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Meta Llama 3.1 8B

$\"Model$ $\"Model$

Model Information

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

This model is ready for commercial use.

Model Developer: Meta

Model Release Date: July 23, 2024

License A custom commercial license, the Llama 3.1 Community License, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go here.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see the link to the Llama 3.1 model card.

Llama 3.1 Systems

Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.

As part of our responsible release approach, we provide the community with safeguards that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our reference implementations demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.

New Capabilities

Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.

Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.

Multilinguality: Llama 3.1 supports seven (7) languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.

Intended Use

Intended Use Cases Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 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 3.1 Community License allows for these use cases.

Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.

**Note: Llama 3.1 has been trained on a broader collection of languages than the 10 supported languages.

Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.

Model Details

Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback\n(RLHF) to align with human preferences for helpfulness and safety.

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	Training Data	Params	Input modalities	Output modalities	Context Length	GQA	Token count	Knowledge cutoff
		8B	Multilingual Text	Multilingual Text and code	128k	Yes	15T+	December 2023
Llama 3.1 (text only)	A new mix of publicly available online data.	70B	Multilingual Text	Multilingual Text and code	128k	Yes	15T+	December 2023
		405B	Multilingual Text	Multilingual Text and code	128k	Yes	15T+	December 2023

Input

Input Format: Text\nInput Parameters: Temperature, TopP

Output

Output Format: Text and code\nOutput Parameters: Max output tokens

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Llama 3.1 family of models. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

Getting Started With `.nemo`

Step 1: Fetching the model file

You can download the model from NGC using the CLI tool.

ngc registry model download-version nvidia/nemo/llama-3_1-8b-nemo:1.0\n

(Optional, Recommended) Pre-extract the model from the .nemo file. This will reduce the model loading time overhead because the NeMo scripts will not have to extract the model from the .nemo everytime they are run. This step is not required though, and NeMo scripts can still ingest the .nemo file directly as well.

mkdir llama3_1_8B\ntar -xvf llama3_1_8b_instruct.nemo -C llama3_1_8B\n

\n
You can find the instructions to install and configure the NGC CLI tool here
\n

Step 2: Fetch the NeMo Framework container

You can use the NeMo Framework container available on NGC which comes preloaded with all the required dependencies.

export NEMO_IMAGE=\"nvcr.io/nvidia/nemo:24.05.01\"\ndocker pull $NEMO_IMAGE\n

Step 3: Run the NeMo container

Run the NeMo framework container, mounting the model.

# Full path to the extracted model directory\nexport MODEL_DIR=$(pwd)/llama3_1_8B\ndocker run --gpus all -it --rm  -v ${MODEL_DIR}:/model $NEMO_IMAGE\n

Hardware And Software

Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.

Training Energy Use Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (Thermal Design Power (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.

Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 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 renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.

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	Training Time (GPU hours)	Training Power Consumption (W)	Training Location-Based Greenhouse Gas Emissions (tons CO2eq)
Llama 3.1 8B	1.46M	700	420
Llama 3.1 70B	7.0M	700	2,040
Llama 3.1 405B	30.84M	700	8,930
Total	39.3M	-	11,390

The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.

Training Data

Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.

Data Freshness: The pretraining data has a cutoff of December 2023.

Benchmarks - English Text

In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.

Base pretrained models

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Category	Benchmark	# Shots	Metric	Llama 3 8B	Llama 3.1 8B	Llama 3 70B	Llama 3.1 70B	Llama 3.1 405B
General	MMLU	5	macro_avg/acc_char	66.7	66.7	79.5	79.3	85.2
General	MMLU PRO (CoT)	5	macro_avg/acc_char	36.2	37.1	55.0	53.8	61.6
General	AGIEval English	3-5	average/acc_char	47.1	47.8	63.0	64.6	71.6
General	CommonSenseQA	7	acc_char	72.6	75.0	83.8	84.1	85.8
General	Winogrande	5	acc_char	-	60.5	-	83.3	86.7
General	BIG-Bench Hard (CoT)	3	average/em	61.1	64.2	81.3	81.6	85.9
General	ARC-Challenge	25	acc_char	79.4	79.7	93.1	92.9	96.1
Knowledge reasoning	TriviaQA-Wiki	5	em	78.5	77.6	89.7	89.8	91.8
Reading comprehension	SQuAD	1	em	76.4	77.0	85.6	81.8	89.3
Reading comprehension	QuAC (F1)	1	f1	44.4	44.9	51.1	51.1	53.6
Reading comprehension	BoolQ	0	acc_char	75.7	75.0	79.0	79.4	80.0
Reading comprehension	DROP (F1)	3	f1	58.4	59.5	79.7	79.6	84.8

