM) designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas, there is no need to request model credit usage. Researchers can also request access to the Llama API at https://www.llama.com/products/llama-api/

References:

Not disclosed

Score justification:

The developer discloses that they do not grant external entities API credits as the model weights are released openly.

Indicator notes:

A model credit access protocol refers to the steps, requirements, and considerations involved in granting credits to external entities. We will award this point if the developer discloses key details of its protocol, including (i) where external entities can request access to credits (e.g. via an access request form); (ii) explicit criteria for selecting external entities; and (iii) its policy on granting a transparent decision on whether access has been granted within a specified, reasonable period of time. Additionally, we will award a point if the developer discloses that it does not grant external entities API credits.

Example disclosure:

We implement a researcher access program: (i) Access can be requested from [URL] (ii) Any researcher at an accredited research institution is eligible to apply. Decisions are made based on the alignment between the applicant's project description and our target research directions (as described here: [URL]). (iii) Decision notifications are sent within three weeks of the application receipt.
40. Specialized access (Score: 0)

Does the developer disclose if it provides specialized access to the model?

Disclosure:

We provide our model weights to developers contingent on agreement with the Llama license. https://www.llama.com/llama4/license/

References:

Not disclosed

Score justification:

The developer does not disclose information on the entities provided with special access.

Indicator notes:

Specialized access could include several categories, such as early access, subsidized access, or deeper access (e.g., to model weights or checkpoints, that are not publicly available). We will award this point if the developer discloses (i) if it provides specialized access and (ii) statistics on the number of users granted access across academia, industry, non-profits, and governments, to one significant figure.

Example disclosure:

We provide early access to the model via API to: (1) 250 academics vetted by our program (2) 0 industry affiliates (3) 0 non-profit affiliates (3) 2 government entities with whom we have signed MoUs We provide no other specialized research access.
41. Open weights (Score: 1)

Are the model's weights openly released?

Disclosure:

https://llama.com/ Model weights are available at https://llama.com/. The license is available here. https://www.llama.com/llama4/license/

References:

Not disclosed

Score justification:

The download page is gated, but does not seem to substantially restrict access to the weights.

Indicator notes:

To receive this point, model weights need to be publicly available at no cost. Developers may receive this point even if there are some restrictions on the external entities that are permitted access (e.g. geographic restrictions), insofar as these restrictions are transparent (e.g. via a license or some high-level description of who has been granted access to the foundation model).

Example disclosure:

Model weights are available on HuggingFace by following this link: [URL]
42. Agent Protocols (Score: 0)

Are the agent protocols supported for the model disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

Agent protocols are specifications that define how autonomous agents exchange messages, context, or function calls with other agents, tools, or services (e.g., Anthropic’s Model Context Protocol (MCP) and Google’s Agent‑to‑Agent (A2A) spec). To earn this point, documentation must enumerate each protocol and describe any deviations or proprietary extensions.

Example disclosure:

We support MCP and A2A for agents built using model A
43. Capabilities taxonomy (Score: 1)

Are the specific capabilities or tasks that were optimized for during post-training disclosed?

Disclosure:

Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md These models are optimized for multimodal understanding, multilingual tasks, coding, tool-calling, and powering agentic systems. The models have a knowledge cutoff of August 2024. (https://www.llama.com/docs/model-cards-and-prompt-formats/llama4/)

References:

Not disclosed

Score justification:

The developer discloses the capabilities optimized for during post-training.

Indicator notes:

Capabilities refer to the specific and distinctive functions that the model can perform. We recognize that different developers may use different terminology for capabilities, or conceptualize capabilities differently. We will award this point for a list of capabilities specifically optimized for in the post-training phase of the model, even if some of the capabilities are not reflected in the final model.

Example disclosure:

We focus on the following capabilities during post-training: (1) Coding ability (2) Retrieval of information and factuality (3) Multilingual language proficiency on non-English languages (4) Tool-use
44. Capabilities evaluation (Score: 1)

Does the developer evaluate the model's capabilities prior to its release and disclose them concurrent with release?

Disclosure:

Pre-trained models Category Benchmark # Shots Metric Llama 3.1 70B Llama 3.1 405B Llama 4 Scout Llama 4 Maverick Reasoning & Knowledge MMLU 5 macro_avg/acc_char 79.3 85.2 79.6 85.5 MMLU-Pro 5 macro_avg/em 53.8 61.6 58.2 62.9 MATH 4 em_maj1@1 41.6 53.5 50.3 61.2 Code MBPP 3 pass@1 66.4 74.4 67.8 77.6 Multilingual TydiQA 1 average/f1 29.9 34.3 31.5 31.7 Image ChartQA 0 relaxed_accuracy No multimodal support 83.4 85.3 DocVQA 0 anls 89.4 91.6 Instruction tuned models Category Benchmark # Shots Metric Llama 3.3 70B Llama 3.1 405B Llama 4 Scout Llama 4 Maverick Image Reasoning MMMU 0 accuracy No multimodal support 69.4 73.4 MMMU Pro^ 0 accuracy 52.2 59.6 MathVista 0 accuracy 70.7 73.7 Image Understanding ChartQA 0 relaxed_accuracy 88.8 90.0 DocVQA (test) 0 anls 94.4 94.4 Code LiveCodeBench (10/01/2024-02/01/2025) 0 pass@1 33.3 27.7 32.8 43.4 Reasoning & Knowledge MMLU Pro 0 macro_avg/acc 68.9 73.4 74.3 80.5 GPQA Diamond 0 accuracy 50.5 49.0 57.2 69.8 Multilingual MGSM 0 average/em 91.1 91.6 90.6 92.3 Long Context MTOB (half book) eng->kgv/kgv->eng - chrF Context window is 128K 42.2/36.6 54.0/46.4 MTOB (full book) eng->kgv/kgv->eng - chrF 39.7/36.3 50.8/46.7 https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

The developer discloses evaluations for multiple capabilities: multimodal understanding (ChartQA), multilingual tasks (MGSM), coding (LiveCodeBench).

Indicator notes:

The evaluations must contain precise quantifications of the model's behavior in relation to the capabilities specified in the capabilities taxonomy. We will award this point for any clear, but potentially incomplete, evaluation of multiple capabilities.

