Meta-llama
Models by this creator
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Llama-2-7b
3.9K
Llama-2-7b is a 7 billion parameter pretrained and fine-tuned generative text model developed by Meta. It is part of the Llama 2 family of large language models (LLMs) that range in size from 7 billion to 70 billion parameters. The fine-tuned Llama-2-Chat models are optimized for dialogue use cases and outperform open-source chat models on most benchmarks, performing on par with closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety. Model Inputs and Outputs Inputs Text**: The model takes text as input. Outputs Text**: The model generates text as output. Capabilities The Llama-2-7b model demonstrates strong performance across a range of academic benchmarks, including commonsense reasoning, world knowledge, reading comprehension, and math. It also shows improved safety characteristics compared to previous models, with higher truthfulness and lower toxicity on evaluation datasets. What Can I Use It For? Llama-2-7b is intended for commercial and research use in English. The fine-tuned Llama-2-Chat models can be used for assistant-like dialogue, while the pretrained models can be adapted for a variety of natural language generation tasks. Developers should follow the specific formatting guidelines provided by Meta to get the expected features and performance for the chat versions. Things To Try When using Llama-2-7b, it's important to keep in mind that as with all large language models, the potential outputs cannot be fully predicted in advance. Developers should perform thorough safety testing and tuning tailored to their specific applications before deployment. See the Responsible Use Guide for more information on the ethical considerations and limitations of this technology.
Updated 4/28/2024
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Llama-2-7b-chat-hf
3.5K
Llama-2-7b-chat-hf is a 7 billion parameter generative text model developed and released by Meta. It is part of the Llama 2 family of large language models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are trained on a new mix of publicly available online data and fine-tuned for dialogue use cases using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). Compared to the pretrained Llama-2-7b model, the Llama-2-7b-chat-hf model is specifically optimized for chat and assistant-like applications. Model inputs and outputs Inputs The Llama-2-7b-chat-hf model takes text as input. Outputs The model generates text as output. Capabilities The Llama 2 family of models, including Llama-2-7b-chat-hf, have shown strong performance on a variety of academic benchmarks, outperforming many open-source chat models. The 70B parameter Llama 2 model in particular achieved top scores on commonsense reasoning, world knowledge, reading comprehension, and mathematical reasoning tasks. The fine-tuned chat models like Llama-2-7b-chat-hf are also evaluated to be on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety, as measured by human evaluations. What can I use it for? The Llama-2-7b-chat-hf model is intended for commercial and research use in English, with a focus on assistant-like chat applications. Developers can use the model to build conversational AI agents that can engage in helpful and safe dialogue. The model can also be adapted for a variety of natural language generation tasks beyond just chat, such as question answering, summarization, and creative writing. Things to try One key aspect of the Llama-2-7b-chat-hf model is the specific formatting required to get the expected chat-like features and performance. This includes using INST and > tags, BOS and EOS tokens, and proper whitespacing and linebreaks in the input. Developers should review the reference code provided in the Llama GitHub repository to ensure they are properly integrating the model for chat use cases.
Updated 4/28/2024
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Meta-Llama-3-8B
2.7K
The Meta-Llama-3-8B is an 8-billion parameter language model developed and released by Meta. It is part of the Llama 3 family of large language models (LLMs), which also includes a 70-billion parameter version. The Llama 3 models are optimized for dialogue use cases and outperform many open-source chat models on common benchmarks. The instruction-tuned version is particularly well-suited for assistant-like applications. The Llama 3 models use an optimized transformer architecture and were trained on over 15 trillion tokens of data from publicly available sources. The 8B and 70B models both use Grouped-Query Attention (GQA) for improved inference scalability. The instruction-tuned versions leveraged supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the models with human preferences for helpfulness and safety. Model inputs and outputs Inputs Text input only Outputs Generates text and code Capabilities The Meta-Llama-3-8B model excels at a variety of natural language generation tasks, including open-ended conversations, question answering, and code generation. It outperforms previous Llama models and many other open-source LLMs on standard benchmarks, with particularly strong performance on tasks that require reasoning, commonsense understanding, and following instructions. What can I use it for? The Meta-Llama-3-8B model is well-suited for a range of commercial and research applications that involve natural language processing and generation. The instruction-tuned version can be used to build conversational AI assistants for customer service, task automation, and other applications where helpful and safe language models are needed. The pre-trained model can also be fine-tuned for specialized tasks like content creation, summarization, and knowledge distillation. Things to try Try using the Meta-Llama-3-8B model in open-ended conversations to see its capabilities in areas like task planning, creative writing, and answering follow-up questions. The model's strong performance on commonsense reasoning benchmarks suggests it could be useful for applications that require understanding the real-world context. Additionally, the model's ability to generate code makes it a potentially valuable tool for developers looking to leverage language models for programming assistance.