Instruction Tuned Models

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Category	Benchmark	# Shots	Metric	Llama 3 8B Instruct	Llama 3.1 8B Instruct	Llama 3 70B Instruct	Llama 3.1 70B Instruct	Llama 3.1 405B Instruct
General	MMLU	5	macro_avg/acc	68.5	69.4	82.0	83.6	87.3
General	MMLU (CoT)	0	macro_avg/acc	65.3	72.7	80.9	85.9	88.6
General	MMLU PRO (CoT)	5	micro_avg/acc_char	45.5	48.3	63.4	65.1	73.3
Reasoning	ARC-C	0	acc	82.4	83.4	94.4	94.8	96.9
Reasoning	GPQA	0	em	34.6	30.4	39.5	41.7	50.7
Reasoning	MuSR	0	correct	56.3	45.7	55.1	58.1	56.7
Steerability	IFEval			76.8	80.4	82.9	87.5	88.6
Code	HumanEval	0	pass@1	60.4	72.6	81.7	80.5	89.0
Code	MBPP ++ base version	0	pass@1	70.6	72.8	82.5	86.0	88.6
Math	GSM-8K (CoT)	8	em_maj1@1	80.6	84.5	93.0	95.1	96.8
Math	MATH (CoT)	0	final_em	29.1	51.9	51.0	68.0	73.8
Tool Use	API-Bank	0	acc	83.6	82.6	85.1	90.0	92.0
Tool Use	Berkeley Function Calling	0	acc	76.1	76.1	83.0	85.1	88.5
Tool Use	Gorilla Benchmark API Bench	0	acc	8.8	8.2	14.7	29.7	35.3
Tool Use	Nexus (0-shot)	0	macro_avg/acc	37.6	38.5	47.8	56.7	58.7
Multilingual	Multilingual MGSM	8	em	-	68.2	-	85.6	90.3

Multilingual Benchmarks

Category Benchmark Language Llama 3.1 8B Llama 3.1 70B Llama 3.1 405B

Portuguese 62.12 80.13 84.95

Spanish 62.45 80.05 85.08

Italian 61.63 80.4 85.04

German 60.59 79.27 84.36

French 62.34 79.82 84.66

Hindi 50.88 74.52 80.31

Thai 50.32 72.95 78.21

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Category	Benchmark	Language	Llama 3.1 8B	Llama 3.1 70B	Llama 3.1 405B
		Portuguese	62.12	80.13	84.95
		Spanish	62.45	80.05	85.08
		Italian	61.63	80.4	85.04
General	MMLU (5-shot, macro_avg/acc)	German	60.59	79.27	84.36
		French	62.34	79.82	84.66
		Hindi	50.88	74.52	80.31
		Thai	50.32	72.95	78.21

Llama 3.1 Instruct

Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.

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 And Tone

Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.

Evaluations

We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.

Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.

Responsibility & Safety

As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:

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Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
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Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
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Provide protections for the community to help prevent the misuse of our models.
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Responsible Deployment

Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta's Llama models have been responsibly deployed can be found in our Community Stories webpage. Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the Responsible Use Guide to learn more.

Red Teaming

For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.

Critical And Other Risks

We specifically focused our efforts on mitigating 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, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.

2. Child Safety

Child Safety risk assessments were conducted using a team of experts, to assess the model's capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.

3. Cyber Attack Enablement

Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B's social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security white paper to learn more.

Community

Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.

We also set up the Llama Impact Grants program to identify and support the most compelling applications of Meta's Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found here.\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.

Ethical Considerations And Limitations

The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.

But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our Responsible Use Guide, Trust and Safety solutions, and other resources to learn more about responsible development.