Example disclosure:

We evaluate capabilities using the following benchmarks: (1) Coding: HumanEval (2) Retrieval: HotPotQA (3) Multilingual performance: MMMLU (4) Tool use: UltraTool
45. External reproducibility of capabilities evaluation (Score: 0)

Are code and prompts that allow for an external reproduction of the evaluation of model capabilities disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

The released code and prompts need not be the same as what is used internally, but should allow the developer's results on all capability evaluations to be reproduced. The released code must be open source, following the OSI definition of open source.

Example disclosure:

The code and prompts to reproduce our evaluations can be found on this GitHub repository link: [URL]
46. Train-test overlap (Score: 0)

Does the developer measure and disclose the overlap between the training set and the dataset used to evaluate model capabilities?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if, with every capability evaluation for which the developer reports results, the developer reports the overlap between the training set of the model and the dataset used for evaluation, as well as the general methodology for computing train-test overlap (e.g. a description of how n-gram matching was used).

Example disclosure:

We compute train-test overlap using n-gram matching using the procedure described here [URL]. We evaluate the train-test overlap for the following benchmarks: (1) Coding: HumanEval (1.6%) (2) Retrieval: HotPotQA (4%) (3) Multilingual performance: MMMLU (3%) (4) Tool use: UltraTool (9%)
47. Risks taxonomy (Score: 1)

Are the risks considered when developing the model disclosed?

Disclosure:

Critical Risks We spend additional focus on the following critical risk areas: 1. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area. 2. Child Safety We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. 3. Cyber attack enablement Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md#pre-trained-models

References:

Not disclosed

Score justification:

The developer discloses the risks considered.

Indicator notes:

Risks refer to possible negative consequences or undesirable outcomes that can arise from the model's deployment and usage. These consequences or outcomes may arise from model limitations (functions that the model cannot perform) or issues with the model's trustworthiness (e.g., its lack of robustness, reliability, calibration). We recognize that different developers may use different terminology for risks, or conceptualize risks differently. We will award this point for a complete list of risks considered, even if some of the risks are not reflected in the final model.

Example disclosure:

We consider the following risks when developing and evaluating the model: (1) Misinformation (2) Harassment (3) Cybersecurity risks (4) Bioweapons design (5) Revealing personally-identifiable information
48. Risks evaluation (Score: 0)

Does the developer evaluate the model's risks prior to its release and disclose them concurrent with release?

Disclosure:

We assess residual risk, taking into consideration the details of the risk assessment, the results of evaluations conducted throughout training, and the mitigations that have been implemented. The residual risk assessment is reviewed by the relevant research and/or product teams, as well as a multidisciplinary team of reviewers as needed. Informed by this analysis, a leadership team will either request further testing or information, require additional mitigations or improvements, or they will approve the model for release. https://ai.meta.com/static-resource/meta-frontier-ai-framework

References:

Not disclosed

Score justification:

The developer specifies that they perform assessments of residual risk, but does not disclose the results of these assessments. It's also unclear whether these risk assessments are made in relation to the risks taxonomy specified in the previous indicator and whether these assessments are precise quantifications of risk.

Indicator notes:

The evaluations must contain precise quantifications of the model's behavior in relation to the risks specified in the risk taxonomy. We will award this point for clear evaluations of the majority of the states risks.

Example disclosure:

We evaluate the risks for each of the above harms using HarmBench. The results (in terms of mean attack success rate) are: (1) Misinformation: 0.02 (2) Harassment: 0.01 (3) Cybersecurity: 0.10 (4) Bioweapons design (subset of Chemical Biological in HarmBench): 0.12 (5) Revealing personally-identifiable information (subset of General Harm in HarmBench): 0.02
49. External reproducibility of risks evaluation (Score: 0)

Are code and prompts to allow for an external reproduction of the evaluation of model risks disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

The released code and prompts need not be the same as what is used internally, but should allow the developer's results on all risk evaluations to be reproduced. The released code must be open-source, following the OSI definition of open-source.

Example disclosure:

The code and prompts to reproduce our evaluations can be found on this GitHub repository link: [URL]
50. Pre-deployment risk evaluation (Score: 0)

Are the external entities have evaluated the model pre-deployment disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

By external entities, we mean entities that are significantly or fully independent of the developer. We will award this point if the developer specifies the entity that carried out the pre-deployment analysis, discloses the terms of the analysis (such as conditions for releasing the evaluation results or the developer's control over the final results), as well as any financial transaction between the parties. We will award this point if the developer discloses no external entities have evaluated the model pre-deployment, or discloses only terms of the analysis where it is not bound by NDA while still naming all external entities.

Example disclosure:

We provide the following parties access to our model for pre-deployment capabilities evaluation: METR. METR has control over the release of the evaluation results (including whether or not to release the results and the contents of the results being released), but must provide the evaluation results to us for review before release. There are no financial transactions between us and METR.
51. External risk evaluation (Score: 0)

Are the parties contracted to evaluated model risks disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses statistics regarding all contracted parties that are responsible for evaluating risks (not limited to external entities or pre-deployment evaluation). This includes the number of contracted for-profit or non-profit entities, government entities, independent contractors, and researchers contracted by the developer to evaluate risks. We will award this point if the developer discloses it has no such contracts.

Example disclosure:

Contracted parties responsible for evaluating risks: (1) 2 contracting non-profits (2) 5 independent contractors (3) 0 government entities (4) 20 researchers
52. Mitigations taxonomy (Score: 0)

Are the post-training mitigations implemented when developing the model disclosed?

Disclosure:

For post-training, we apply a range of techniques to ensure our models conform to policies that are helpful to users and developers, including the right level of safety data at each stage. https://ai.meta.com/blog/llama-4-multimodal-intelligence/ Updated: For post-training, we apply a range of techniques to ensure our models conform to policies that are helpful to users and developers, including the right level of safety data at each stage. https://ai.meta.com/blog/llama-4-multimodal-intelligence/ Llama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities within a single safety classifier. Specifically, it combines the capabilities of the previous Llama Guard 3-8B and Llama Guard 3-11B-vision models by supporting English and multilingual text prompts (on the languages supported by Llama Guard 3) as well as mixed text-and-image prompts for image understanding. Unlike Llama Guard 3-11B-vision, Llama Guard 4 now supports safety classification when multiple images are given in the prompt as input. Llama Guard 4 is also integrated into the Llama Moderations API for text and images. https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard4/12B/MODEL_CARD.md

References:

Not disclosed

Score justification:

The developer specifies that, during post-training, they apply a "range of safety techniques ... including the right level of safety data at each stage", which is not a sufficiently specific list of mitigations. The developer also describes Llama Guard as a mitigation for risks. However, Llama Guard is better described as a post-deployment mitigation rather than a post-training mitigation as it's an independent model applied on prompts/model responses.

