Meta-Llama-3.1-70B
MetaKey capabilities
About this model
Model developer: Meta
Model Release Date: July 23, 2024.
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.
Key model capabilities
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.
| Packages | Sample Notebook |
| OpenAI SDK | openaisdk.ipynb |
| LangChain | langchain.ipynb |
| Azure API | webrequests.ipynb |
| LiteLLM SDK | litellm.ipynb |
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
| 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 |
| MMLU PRO (CoT) | 5 | macro_avg/acc_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 | |
| AGIEval English | 3-5 | average/acc_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 | |
| CommonSenseQA | 7 | acc_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 | |
| Winogrande | 5 | acc_char | - | 60.5 | - | 83.3 | 86.7 | |
| BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 | |
| 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 |
| QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 |
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 (RLHF) to align with human preferences for helpfulness and safety.
| Training Data | Params | Input Modalities | Output Modalities | Context Length | GQA | Token Count | Knowledge Cutoff | |
| Llama 3.1 | A new mix of publicly available online data. | 8B | Multilingual Text | Multilingual Text and code | 128k | Yes | 15T+ | December 2023 |
| 70B | Multilingual Text | Multilingual Text and code | 128k | Yes | ||||
| 405B | Multilingual Text | Multilingual Text and code | 128k | Yes |
Training cut-off date
The pretraining data has a cutoff of December 2023.
Training time
Training utilized a cumulative of 39.3M 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.
| 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 3.1 8B | 1.46M | 700 | 420 | 0 |
| Llama 3.1 70B | 7.0M | 700 | 2,040 | 0 |
| Llama 3.1 405B | 30.84M | 700 | 8,930 | 0 |
| Total | 39.3M | - | 11,390 | 0 |
Input formats
Multilingual Text
Output formats
Multilingual Text and code
Supported languages
Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Note: Llama 3.1 has been trained on a broader collection of languages than the 8 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.
Sample JSON response
Sample input
{
"input_data": {
"input_string": ["I believe the meaning of life is"],
"parameters":{
"top_p": 0.8,
"temperature": 0.8,
"max_new_tokens": 96,
"do_sample": true
}
}
}
Sample output
[
{
"0": "I believe the meaning of life is to make others happy. There is nothing more satisfying than seeing a smile on someone's face and knowing you put it there. In a world that is constantly moving and evolving, it's important to have someone who can help keep you grounded and to bring a smile to your face. I want to be that person for someone. I want to be the reason that someone smiles.\\nI am a loving and caring person. I love to have fun and to be with the people I love."
}
]
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 (RLHF) to align with human preferences for helpfulness and safety.
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.
Long context
128k
Optimizing model performance
The provider has not supplied this information.
Additional assets
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