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The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.\n\nThis model is ready for commercial use.\n\n**Model Developer**: Meta\n\n**Model Release Date:** July 23, 2024 \n\n**License** A custom commercial license, the Llama 3.1 Community License, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE \n\nWhere to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](ttps://github.com/meta-llama/llama-recipes).\n\n## Third-Party Community Consideration \nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see the link to the Llama 3.1 model card. \n\n## Llama 3.1 Systems\n\n**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. \n\nAs part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.\n\n## New Capabilities\n\nNote that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. \n\n**Tool-use:** Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. \n\n**Multilinguality:** Llama 3.1 supports seven (7) languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.\n\n## Intended Use\n\n**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 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 3.1 Community License allows for these use cases.\n\n**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**. \n\n**Note: Llama 3.1 has been trained on a broader collection of languages than the 10 supported languages. \n\nDevelopers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.\n\n\n## Model Details\n\n**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback\n(RLHF) to align with human preferences for helpfulness and safety.\n\n| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Token count | Knowledge cutoff |\n|-|-|-----------------------|----------------------------------------------|-----------------------|---------------------|-----------------------|-------|---------------|\n| | | 8B | Multilingual Text | Multilingual Text and code| 128k | Yes | 15T+ | December 2023 |\n| Llama 3.1 (text only) | A new mix of publicly available online data. | 70B | Multilingual Text | Multilingual Text and code| 128k | Yes | 15T+ | December 2023 |\n| | | 405B | Multilingual Text | Multilingual Text and code| 128k | Yes | 15T+ | December 2023 |\n\n## Input\n\n**Input Format:** Text\n**Input Parameters:** Temperature, TopP\n\n## Output\n\n**Output Format:** Text and code\n**Output Parameters:** Max output tokens\n\n**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.\n\n**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.\n\n**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. \n\n## Getting Started With `.nemo`\n\n### Step 1: Fetching the model file\n1. You can download the model from NGC using the CLI tool.\n\n```bash\nngc registry model download-version nvidia/nemo/llama-3_1-8b-nemo:1.0\n```\n\n2. (Optional, Recommended) Pre-extract the model from the `.nemo` file. This will reduce the model loading time overhead because the NeMo scripts will not have to extract the model from the `.nemo` everytime they are run. This step is not required though, and NeMo scripts can still ingest the `.nemo` file directly as well.\n\n```bash\nmkdir llama3_1_8B\ntar -xvf llama3_1_8b_instruct.nemo -C llama3_1_8B\n```\n\n> You can find the instructions to install and configure the NGC CLI tool [here](https://org.ngc.nvidia.com/setup/installers/cli)\n\n### Step 2: Fetch the NeMo Framework container\nYou can use the NeMo Framework [container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo) available on NGC which comes preloaded with all the required dependencies.\n\n```bash\nexport NEMO_IMAGE=\"nvcr.io/nvidia/nemo:24.05.01\"\ndocker pull $NEMO_IMAGE\n```\n\n### Step 3: Run the NeMo container\nRun the NeMo framework container, mounting the model.\n\n```bash\n# Full path to the extracted model directory\nexport MODEL_DIR=$(pwd)/llama3_1_8B\ndocker run --gpus all -it --rm -v ${MODEL_DIR}:/model $NEMO_IMAGE\n```\n\n## Hardware And Software\n\n**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. \n\n**Training Energy Use** Training utilized a cumulative of **39.3**M GPU hours of computation on H100-80GB (Thermal Design Power (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.\n\n**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** 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 renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.\n\n| | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |\n| - |---------------------------------------|---------------------------------------|---------------------------|--------|\n| Llama 3.1 8B | 1.46M | 700 | 420 | 0 |\n| Llama 3.1 70B | 7.0M | 700 | 2,040 | 0 |\n| Llama 3.1 405B | 30.84M | 700 | 8,930 | 0 |\n| Total | 39.3M | - | 11,390 | 0 |\n\nThe methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.