Indicator notes:

By post-training mitigations, we refer to interventions implemented by the developer during the post-training phase to reduce the likelihood and/or the severity of the model’s risks. We recognize that different developers may use different terminology for mitigations, or conceptualize mitigations differently. We will award this point for a complete list of mitigations considered, even if some of the mitigations are not reflected in the final model. Alternatively, we will award this point if the developer reports that it does not mitigate risk in this way.

Example disclosure:

We implement supervised fine tuning and reinforcement learning with human feedback to address model risks. We use no other methods to address risks.
53. Mitigations taxonomy mapped to risk taxonomy (Score: 0)

Does the developer disclose how the post-training mitigations map onto the taxonomy of risks?

Disclosure:

Updated: "The Llama Guard 4 model is trained to predict safety labels on the categories shown below, based on the MLCommons safety taxonomy. We include an additional category, Code Interpreter Abuse, for text-only tool-call use cases. Hazard categories S1: Violent Crimes S2: Non-Violent Crimes S3: Sex-Related Crimes S4: Child Sexual Exploitation S5: Defamation S6: Specialized Advice S7: Privacy S8: Intellectual Property S9: Indiscriminate Weapons S10: Hate S11: Suicide & Self-Harm S12: Sexual Content S13: Elections S14: Code Interpreter Abuse (text only) https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard4/12B/MODEL_CARD.md"

References:

Not disclosed

Score justification:

The developer describes risk evaluation results for Llama Guard. However, Llama Guard is better described as a post-deployment mitigation rather than a post-training mitigation as it's an independent model applied on prompts/model responses.

Indicator notes:

We will award this point for a complete mapping of the primary risk that each mitigation is meant to address, even if the mitigation potentially maps on to other risks in the taxonomy. Alternatively, we will award this point if the developer reports that it does not mitigate risk.

Example disclosure:

We use supervised fine tuning for general instruction following. We use RLHF to reduce the model's propensity to output information about cybercrimes, bioweapons, disinformation, content harassing someone, and PII.
54. Mitigations efficacy (Score: 0)

Does the developer evaluate and disclose the impact of post-training mitigations?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses the results on the risk evaluations before and after the post-training mitigations are applied. Alternatively, we will award this point if the developer reports that it does not mitigate risk in this way.

Example disclosure:

Pre-mitigations (measured through mean attack success rate on HarmBench): (1) Misinformation: 0.80 (2) Harassment: 0.91 (3) Cybersecurity risks: 0.56 (4) Bioweapons design (subset of Chemical Biological in HarmBench): 0.62 (5) Personally-identifiable information (subset of General Harm in HarmBench): 0.52 Post-mitigations (measured through mean attack success rate on HarmBench): (1) Misinformation: 0.02 (2) Harassment: 0.01 (3) Cybersecurity risks: 0.10 (4) Bioweapons design (subset of Chemical Biological in HarmBench): 0.12 (5) Personally-identifiable information (subset of General Harm in HarmBench): 0.02
55. External reproducibility of mitigations evaluation (Score: 0)

Are code and prompts to allow for an external reproduction of the evaluation of post-training mitigations disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

The released code and prompts need not be the same as what is used internally, but should allow the developer's results on all mitigations evaluations to be reproduced. The released code must be open-source, following the OSI definition of open-source. Alternatively, we will award this point if the developer reports that it does not mitigate risk.

Example disclosure:

We release the code and prompts for reproducing post-training mitigation evaluations at this GitHub link: [URL]
56. Model theft prevention measures (Score: 0)

Does the developer disclose the security measures used to prevent unauthorized copying (“theft”) or unauthorized public release of the model weights?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

This indicator assesses the developer's disclosures regarding how it addresses the risk that malicious actors or insiders could exfiltrate or replicate proprietary weights. Security measures could include insider threat analysis and detection, in addition to external threat management. Examples of such measures include encryption at rest, key management, remote attestation, or auditing for suspicious queries. We will award a point if the developer discloses specific steps taken to safeguard the model weights or that none are implemented.

Example disclosure:

We store model weights on encrypted volumes with hardware-based key management. We monitor inference queries for suspicious patterns (like repeated attempts to reconstruct weights token-by-token), and we audit all staff access logs monthly.
57. Release stages (Score: 0)

Are the stages of the model's release disclosed?

Disclosure:

We provide our model weights to all developers as long as they abide by the Llama license. https://www.llama.com/llama4/license/

References:

Not disclosed

Score justification:

The disclosure does not discuss release stages.

Indicator notes:

Release stages include A/B testing, release on a user-facing product, GA release, open-weight release, etc. We recognize that the release of a foundation model falls along a spectrum, with many forms of partial release, and that different developers may conceptualize release differently. We will award a point if the developer provides a clear identification of the stages through which the model was released.

Example disclosure:

We began with an internal alpha test for two weeks, followed by a closed beta with selected enterprise partners for one month, then a public waitlisted preview, and finally a general availability release once thresholds on safety benchmarks were met.
58. Risk thresholds (Score: 1)

Are risk thresholds disclosed?

Disclosure:

https://ai.meta.com/static-resource/meta-frontier-ai-framework

References:

Not disclosed

Score justification:

The developer discloses risk thresholds.

Indicator notes:

Risk thresholds determine when a risk level is unacceptably high to a developer (e.g. leading to the decision to not release a model), moderately high (e.g. triggering additional safety screening), or low enough to permit normal usage. We will award this point if the developer discloses explicit risk thresholds that clarify (i) which harmful outcomes are being scored, (ii) how the scores are computed (in general terms, not necessarily disclosing internal algorithms), and (iii) what triggers an action to block, delay, or otherwise modify a model's release. Alternatively, we will award a point if the developer discloses that it does not consider explicit risk thresholds during model release.

Example disclosure:

Our risk threshold for biorisks is the ability to autonomously create bioweapons. Current models score a medium: they don't autonomously create bioweapons but could help a skilled practitioner with access to materials in speeding up creation of bioweapons. Risk thresholds higher than medium would delay the model's release until the risk level drops to medium or below.
59. Versioning protocol (Score: 0)

Is there a disclosed protocol for versioning and deprecation of the model?

