Llama-3.1-8B
Maintainer: meta-llama
906
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Property | Value |
---|---|
Run this model | Run on HuggingFace |
API spec | View on HuggingFace |
Github link | No Github link provided |
Paper link | No paper link provided |
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Model overview
The Llama-3.1-8B
is a part of the Meta Llama 3.1 collection of multilingual large language models (LLMs) developed by Meta. This collection includes models in 8B, 70B, and 405B parameter sizes, all of which are optimized for multilingual dialogue use cases. The Llama-3.1-8B
model specifically is an auto-regressive language model that uses an optimized transformer architecture and has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
The Llama-3.1-8B
model is part of a family of similar Llama 3.1 models, including the Llama-3.1-405B and Meta-Llama-3.1-70B models. All of these models were developed by the meta-llama team at Meta.
Model inputs and outputs
Inputs
- Multilingual Text: The
Llama-3.1-8B
model supports multilingual text input in 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. - Multilingual Code: The model can also take in code snippets in these 8 supported languages.
Outputs
- Multilingual Text: The model can generate multilingual text output in the 8 supported languages.
- Multilingual Code: The model can also generate code snippets in the 8 supported languages.
Capabilities
The Llama-3.1-8B
model is capable of performing a variety of natural language generation tasks, such as open-ended dialogue, question answering, and text summarization. It has been shown to outperform many available open source and closed chat models on common industry benchmarks. The model's multilingual capabilities make it particularly useful for applications that need to communicate in multiple languages.
What can I use it for?
The Llama-3.1-8B
model is intended for commercial and research use in multiple languages. The instruction-tuned text-only models like this one are well-suited for assistant-like chat applications, while the pretrained models can be adapted for a wider range 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, such as through synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
Things to try
One interesting aspect of the Llama-3.1-8B
model is its ability to handle long-form context. With a context length of 128k tokens, the model can maintain coherence and consistency over extended dialogues or documents. Developers could explore using this capability to build more natural and engaging conversational AI assistants.
Another area to experiment with is the model's multilingual capabilities. Since the Llama-3.1-8B
supports 8 languages, developers could try fine-tuning or adapting the model for specific language domains or tasks in those languages. The Llama 3 paper discusses some of the techniques used to enable this multilingual functionality.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The Meta-Llama-3.1-8B is a large language model (LLM) developed by Meta. It is part of the Meta Llama 3.1 collection of pretrained and instruction-tuned generative models in 8B, 70B, and 405B sizes. The Llama 3.1 instruction-tuned text-only models are optimized for multilingual dialogue use cases and outperform many available open-source and closed chat models on common industry benchmarks. The model uses an optimized transformer architecture and was trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. Similar models in the Llama 3.1 family include the Meta-Llama-3.1-405B-Instruct and the Meta-Llama-3.1-8B-Instruct, which provide different model sizes and levels of instruction tuning. Model inputs and outputs Inputs Multilingual Text**: The model accepts input text in multiple languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Multilingual Code**: The model can also accept input code in these supported languages. Outputs Multilingual Text**: The model generates output text in the same supported languages as the inputs. Multilingual Code**: The model can output code in the supported languages. Capabilities The Meta-Llama-3.1-8B model is capable of engaging in multilingual dialogue, answering questions, and generating text and code across a variety of domains. It has demonstrated strong performance on industry benchmarks such as MMLU, CommonSenseQA, and HumanEval, outperforming many open-source and closed-source chat models. What can I use it for? The Meta-Llama-3.1-8B model is intended for commercial and research use in the supported languages. The instruction-tuned versions are well-suited for assistant-like chat applications, while the pretrained models can be adapted for a range of natural language generation tasks. The model collection also supports the ability to leverage the outputs to improve other models, including through synthetic data generation and distillation. Things to try Some interesting things to try with the Meta-Llama-3.1-8B model include exploring its multilingual capabilities, testing its performance on domain-specific tasks, and experimenting with ways to fine-tune or adapt the model for your specific use case. The Llama 3.1 Community License and Responsible Use Guide provide helpful guidance on responsible development and deployment of the model.
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The Llama-3.1-70B is part of the Meta Llama 3.1 collection of multilingual large language models (LLMs) developed by meta-llama. This 70 billion parameter model is optimized for multilingual dialogue use cases and outperforms many available open source and closed chat models on common industry benchmarks. It uses a transformer architecture with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the model with human preferences for helpfulness and safety. Similar models in the Llama 3.1 family include the Meta-Llama-3.1-8B and Meta-Llama-3.1-405B. Model inputs and outputs The Llama-3.1-70B model takes multilingual text as input and can generate multilingual text and code as output. It has a context length of 128k tokens and uses Grouped-Query Attention (GQA) for improved inference scalability. The model was pretrained on around 15 trillion tokens of data from publicly available sources, with a cutoff date of December 2023. Inputs Multilingual text Outputs Multilingual text Multilingual code Capabilities The Llama-3.1-70B model excels at a variety of natural language processing tasks, including general question answering, commonsense reasoning, reading comprehension, and code generation. It outperforms many other large language models on benchmarks like MMLU, ARC-Challenge, and GSM-8K. What can I use it for? The Llama-3.1-70B model is intended for commercial and research use in multiple languages. The instruction-tuned version is well-suited for assistant-like chat applications, while the pretrained model can be adapted for a variety of natural language generation tasks. Developers can also leverage the model's outputs to improve other models, such as through synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. Things to try With its multilingual capabilities and strong performance on benchmarks, the Llama-3.1-70B model could be a powerful tool for developers working on language-based applications that need to support multiple languages. Try fine-tuning the model on your own datasets or using it as a starting point for building more specialized models tailored to your specific use case.
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