\n\n## Training Data\n\n**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.\n\n**Data Freshness:** The pretraining data has a cutoff of December 2023.\n\n## Benchmarks - English Text\n\nIn this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.\n\n### Base pretrained models\n| Category | Benchmark | # Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |\n|--------------------------|---------------|--------------------|----------|------------|--------------|-------------|---------------|----------------|\n| General | MMLU | 5 | macro_avg/acc_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 | |\n| General | MMLU PRO (CoT) | 5 | macro_avg/acc_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 | |\n| General | AGIEval English | 3-5 | average/acc_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 | |\n| General | CommonSenseQA | 7 | acc_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 |\n| General | Winogrande | 5 | acc_char | - | 60.5 | - | 83.3 | 86.7 | |\n| General | BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | **85.9** | |\n| General | ARC-Challenge | 25 | acc_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 | |\n| Knowledge reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |\n| Reading comprehension | SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 | |\n| Reading comprehension | QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 | |\n| Reading comprehension | BoolQ | 0 | acc_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 |\n| Reading comprehension | DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | **84.8** | |\n\n### Instruction Tuned Models\n\n\n| Category | Benchmark | # Shots | Metric | Llama 3 8B Instruct | Llama 3.1 8B Instruct | Llama 3 70B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct | \n| --- | --- | --- | --- | --- | --- | --- | --- | --- | \n | General | MMLU | 5 | macro_avg/acc | 68.5 | 69.4 | 82.0 | 83.6 | 87.3 | \n | General | MMLU (CoT) | 0 | macro_avg/acc | 65.3 | 72.7 | 80.9 | 85.9 | 88.6 | \n | General | MMLU PRO (CoT) | 5 | micro_avg/acc_char | 45.5 | 48.3 | 63.4 | 65.1 | 73.3 | \n | Reasoning | ARC-C | 0 | acc | 82.4 | 83.4 | 94.4 | 94.8 | **96.9** | \n | Reasoning | GPQA | 0 | em | 34.6 | 30.4 | 39.5 | 41.7 | 50.7 | \n | Reasoning | MuSR | 0 | correct | 56.3 | 45.7 | 55.1 | 58.1 | 56.7 | \n | Steerability | IFEval | | | 76.8 | 80.4 | 82.9 | 87.5 | **88.6** | \n | Code | HumanEval | 0 | pass@1 | 60.4 | 72.6 | 81.7 | 80.5 | 89.0 | \n | Code | MBPP ++ base version | 0 | pass@1 | 70.6 | 72.8 | 82.5 | 86.0 | 88.6 | \n | Math | GSM-8K (CoT) | 8 | em_maj1@1 | 80.6 | 84.5 | 93.0 | 95.1 | 96.8 | \n | Math | MATH (CoT) | 0 | final_em | 29.1 | 51.9 | 51.0 | 68.0 | 73.8 | \n | Tool Use | API-Bank | 0 | acc | 83.6 | 82.6 | 85.1 | 90.0 | 92.0 | \n | Tool Use | Berkeley Function Calling | 0 | acc | 76.1 | 76.1 | 83.0 | 85.1 | **88.5** |\n | Tool Use | Gorilla Benchmark API Bench | 0 | acc | 8.8 | 8.2 | 14.7 | 29.7 | 35.3 | \n | Tool Use | Nexus (0-shot) | 0 | macro_avg/acc | 37.6 | 38.5 | 47.8 | 56.7 | **58.7** | \n | Multilingual | Multilingual MGSM | 8 | em | - | 68.2 | - | 85.6 | 90.3 |\n\n## Multilingual Benchmarks\n\nCategory Benchmark Language Llama 3.1 8B Llama 3.1 70B Llama 3.1 **405B**\n\nPortuguese 62.12 80.13 84.95\n\nSpanish 62.45 80.05 85.08\n\nItalian 61.63 80.4 85.04\n\nGerman 60.59 79.27 84.36\n\nFrench 62.34 79.82 84.66\n\nHindi 50.88 74.52 80.31\n\nThai 50.32 72.95 78.21\n\n| Category | Benchmark | Language | Llama 3.1 8B | Llama 3.1 70B | Llama 3.1 405B | \n| --- | --- | --- | --- | --- | --- | \n| | | Portuguese | 62.12 | 80.13 | 84.95 |\n| | | Spanish | 62.45 | 80.05 | 85.08 |\n| | | Italian | 61.63 | 80.4 | 85.04 | \n| General | MMLU (5-shot, macro_avg/acc) | German | 60.59 | 79.27 | 84.36 | \n| | | French | 62.34 | 79.82 | 84.66 | \n| | | Hindi | 50.88 | 74.52 | 80.31 | \n| | | Thai | 50.32 | 72.95 | 78.21 |\n\n\n## Llama 3.1 Instruct\n\nOur main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.\n\n### Fine-Tuning Data\n\nWe 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.\n\n### Refusals And Tone\n\nBuilding on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.\n\n## Evaluations\n\nWe evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. \n\nCapability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.\n\n## Responsibility & Safety\n\nAs part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:\n- Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.