Disclosure:

We have classes of models and with every update, the corresponding model name is based on the model family and model version (ex: Llama 3, Llama 3.1, Llama 3.2)

References:

Not disclosed

Score justification:

The developer's disclosure describes their versioning protocol. However, it does not include information about how their deprecation/communication protocol.

Indicator notes:

We will award a point if the developer discloses how model versions are labeled, updated, deprecated, and communicated to users.

Example disclosure:

We version models based on the date of release: e.g., ModelName-11-01-2024. We additionally provide ModelName-latest, corresponding to the latest release. We deprecate versions of models when we plan to remove access to with a six months notice to users. Users should respond to model deprecation by switching to the newest version of the models or an equivalent non-deprecated model. Users can switch to a different model by replacing the model identifier (to e.g., ModelName-latest for the latest version) in API calls or through the Python SDK.
60. Change log (Score: 1)

Is there a disclosed change log for the model?

Disclosure:

Model weights are openly available and versioned when new versions are introduced As part of the Llama 3.1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. Please use the following repos going forward: llama-models - Central repo for the foundation models including basic utilities, model cards, license and use policies PurpleLlama - Key component of Llama Stack focusing on safety risks and inference time mitigations llama-toolchain - Model development (inference/fine-tuning/safety shields/synthetic data generation) interfaces and canonical implementations llama-agentic-system - E2E standalone Llama Stack system, along with opinionated underlying interface, that enables creation of agentic applications llama-cookbook - Community driven scripts and integrations https://github.com/meta-llama/llama?tab=readme-ov-file

References:

Not disclosed

Score justification:

The developer provides a number of repositories and links to documentation the combined together can help users trace changes in the model.

Indicator notes:

We will award a point if the developer publishes a version-by-version record of new features, fixes, or performance improvements.

Example disclosure:

On 11/1/2024 (version ModelName-11-01-2024), we improved model reasoning in technical domains. This resulted in a 20-point increase on the MATH benchmark (from 62% to 82%). Past change logs can be viewed at [URL]
61. Foundation model roadmap (Score: 0)

Is a forward-looking roadmap for upcoming models, features, or products disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

A foundation model roadmap is a transparent statement about how the developer intends to evolve or expand its LLM offerings, including upcoming models, major feature releases, or expanded products based on the model, along with approximate timelines or version milestones. It can be high-level (e.g., “new model Q2 2025”), but must exist publicly.

Example disclosure:

We plan to release ModelX2 in Q2 2025, featuring enhanced multilingual capabilities and improved retrieval. We also aim to launch an enterprise-specific product tier for regulated industries by early 2026.
62. Top distribution channels (Score: 0)

Are the top-5 distribution channels for the model disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We define distribution channels to be either an API provider (a pathway by which users can query the model with inputs and receive outputs) or a model distributor (a pathway by which model weights are released). We recognize that distribution channels may arise without the knowledge of the model developer. For example, the weights of a model may be released through one distribution channel and then be distributed through other channels. Distribution channels can be ranked by any reasonable metric (e.g., number of queries, number of downloads, number of users, revenue). A description of the metric should be provided. API providers and model distributors may be ranked separately using different metrics as long as the total number of distribution channels equals five (if five distribution channels exist). For example, the developer may choose to disclose the top-3 API providers (ranked by the number of queries) and the top-2 model distributors (ranked by the number of downloads).

Example disclosure:

We provide API access to the model through A, B, and C. We distribute model weights through D and E. We pick the top-3 API providers based on the average number of queries per month and the top-2 model weight providers based on the average number of downloads per month.
63. Quantization (Score: 1)

Is the quantization of the model served to customers in the top-5 distribution channels disclosed?

Disclosure:

The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md#pre-trained-models

References:

Not disclosed

Score justification:

Although the developer does not disclose the distribution channels, this information likely applies to most distribution channels.

Indicator notes:

We will award this point for a disclosure of the model precision in each of the top-5 distribution channels.

Example disclosure:

We serve the model at 16-bit precision on all distribution channels.
64. Terms of use (Score: 1)

Are the terms of use of the model disclosed?

Disclosure:

https://www.llama.com/llama4/license/ https://www.llama.com/llama4/use-policy/

References:

Not disclosed

Score justification:

A terms of service that appears to apply to the bulk of the model's distribution channels is disclosed.

Indicator notes:

We define terms of use to include terms of service and model licenses. We will award this point for a pointer to the terms of service or model license. In the event that model's licenses are written more generally, it should be clear which assets they apply to. We recognize that different developers may adopt different business models and therefore have different types of model licenses. Examples of model licenses include responsible AI licenses, open-source licenses, and licenses that allow for commercial use. Terms of service should be disclosed for each of the top-5 distribution channels. However, we will award this point if there are terms-of-service that appear to apply to the bulk of the model’s distribution channels.

Example disclosure:

Our terms of service are published at https://ourcompany.com/model-tos - these terms cover both our API and all distribution channels for model weights.
65. Distribution channels with usage data (Score: 0)

What are the top-5 distribution channels for which the developer has usage data?

Disclosure:

Meta's Family of Apps.

References:

Not disclosed

Score justification:

The specific distribution channels with usage data are not named.

Indicator notes:

We define distribution channels to be either an API provider (a pathway by which users can query the model with inputs and receive outputs) or a model distributor (a pathway by which model weights are released). We recognize that distribution channels may arise without the knowledge of the model developer. For example, the weights of a model may be released through one distribution channel and then be distributed through other channels. Distribution channels can be ranked by any reasonable metric (e.g., number of queries, number of downloads, number of users, revenue). A description of the metric should be provided. We define usage data as any form of developer-exclusive data collected from any of a developer's distribution channel. A developer has access to usage data from a distribution channel if it is able to use that data for downstream purposes (e.g., analytics, training etc.). Usage data may be shared outside of the developer, but it is initially collected by the distribution channel and shared to the developer.

Example disclosure:

We have access to usage data through the distribution channels: A, B, and C.
66. Amount of usage (Score: 0)

For each of the top-5 distribution channels, how much usage is there?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

Usage should be reported as the number of queries over the span of a month, reported to the precision of one significant figure (e.g., 50 million queries).

Example disclosure:

Distribution channel A: 50 million queries. Distribution channel B: 10 million queries. Distribution channel C: 10 million queries.
67. Classification of usage data (Score: 0)

Is a representative, anonymized dataset classifying queries into usage categories disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

Developers may either share a fully public dataset or a partially restricted dataset (e.g., under a research license). We will award this point if there is a clear, aggregated or sample dataset that reveals categories of tasks/queries.

