Decapoda Research Llama 7B Hf

🚀 LLaMA-7B Model

LLaMA-7B has been converted to work with Transformers/HuggingFace. It operates under a special license. For detailed information, please refer to the LICENSE file.

🚀 Quick Start

LLaMA-7B is now compatible with Transformers/HuggingFace. You can start using it for your research on large language models.

✨ Features

Model Details

• Organization developing the model: The FAIR team of Meta AI.
• Model date: LLaMA was trained between December 2022 and February 2023.
• Model version: This is version 1 of the model.
• Model type: LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
• Paper or resources for more information: More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
• Citations details: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
• License: Non-commercial bespoke license.
• Where to send questions or comments about the model: Questions and comments about LLaMA can be sent via the GitHub repository of the project, by opening an issue.

Intended Use

• Primary intended uses: The primary use of LLaMA is research on large language models, including exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of current language models, and developing techniques to improve those, evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
• Primary intended users: The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
• Out-of-scope use cases: LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.

Factors

• Relevant factors: One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
• Evaluation factors: As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.

Metrics

• Model performance measures: We use the following measure to evaluate the model:
  • Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs.
  • Exact match for question answering.
  • The toxicity score from Perspective API on RealToxicityPrompts.
• Decision thresholds: Not applicable.
• Approaches to uncertainty and variability: Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.

Evaluation Datasets

The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.

Quantitative Analysis

Model Hyperparameters

Table 1 - Summary of LLama Model Hyperparameters

Model Performance on Reasoning Tasks

Table 2 - Summary of LLama Model Performance on Reasoning tasks

Model Bias

Table 3 - Summary bias of our model output

Ethical Considerations

• Data: The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
• Human life: The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
• Mitigations: We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser - Ney language model and a fastText linear classifier.
• Risks and harms: Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
• Use cases: LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.

⚠️ Important Note

LLaMA is a foundational model. It should not be used on downstream applications without further risk evaluation and mitigation, as it can generate toxic or offensive content, incorrect information or generally unhelpful answers.

💡 Usage Tip

The model performance may vary depending on the language used. Since most of the training data is English text, the model is expected to perform better for English than other languages.

📄 License

This project is under a non - commercial bespoke license. Please see the LICENSE file for details.