Llama 2
Fine-tune & deploy Meta's Llama 2 models on Vertex AI.Note: Use of this model is governed by the Meta license. See the License tab.
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama-2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.
Model Developers Meta
Variations Llama 2 comes in a range of parameter sizes - 7B, 13B, and 70B - as well as pretrained and fine-tuned variations.
Input Models input text only.
Output Models generate text only.
Model Architecture Llama 2 is an auto-regressive language optimized transformer. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
Training Data | Params | Content Length | GQA | Tokens | LR | |
---|---|---|---|---|---|---|
Llama 2 | A new mix of publicly available online data | 7B | 4k | ✗ | 2.0T | 3.0 x 10^-4 |
Llama 2 | A new mix of publicly available online data | 13B | 4k | ✗ | 2.0T | 3.0 x 10^-4 |
Llama 2 | A new mix of publicly available online data | 70B | 4k | ✔ | 2.0T | 1.5 x 10^-4 |
Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B - use Grouped-Query Attention (GQA) for improved inference scalability.
Model Dates Llama 2 was trained between January 2023 and July 2023.
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.
License A custom commercial license is available. Please see the License tab.
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, or by opening an issue in the GitHub repository.
Before you click the Open notebook button at the top of this model card page, use Cloud Shell or your local terminal to enter the command line prompt below to copy the Llama 2 model artifacts to a Cloud Storage bucket.
Preview
This feature is a preview offering, subject to the "Pre-GA Offerings Terms" of the Service Specific Terms. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.The Fast Deployment feature prioritizes speed for model exploration, making it ideal for initial testing and experimentation. For sensitive data or production workloads, use the Standard environment for enhanced security and stability.
Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta's sustainability program.
Time (GPU hours) | Power Consumption (W) | Carbon Emitted(tCO2eq) | |
---|---|---|---|
Llama 2 7B | 184320 | 400 | 31.22 |
Llama 2 13B | 368640 | 400 | 62.44 |
Llama 2 70B | 1720320 | 400 | 291.42 |
Total | 3311616 | 539.00 |
CO2 emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
Model | Size | Code | Commonsense Reasoning | World Knowledge | Reading Comprehension | Math | MMLU | BBH | AGI Eval |
---|---|---|---|---|---|---|---|---|---|
Llama 1 | 7B | 14.1 | 60.8 | 46.2 | 58.5 | 6.95 | 35.1 | 30.3 | 23.9 |
Llama 1 | 13B | 18.9 | 66.1 | 52.6 | 62.3 | 10.9 | 46.9 | 37.0 | 33.9 |
Llama 1 | 33B | 26.0 | 70.0 | 58.4 | 67.6 | 21.4 | 57.8 | 39.8 | 41.7 |
Llama 1 | 65B | 30.7 | 70.7 | 60.5 | 68.6 | 30.8 | 63.4 | 43.5 | 47.6 |
Llama 2 | 7B | 16.8 | 63.9 | 48.9 | 61.3 | 14.6 | 45.3 | 32.6 | 29.3 |
Llama 2 | 13B | 24.5 | 66.9 | 55.4 | 65.8 | 28.7 | 54.8 | 39.4 | 39.1 |
Llama 2 | 70B | 37.5 | 71.9 | 63.6 | 69.4 | 35.2 | 68.9 | 51.2 | 54.2 |
Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
TruthfulQA | Toxigen | ||
---|---|---|---|
Llama 1 | 7B | 27.42 | 23.00 |
Llama 1 | 13B | 41.74 | 23.08 |
Llama 1 | 33B | 44.19 | 22.57 |
Llama 1 | 65B | 48.71 | 21.77 |
Llama 2 | 7B | 33.29 | 21.25 |
Llama 2 | 13B | 41.86 | 26.10 |
Llama 2 | 70B | 50.18 | 24.60 |
Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
TruthfulQA | Toxigen | ||
---|---|---|---|
Llama-2-Chat | 7B | 57.04 | 0.00 |
Llama-2-Chat | 13B | 62.18 | 0.00 |
Llama-2-Chat | 70B | 64.14 | 0.01 |
Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above.
Llama 2 is a new technology that carries risks with use. Testing conducted to date has not, and could not, cover all scenarios, including uses in languages other than English. For these reasons, as with all LLMs, Llama 2'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 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at ai.meta.com/llama/responsible-use-guide
Resource ID | Release date | Release stage | Description |
---|---|---|---|
meta/llama2 | 2024-04-01 | General Availability | Fine-tune & deploy Meta's Llama 2 models on Vertex AI |
Llama 2 Version Release Date: July 18, 2023
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and- libraries/llama-downloads/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and- libraries/llama-downloads/.
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and Documentation (and any portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy ("Policy"). The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
Please report any violation of this Policy, software "bug," or other problems that could lead to a violation of this Policy through one of the following means:
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