Example disclosure:

We provide quarterly releases of an anonymized dataset that classifies user queries into 20 broad job-related categories. Researchers can request access via [URL]. We ensure no PII is included.
68. Data retention and deletion policy (Score: 0)

Is a policy for data retention and deletion disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

A data retention and deletion policy is a policy for removing particular data from the training set and/or preventing it from being used if there is a user or external request (e.g., “right to be forgotten”) that also covers internal data governance. This includes whether there is a formal process to delete or retract data from future training runs and how long raw data is retained. It also clarifies how quickly deletions propagate to the model (e.g., “only in subsequent major model releases”).

Example disclosure:

We honor verified user requests to delete personal data from our training corpus by removing it from any subsequent scheduled retraining. Our data retention policy ensures chat logs are purged after 90 days.
69. Geographic statistics (Score: 0)

Across all forms of downstream use, are statistics of model usage across geographies disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if there is a meaningful, though potentially incomplete or vague, disclosure of geographic usage statistics at the country-level.

Example disclosure:

We share anonymized per-country usage metrics in a publicly accessible dashboard, updated monthly, on this link: [link]
70. Internal products and services (Score: 0)

What are the top-5 internal products or services using the model?

Disclosure:

Meta's Family of Apps.

References:

Not disclosed

Score justification:

The specific products and services are not named.

Indicator notes:

An internal product or service is a product or service built by the developer. Products or services can be ranked by any reasonable metric (e.g., number of users, queries, revenue). A description of the metric should be provided.

Example disclosure:

The model is used in products A, B, C, D, and E. We choose products based on the number of montly active users.
71. External products and services (Score: 0)

What are the top-5 external products or services using the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

An external product or service is a product or service built by a party external to the developer. Products or services can be ranked by any reasonable metric (e.g., number of users, queries, revenue). A description of the metric should be provided. We will award a point if the developer discloses that that it does not have access to such metrics about external products or services.

Example disclosure:

The model is used in products A, B, C, D, and E. We choose products based on the number of montly active users.
72. Users of internal products and services (Score: 0)

How many monthly active users are there for each of the top-5 internal products or services using the model?

Disclosure:

Meta AI has 1 billion monthly active users across Meta's Family of Apps.

References:

Not disclosed

Score justification:

Needs information about specific applications to suffice.

Indicator notes:

An internal product or service is a product or service built by the developer. The number of users refers to users who engaged or interacted with the model through the internal product or service over the last month or averaged over the last X months (this should be specified). Number of users should be specified to one significant figure (e.g. 100,000).

Example disclosure:

Over the last 6 months, the total monthly active users for our top-5 products using model Y are: Product A: 100,000 users Product B: 30,000 users Product C: 10,000 users Product D: 10,000 users Product E: 10,000 users
73. Consumer/enterprise usage (Score: 0)

Across all distribution channels for which the developer has usage data, what portion of usage is consumer versus enterprise?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

Consumer usage refers to usage by individual consumers. Enterprise usage refers to usage by enterprise customers (including government use). Consumer and enterprise usage should be calculated in terms of the number of queries by or the amount of revenue from consumer or enterprise users. Percentages should be specified to two significant digits (e.g., 12% consumer, 88% enterprise).

Example disclosure:

12% of the usage of model A across all distribution channels is from consumers, 88% is from enterprise users. Of this 88%, 6% is from users at governments. Usage is calculated based on number of queries.
74. Enterprise users (Score: 0)

Across all distribution channels for which the developer has usage data, what are the top-5 enterprises that use the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

Enterprises should be ranked by the number of queries made or the amount of revenue from usage since the model's release. We will also award this point if the developer indicates it does not have access to enterprise usage data.

Example disclosure:

The top-5 enterprises are A, B, C, D, and E. The enterprises are selected based on the number of queries.
75. Government use (Score: 0)

What are the 5 largest government contracts for use of the model?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

This includes known government contracts of enterprise or government-specific products and services that use the model. We will award this point if the developer discloses its top five government contracts ranked monetary value, though the developer may omit contracts where it is under NDA regarding the existence of the contract.

Example disclosure:

The five largest government users of our service, along with their use cases, are: 1. County A is utilizing our product for improving access to internal resources 2. National Lab B is using our model to advance bioscientific research. 3. Federal agency C is using our product to deliver faster, more accurate translation services 4. City D is participating in a pilot program found our product helped reduce the time spent on routine tasks 5. Country E is using our product to summarize legal documents in their lower courts.
76. Benefits Assessment (Score: 1)

Is an assessment of the benefits of deploying the model disclosed?

Disclosure:

https://www.llama.com/llama-ai-innovation

References:

Not disclosed

Score justification:

Meta links to its Llama innovation page, which includes a large number of case studies on the impact of using its models; e.g. in using Llama 4 Maverick 17B CodeGPT created 30%+ time savings for developers (/; https://www.llama.com/resources/case-studies/codegpt/)

Indicator notes:

We will award this point for any quantitative assessment of the benefits or potential benefits of deploying the model.

Example disclosure:

We analyze the impact of using the model in education outcomes using a randomized controlled trial in third grade math assignnments, and find that use in the classroom improves standardized test outcomes by 26%. [Link to report.]
77. AI bug bounty (Score: 1)

Does the developer operate a public bug bounty or vulnerability reward program under which the model is in scope?

Disclosure:

https://bugbounty.meta.com/

References:

Not disclosed

Score justification:

Meta AI is included in the bug bounty

Indicator notes:

We will award this point for a publicly documented bug bounty or vulnerability reward program describing (i) in-scope vulnerabilities (e.g., prompt bypasses, data leaks), (ii) out-of-scope items, (iii) submission process, and (iv) reward tiers or recognition if applicable. We will award a point if the developer discloses it has no AI bug bounty that encourages external researchers to report security, privacy, or adversarial vulnerabilities in the model.

Example disclosure:

We run a bug bounty program with HackerOne. We award up to $5,000 for critical vulnerabilities, such as discovering a major exploit that circumvents our content filters or reveals private data. [link to bug bounty]
78. Responsible disclosure policy (Score: 1)

Does the developer clearly define a process by which external parties can disclose model vulnerabilities or flaws?

Disclosure:

https://bugbounty.meta.com/

References:

Not disclosed

Score justification:

The bug bounty includes a responsible research and disclosure policy https://bugbounty.meta.com/terms/. Also states "You give us reasonable time to investigate and mitigate an issue you report before publicly disclosing any information about the report or sharing such information with others."