Updated 4/29/2024
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Llama-2-70b-chat-hf
2.1K
Llama-2-70b-chat-hf is a 70 billion parameter language model from Meta, fine-tuned for dialogue use cases. It is part of the Llama 2 family of models, which also includes smaller versions of 7B and 13B parameters as well as fine-tuned "chat" variants. According to the maintainer meta-llama, the Llama-2-Chat models outperform open-source chat models on most benchmarks and are on par with some popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. Model inputs and outputs Inputs The model accepts text input only. Outputs The model generates text output only. Capabilities The Llama-2-70b-chat-hf model is capable of engaging in open-ended dialogue, answering questions, and generating human-like text across a variety of topics. It has been fine-tuned to provide helpful and safe responses, making it suitable for use cases like virtual assistants, chatbots, and language generation. What can I use it for? The Llama-2-70b-chat-hf model could be used to build conversational AI applications, such as virtual assistants or chatbots, that can engage in open-ended dialogue with users. It could also be used for text generation tasks like summarization, creative writing, or content creation. However, as with any large language model, care should be taken to ensure its outputs are aligned with intended use cases and do not contain harmful or biased content. Things to try One interesting thing to try with Llama-2-70b-chat-hf is exploring its capabilities in multi-turn dialogue. By providing it with context from previous exchanges, you can see how it maintains coherence and builds upon the conversation. Additionally, you could experiment with prompting the model to take on different personas or styles of communication to observe how it adapts its language.
Updated 4/28/2024
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Meta-Llama-3-8B-Instruct
1.5K
The Meta-Llama-3-8B-Instruct is a large language model developed and released by Meta. It is part of the Llama 3 family of models, which come in 8 billion and 70 billion parameter sizes, with both pretrained and instruction-tuned variants. The instruction-tuned Llama 3 models are optimized for dialogue use cases and outperform many open-source chat models on common industry benchmarks. Meta has taken care to optimize these models for helpfulness and safety. The Llama 3 models use an optimized transformer architecture and were trained on a mix of publicly available online data. The 8 billion parameter version uses a context length of 8k tokens and is capable of tasks like commonsense reasoning, world knowledge, reading comprehension, and math. Compared to the earlier Llama 2 models, the Llama 3 models have improved performance across a range of benchmarks. Model inputs and outputs Inputs Text input only Outputs Generates text and code Capabilities The Meta-Llama-3-8B-Instruct model is capable of a variety of natural language generation tasks, including dialogue, summarization, question answering, and code generation. It has shown strong performance on benchmarks evaluating commonsense reasoning, world knowledge, reading comprehension, and math. What can I use it for? The Meta-Llama-3-8B-Instruct model is intended for commercial and research use in English. The instruction-tuned variants are well-suited for assistant-like chat applications, while the pretrained models can be further fine-tuned for a range of text generation tasks. Developers should carefully review the Responsible Use Guide before deploying the model in production. Things to try Developers may want to experiment with fine-tuning the Meta-Llama-3-8B-Instruct model on domain-specific data to adapt it for specialized applications. The model's strong performance on benchmarks like commonsense reasoning and world knowledge also suggests it could be a valuable foundation for building knowledge-intensive applications.
Updated 4/28/2024
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Llama-2-7b-hf
1.4K
Llama-2-7b-hf is a 7 billion parameter generative language model developed and released by Meta. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are trained on a new mix of publicly available online data and use an optimized transformer architecture. The tuned versions, called Llama-2-Chat, are further fine-tuned using supervised fine-tuning and reinforcement learning with human feedback to optimize for helpfulness and safety. These models are intended to outperform open-source chat models on many benchmarks. The Llama-2-70b-chat-hf model is a 70 billion parameter version of the Llama 2 family that is fine-tuned specifically for dialogue use cases, also developed and released by Meta. Both the 7B and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model inputs and outputs Inputs Text prompts Outputs Generated text continuations Capabilities Llama-2-7b-hf is a powerful generative language model capable of producing high-quality text on a wide range of topics. It can be used for tasks like summarization, language translation, question answering, and creative writing. The fine-tuned Llama-2-Chat models are particularly adept at engaging in open-ended dialogue and assisting with task completion. What can I use it for? Llama-2-7b-hf and the other Llama 2 models can be used for a variety of commercial and research applications, including chatbots, content generation, language understanding, and more. The Llama-2-Chat models are well-suited for building assistant-like applications that require helpful and safe responses. To get started, you can fine-tune the models on your own data or use them directly for inference. Meta provides a custom commercial license for the Llama 2 models, which you can access by visiting the website and agreeing to the terms. Things to try One interesting aspect of the Llama 2 models is their ability to scale in size while maintaining strong performance. The 70 billion parameter version of the model significantly outperforms the 7 billion version on many benchmarks, highlighting the value of large language models. Developers could experiment with using different sized Llama 2 models for their specific use cases to find the right balance of performance and resource requirements. Another avenue to explore is the safety and helpfulness of the Llama-2-Chat models. The developers have put a strong emphasis on aligning these models to human preferences, and it would be interesting to see how they perform in real-world applications that require reliable and trustworthy responses.