\n\n- Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.\n\n- Provide protections for the community to help prevent the misuse of our models.\n\n## Responsible Deployment\n\nLlama is a foundational technology designed to be used in a variety of use cases, examples on how Meta's Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.\n\n\n## Red Teaming\n\nFor both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.\n\n## Critical And Other Risks\n\nWe specifically focused our efforts on mitigating the following critical risk areas: \n\n ### 1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness\n To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.\n\n### 2. Child Safety\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model's capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.\n\n### 3. Cyber Attack Enablement\n\nOur cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B's social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security white paper to learn more.\n\n## Community\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). \n\nWe also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta's Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). \nFinally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.\n\n## Ethical Considerations And Limitations\n\nThe core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. \n\nBut Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.","displayName","Llama 3.1 8B",["D",1744228979198],"publisher","Meta","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/llama-3_1-8b-nemo","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":240,"_241":240,"_242":243,"_244":245},"NSPECT-PK1V-L2N7","value","filter",{"_246":245,"_241":240},"category","label","field",{"_107":1421,"_109":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":258,"_138":282,"_1364":1365,"_1366":56,"_1367":86,"_1368":86,"_1369":1370,"_1371":86,"_1372":1373},"amplitude",{"_1352":1353,"_953":-7,"_1354":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1228":224,"_1229":1230,"_1231":1232,"_1233":1234},"announcements",[],"consentState",{"_319":1218,"_1219":86,"_1220":31,"_1221":31,"_1222":1223},"consentConfig",{"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320},"showContactAdmin",1784111759588,[],[],[296,297,298,299,300],{"_45":60,"_47":71,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504],[343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359],{"_360":466,"_206":466,"_167":224,"_333":467},{"_360":461,"_206":461,"_167":224,"_333":462},{"_360":457,"_206":457,"_167":224,"_333":458},{"_360":452,"_206":452,"_167":224,"_333":453},{"_360":447,"_206":447,"_167":224,"_333":448},{"_360":435,"_206":435,"_167":224,"_333":436},{"_360":423,"_206":423,"_167":224,"_333":424},{"_360":418,"_206":418,"_167":224,"_333":419},{"_360":407,"_206":407,"_167":224,"_333":408},{"_360":402,"_206":402,"_167":224,"_333":403},{"_360":397,"_206":397,"_167":224,"_333":398},{"_360":392,"_206":392,"_167":224,"_333":393},{"_360":387,"_206":387,"_167":224,"_333":388},{"_360":382,"_206":382,"_167":224,"_333":383},{"_360":377,"_206":377,"_167":224,"_333":378},{"_360":370,"_206":370,"_167":224,"_333":371},{"_360":361,"_206":361,"_167":224,"_333":362},"hostName","amazon-adsystem.com",[363,364],{"_78":369,"_366":361,"_367":31,"_167":368},{"_78":365,"_366":361,"_367":31,"_167":368},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[372,373],{"_78":376,"_366":370,"_367":31,"_167":375},{"_78":374,"_366":370,"_367":31,"_167":375},"IDE","This domain is owned by Doubleclick (Google). 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. 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Information collected with these cookies is used to measure and find ways to improve website 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Meta Llama 3.1 8B

Model Information

Third-Party Community Consideration

Llama 3.1 Systems

New Capabilities

Intended Use

Model Details

Input

Output

Getting Started With .nemo

Step 1: Fetching the model file

Step 2: Fetch the NeMo Framework container

Step 3: Run the NeMo container

Hardware And Software

Training Data

Benchmarks - English Text

Base pretrained models

Instruction Tuned Models

Multilingual Benchmarks

Llama 3.1 Instruct

Fine-Tuning Data

Refusals And Tone

Evaluations

Responsibility & Safety

Responsible Deployment

Red Teaming

Critical And Other Risks

1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness

2. Child Safety

3. Cyber Attack Enablement

Community

Ethical Considerations And Limitations

Getting Started With `.nemo`