Indicator notes:

We will award this point for a description of the process external parties can use for responsbly disclosing model vulnerabilities and flaws, which should include (i) what mechanism external parties can use to disclose vulnerabilities or flaws (e.g., a form, an email) and (ii) what process follows a disclosure (e.g., how much time must parties wait until public release). This is often included with a bug bounty, but can also be standalone. We will award a point if the developer discloses it has no responsible disclosure policy.

Example disclosure:

We maintain a responsible disclosure policy at [URL] that describes how external parties can disclose vulnerabilities and flaws in Model A, including a 45-day disclosure window and an official contact for urgent security vulnerabilities.
79. Safe harbor (Score: 0)

Does the developer disclose its policy for legal action against external evaluators conducting good-faith research?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses whether it has a policy committing it to not pursue legal action against external evaluators conducting good-faith research. This should not be only for software security vulnerabilities, but also AI flaws, and it should be based on researcher conduct standards, not at the sole discretion of the company. We will award this point if the developer provides a clear description of its policy regarding such protections for external researchers, or lack thereof.

Example disclosure:

We do not have a policy for researcher protections for good-faith safety research. OR Our policy ensures no legal action against good‐faith researchers who follow our disclosure guidelines, see: [link]
80. Security incident reporting protocol (Score: 0)

Are major security incidents involving the model disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

A security incident reporting protocol provides post-deployment transparency about serious incidents or breaches. Security incidents refer to incidents where external security threats affect the model (e.g., data breaches or DDoS attacks on the service). We will award this point if the developer states (i) how to submit a security incident report, (ii) how quickly it will respond, and (iii) when and whether results are disclosed. Every incident need not be reported publicly, but the developer must disclose a policy determining how incidents are reported and disclosed.

Example disclosure:

We publish a public ‘Security Incident Report’ on our website for any confirmed security incident affecting the model within 7 days of a patch being implemented. Users and researchers can report incidents via security@ourcompany.com, and we commit to an initial acknowledgment within 48 hours.
81. Misuse incident reporting protocol (Score: 0)

Are misuse incidents involving the model disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

A misuse incident reporting protocol provides post-deployment transparency about incidents of misuse involving the model. As opposed to the previous indicator, this indicator is about actors misusing the model to cause real-world harm, such as misinformation operations or cybersecurity attacks. We will award this point if the developer states (i) how to submit a misuse incident report, (ii) how quickly it will respond, and (iii) when and whether results are disclosed. Every incident need not be reported publicly, but there needs to be a policy governing how incidents are reported.

Example disclosure:

We publish a public ‘Misuse Incident Report’ on our website for any confirmed misuse incident within 7 days of a patch being implemented. Users and researchers can report incidents regarding our flagship foundation model via security@ourcompany.com, and we commit to an initial acknowledgment within 48 hours.
82. Post-deployment coordination with government (Score: 0)

Does the developer coordinate evaluation with government bodies?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer specifies which government bodies it is coordinating with and for what types of post-deployment evaluations. Government bodies include AI Safety Institutes, national security agencies, national labs, and international governmental enties such as UN agencies or the G7. Evaluation here may also include sharing of the developer's proprietary evaluation results for help with interpretation.

Example disclosure:

We do not coordinate with any government entities or AI Safety Institutes. OR We coordinate with the UK AISI for post-deployment evaluation of cyber, CB, and autonomy-related capabilities.
83. Feedback mechanisms (Score: 0)

Does the developer disclose a way to submit user feedback? If so, is a summary of major categories of feedback disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer (i) discloses how users can submit feedback (e.g., via a form or a thumbs up/thumbs down for model responses) and (ii) discloses aggregated or categorized feedback data (e.g. a categorization of thumbs up and thumbs down data).

Example disclosure:

Users can submit feedback at this url: [URL] We find that users mainly report issues with API call response times, over-refusals from models, and outdated information in model outputs. A detailed categorization of user reports is available at [URL]
84. Permitted, restricted, and prohibited model behaviors (Score: 0)

Are model behaviors that are permitted, restricted, and prohibited disclosed?

Disclosure:

For post-training, we apply a range of techniques to ensure our models conform to policies that are helpful to users and developers, including the right level of safety data at each stage. https://ai.meta.com/blog/llama-4-multimodal-intelligence/ Llama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities within a single safety classifier. Specifically, it combines the capabilities of the previous Llama Guard 3-8B and Llama Guard 3-11B-vision models by supporting English and multilingual text prompts (on the languages supported by Llama Guard 3) as well as mixed text-and-image prompts for image understanding. Unlike Llama Guard 3-11B-vision, Llama Guard 4 now supports safety classification when multiple images are given in the prompt as input. Llama Guard 4 is also integrated into the Llama Moderations API for text and images. https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard4/12B/MODEL_CARD.md The Llama Guard 4 model is trained to predict safety labels on the categories shown below, based on the MLCommons safety taxonomy. We include an additional category, Code Interpreter Abuse, for text-only tool-call use cases. Hazard categories S1: Violent Crimes S2: Non-Violent Crimes S3: Sex-Related Crimes S4: Child Sexual Exploitation S5: Defamation S6: Specialized Advice S7: Privacy S8: Intellectual Property S9: Indiscriminate Weapons S10: Hate S11: Suicide & Self-Harm S12: Sexual Content S13: Elections S14: Code Interpreter Abuse (text only) https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard4/12B/MODEL_CARD.md

References:

Not disclosed

Score justification:

Response discloses model behaviors that are permitted, but doesn't disclose how Llama Guard is applied to the model's outputs.

Indicator notes:

We refer to a policy that includes this information as a model behavior policy, or a developer's policy on what the foundation model can and cannot do (e.g. such a policy may prohibit a model from responding to NSFW content). We recognize that different developers may adopt different business models and that some business models may make enforcement of a model behavior policy more or less feasible. We will award this point if at least two of the three categories (i.e. permitted, restricted, and prohibited model behaviors) are disclosed. Alternatively, we will award this point if the developer reports that it does not impose any restrictions on its model's behavior in this way.

Example disclosure:

We allow responses from Model A that include broad Q&A, restrict sexual or harassing content, and prohibit facilitating illegal or violent acts. More details can be found in our guidelines for model behavior here: [link]
85. Model response characteristics (Score: 1)

Are desired model response characteristics disclosed?

Disclosure:

Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. Tone We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md

References:

Not disclosed

Score justification:

Llama 4 Model Card describes steps taken to adjust the model's tone

Indicator notes:

Model response characteristics include default behaviors or behaviors that the developer steers the model to take. These may include being helpful, taking an objective point of view, or using tools only when necessary. We will award points for a clear description of desired model response characteristics or a statement that there are no such characteristics.

Example disclosure:

We configure responses from Model A to be factual, neutral, and contextually helpful, avoiding personal or biased opinions. More details can be found in our guidelines for model behavior here: [link]
86. System prompt (Score: 1)

Is the default system prompt for at least one distribution channel disclosed?

Disclosure:

https://www.llama.com/docs/model-cards-and-prompt-formats/llama4/

References:

Not disclosed

Score justification:

Meta suggests a potential system prompt - it appears there is no inbuilt system prompt by default. Confirmation of this would be helpful clarification

Indicator notes:

A system prompt is defined as the prompt provided to the system by default that guides the system's behavior. We will award this point for the disclosure of the verbatim text of the full system prompt as well as an explanation for the context in which the system prompt is used.

Example disclosure:

We disclose our default prompt for Model A via our chat interface: ‘You are a helpful AI assistant providing clear, accurate, and policy‐compliant responses.’
87. Intermediate tokens (Score: 1)

Are intermediate tokens used to generate model outputs available to end users?

Disclosure:

Model weights are openly available and all outputs are observable to the deployer.

References:

Not disclosed

Score justification:

All outputs are observable to the deployer

Indicator notes:

Intermediate tokens are defined as any tokens generated by the model before the final output is shown to the user, such as model chains of thought. We will also award this point if a summary of intermediate tokens is made available to end users. If intermediate tokens or summaries are not made available, the developer should provide a justification.

Example disclosure:

Model A is trained to generate intermediate chain-of-thought reasoning, but we withhold most chain-of-thought tokens from final user-facing responses to prevent model distillation. We do disclose chains-of-thought for a small set of research collaborators under NDA.
88. Internal product and service mitigations (Score: 0)

For internal products or services using the model, are downstream mitigations against adversarial attacks disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

An internal product or service is a product or service built by the developer. Adversarial attacks include prompt injection, jailbreaking, or malicious queries. Mitigations against adversarial attacks might include specialized prompt filtering, content scanning, or real-time monitoring of queries or accounts. We will award this point if the developer discloses a clear statement of methods used (e.g., a specialized prompt sanitizer or adversarial pattern detector), or if the developer states it does not implement such product-level mitigations against adversarial attacks.

Example disclosure:

In our chatbot, we implement a second-stage content filter that checks user inputs for disallowed topics and attempts to sanitize adversarial prompts. We also log suspicious prompts for manual review.
89. External developer mitigations (Score: 1)

Does the developer provide built-in or recommended mitigations against adversarial attacks for downstream developers?

Disclosure:

At the system-level, we have open-sourced several safeguards which can help identify and guard against potentially harmful inputs and outputs. These tools can be integrated into our Llama models and with other third-party tools: Llama Guard: Our input/output safety large language model based on the hazards taxonomy we developed with MLCommons. Developers can use it to detect whether inputs or outputs violate the policies they’ve created for their specific application. Prompt Guard: A classifier model trained on a large corpus of attacks, which is capable of detecting both explicitly malicious prompts (Jailbreaks) as well as prompts that contain inject inputs (Prompt Injections). CyberSecEval: Evaluations that help AI model and product developers understand and reduce generative AI cybersecurity risk. We’ve heard from developers that these tools are most effective and helpful when they can be tailored to their applications. We provide developers with an open solution so they can create the safest and most effective experiences based on their needs. We’ll also continue working with a global set of partners to create industry-wide system standards that benefit the open source community. https://ai.meta.com/blog/llama-4-multimodal-intelligence/

References:

Not disclosed

Score justification:

LlamaGuard suffices

Indicator notes:

Downstream developers are developers who access the model through a distribution channel. Adversarial attacks include prompt injection, jailbreaking, or malicious queries. Mitigations against adversarial attacks that developers might build in or recommend include content filtering endpoints and recommended prompt templates. We will award this point if the developer discloses (i) technical mitigations (e.g., a developer provided moderation API or classifier) it offers or implements, (ii) recommended best practices or libraries for downstream developers, or (iii) an explicit statement that it does not build or recommend any particular downstream mitigations in this way..

Example disclosure:

Our API includes an optional parameter that will automatically filter user prompts and model outputs for hateful or disallowed content. We also publish guidelines for building robust chat interfaces that resist common prompt injections.
90. Enterprise mitigations (Score: 1)

Does the developer disclose additional or specialized mitigations for enterprise users?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

Meta's previous references to LlamaGuard suffice

Indicator notes:

Enterprise users are, for example, large organizations with dedicated service agreements or users of enterprise-specific API deployments or products and services. Additional or specialized mitigations may address enterprise needs such as data privacy controls, advanced prompt/response monitoring, or compliance checks with regulations such as GDPR or HIPAA. Additional or specialized mitigations may include single-tenant deployments, custom filters for specific regulated industries, or advanced logging for compliance. We will award a point if the developer at least describes these mitigations or states that it does not provide such additional or specialized enterprise mitigations.

Example disclosure:

Our enterprise offering for Model A includes a dedicated environment with stricter filtering, a HIPAA-compliant data retention policy, and the ability for enterprise admins to define custom blacklisted topics that the model must refuse.
91. Detection of machine-generated content (Score: 1)

Are mechanisms that are used for detecting content generated by this model disclosed?

Disclosure:

Llama Generated Audio Detector & Llama Audio Watermark Detector: Designed to detect AI-generated content, these tools will help organizations detect AI-generated threats, such as scams, fraud, and phishing attempts. At launch, we’re working with ZenDesk, Bell Canada, and AT&T to integrate these into their systems. If interested in learning more, other organizations can request information by visiting the Llama Defenders Program website. https://ai.meta.com/blog/ai-defenders-program-llama-protection-tools/

References:

Not disclosed

Score justification:

Response suffices as watermarking technique

Indicator notes:

A mechanism for detecting machine-generated content might include storing a copy of all outputs generated by the model to compare against, implementing a watermark on model outputs, adding cryptographic metadata (such as C2PA), or training a detector post-hoc to identify such content. We will award this point if any such mechanism is disclosed or if the developer reports that it does not have or use any such mechanism.

Example disclosure:

We train a classifier using model generations and human-written text to identify machine-generated content from Model A and our other models.
92. Documentation for responsible use (Score: 1)

Does the developer provide documentation for responsible use by downstream developers?

Disclosure:

https://www.llama.com/developer-use-guide/

References:

Not disclosed

Score justification:

Llama developer use guide suffices

Indicator notes:

To receive a point, the developer should provide documentation for responsible use. This might include details on how to adjust API settings to promote responsible use, descriptions of how to implement mitigations, or guidelines for responsible use. We will also award this point if the developer states that it does not provide any such documentation. For example, the developer might state that the model is offered as is and downstream developers are accountable for using the model responsibly.

Example disclosure:

Our Developer Documentation Hub consolidates integration guides, responsible‐use guidelines, and best practices: [link]
93. Permitted and prohibited users (Score: 1)

Is a description of who can and cannot use the model on the top-5 distribution channels disclosed?

Disclosure:

https://www.llama.com/llama4/license/ https://www.llama.com/llama4/use-policy/

References:

Not disclosed

Score justification:

License prohibits users with 700mn+ AUM; AUP prohibits EU users of multimodal modals. Additional Commercial Terms. If, on the Llama 4 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.

Indicator notes:

We will award this point for a description of the company's policies for permitted and prohibitted users on its top-5 distribution channels. We will award this point if the developer has a more general acceptable use policy that it confirms applies across these distribution channels. We will award this point if there are no restrictions on users.

Example disclosure:

We allow usage by individuals 13 years of age or older who accept our Terms of Service. We prohibit use by export controlled entities or persons on denied-parties lists or in countries under U.S. embargo. We also reserve the right to restrict use if users engage in targeted harassment. For example, we only permit users over 13 with valid credentials, and prohibit usage from OFAC-sanctioned regions. We do not allow state-sponsored disinformation agencies to access our services.
94. Permitted, restricted, and prohibited uses (Score: 1)

Which uses are explicitly allowed, conditionally permitted, or strictly disallowed under the acceptable use policy for the top-5 distribution channels?

Disclosure:

https://www.llama.com/llama4/license/ https://www.llama.com/llama4/use-policy/

References:

Not disclosed

Score justification:

AUP describes many prohibited uses

Indicator notes:

We will award this point for a rough characterization of two or more of permitted, restricted, and prohibited uses across the top-5 distribution channels. We will award this point if the developer has a more general acceptable use policy that it confirms applies across these distribution channels. We will award this point if there are no restrictions on users.

Example disclosure:

Permitted uses include general conversational queries, brainstorming, and coding assistance. Restricted uses include adult or violent content that requires caution or additional review. Prohibited uses include facilitating illicit activity, disinformation campaigns, or harassment. For example, we permit typical user requests like Q&A, text generation, and educational uses. We restrict content that depicts graphic violence or sexual content by applying additional filters. We prohibit any use aiming to conduct unlawful surveillance, promote extremist violence, or defraud others.
95. AUP enforcement process (Score: 0)

What are the methods used by the developer to enforce the acceptable policy?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses the processes (automated or manual) it uses to detect, review, and respond to potential acceptable use policy violations. We will award this point for a reasonable best-effort attempt to provide the bulk of this information, though one line indicating the developer reserves the right to terminate accounts is insufficient. Alternatively, we will award this point if the developer reports that it does not use such methods to enforce its acceptable use policy.

Example disclosure:

We combine automated checks with human review for severe or repeated violations, issuing warnings or suspensions after repeat violations.
96. AUP enforcement frequency (Score: 0)

Are statistics on the developer's AUP enforcement disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses enforcement statistics (e.g., violation counts or actions taken) from its enforcement of its acceptable use policy. Alternatively, we will award this point if the developer reports that it does not enforce its acceptable use policy.

Example disclosure:

We publish a quarterly enforcement report detailing violation counts by prohibited use category and the corresponding actions taken at [LINK]
97. Regional policy variations (Score: 0)

Are differences in the developer's acceptable use or model behavior policy across geographic regions disclosed?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses distinctions in its AUP or MBP and provides examples of differences in multiple specific regions, or states that no differences exist. For example, some jurisdictions impose content restrictions beyond those in the developer’s global policy that may necessesitate local deviations.

Example disclosure:

In the EU, our model automatically omits certain categories of political content to comply with local election laws. In all other regions, we follow the general global AUP at [URL].
98. Oversight mechanism (Score: 0)

Does the developer have an internal or external body that reviews core issues regarding the model prior to deployment?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses that is has such an internal or external body and provides some description of its scope, or alternatively if the developer discloses that it has no such body. An oversight mechanism covers governance structure beyond mere external risk evaluation, asking whether a formal body regularly reviews design and deployment decisions. Core issues may include model objectives, data usage, or risk mitigation.

Example disclosure:

We convene a monthly advisory board of ethicists, civil society representatives, and academics to review training processes and identify new risks. The board's recommendations regarding deployment are not binding.
99. Whistleblower protection (Score: 0)

Does the developer disclose a whistleblower protection policy?

Disclosure:

Not disclosed

References:

Not disclosed

Score justification:

The developer does not disclose this information.

Indicator notes:

We will award this point if the developer discloses (i) the existence of a whistleblower protection policy, (ii) what protections are afforded to whistleblowers, (iii) how reports are handled and investigated, and (iv) any external oversight of the whistleblower protection process. This might include protections for whistleblowers who report safety, ethical, or legal concerns related to the model. We will also award this point if the developer discloses that it has no such policy.

Example disclosure:

We maintain a whistleblower protection policy that prohibits retaliation against employees who report safety or ethical concerns about our models. Reports can be submitted anonymously through our ethics hotline, are reviewed by an independent board committee, and whistleblowers are entitled to legal representation provided by the company. Our policy is audited annually by an independent ethics consultancy.
100. Government commitments (Score: 0)

What commitments has the developer made to government bodies?

Disclosure:

Our Frontier AI Framework outlines our consideration of risk in our model-release decisions, in line with the commitment we made at last year’s global AI Seoul Summit. https://about.fb.com/news/2025/02/meta-approach-frontier-ai/

References:

Not disclosed

Score justification:

Doesn't acknowledge other commitments made, such as White House voluntary commitments.

Indicator notes:

We will award this point if the company provides an exhaustive list of commitments it has made to government bodies in the jurisdictions where it offers its model.

Example disclosure:

We have committed to the White House Voluntary Committments and the Seoul Committments.