Updated 4/28/2024
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Llama-2-13b-chat-hf
948
The Llama-2-13b-chat-hf is a version of Meta's Llama 2 large language model, a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This specific 13 billion parameter model has been fine-tuned for dialogue use cases and converted for the Hugging Face Transformers format. The Llama-2-70b-chat-hf and Llama-2-7b-hf models are other variations in the Llama 2 family. Model Inputs and Outputs The Llama-2-13b-chat-hf model takes in text as input and generates text as output. It is an auto-regressive language model that uses an optimized transformer architecture. The fine-tuned versions like this one have been further trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the model to human preferences for helpfulness and safety. Inputs Text prompts Outputs Generated text Capabilities The Llama-2-13b-chat-hf model is capable of a variety of natural language generation tasks, from open-ended dialogue to specific prompts. It outperforms open-source chat models on most benchmarks that Meta has tested, and its performance on human evaluations for helpfulness and safety is on par with models like ChatGPT and PaLM. What Can I Use It For? The Llama-2-13b-chat-hf model is intended for commercial and research use in English. The fine-tuned chat versions are well-suited for building assistant-like applications, while the pretrained models can be adapted for a range of natural language tasks. Some potential use cases include: Building AI assistants and chatbots for customer service, personal productivity, and more Generating creative content like stories, dialogue, and poetry Summarizing text and answering questions Providing language models for downstream applications like translation, question answering, and code generation Things to Try One interesting aspect of the Llama 2 models is the use of Grouped-Query Attention (GQA) in the larger 70 billion parameter version. This technique improves the model's inference scalability, allowing for faster generation without sacrificing performance. Another key feature is the careful fine-tuning and safety testing that Meta has done on the chat-focused versions of Llama 2. Developers should still exercise caution and perform their own safety evaluations, but these models show promising results in terms of helpfulness and reducing harmful outputs.
Updated 4/28/2024
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Llama-2-70b-hf
800
Llama-2-70b-hf is a 70 billion parameter generative language model developed and released by Meta as part of their Llama 2 family of large language models. This model is a pretrained version converted for the Hugging Face Transformers format. The Llama 2 collection includes models ranging from 7 billion to 70 billion parameters, as well as fine-tuned versions optimized for dialogue use cases. The Llama-2-70b-chat-hf model is the fine-tuned version of this 70B model, optimized for conversational abilities. Model inputs and outputs Inputs Llama-2-70b-hf takes text input only. Outputs The model generates text output only. Capabilities The Llama-2-70b-hf model is a powerful auto-regressive language model that can be used for a variety of natural language generation tasks. It outperforms many open-source chat models on industry benchmarks and is on par with some popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. What can I use it for? The Llama-2-70b-hf model is intended for commercial and research use in English. The pretrained version can be adapted for tasks like text generation, summarization, and translation, while the fine-tuned Llama-2-70b-chat-hf model is optimized for assistant-like chat applications. Things to try Developers can fine-tune the Llama-2-70b-hf model for their specific use cases, leveraging the model's strong performance on a variety of NLP tasks. The Llama-2-7b-hf and Llama-2-13b-hf models provide smaller-scale alternatives that may be more practical for certain applications.
Updated 4/28/2024
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Meta-Llama-3-70B-Instruct
783
The Meta-Llama-3-70B-Instruct is a large language model (LLM) developed and released by Meta. It is part of the Meta Llama 3 family of models, which includes both 8B and 70B parameter versions in pre-trained and instruction-tuned variants. The Llama 3 instruction-tuned models are optimized for dialogue use cases and outperform many available open-source chat models on common industry benchmarks. Meta took great care in developing these models to optimize for helpfulness and safety. The Meta-Llama-3-8B-Instruct is a smaller 8 billion parameter version of the instruction-tuned Llama 3 model, while the Llama-2-70b-chat-hf is a 70 billion parameter Llama 2 model tuned specifically for chatbot applications. Model inputs and outputs Inputs Text input only Outputs Generates text and code Capabilities The Meta-Llama-3-70B-Instruct model is a powerful generative text model capable of a wide range of natural language tasks. It can engage in helpful and safe dialogue, generate coherent and relevant text, and even produce code. The model's large size and instruction tuning allow it to outperform many open-source chat models on industry benchmarks. What can I use it for? The Meta-Llama-3-70B-Instruct model is well-suited for commercial and research use cases that require an advanced language model for tasks like chatbots, content generation, code generation, and more. Developers can fine-tune the model for specific applications or use the pre-trained version as-is. The model's capabilities make it a valuable tool for businesses looking to enhance their conversational AI offerings or automate content creation. Things to try One interesting aspect of the Meta-Llama-3-70B-Instruct model is its strong performance on both language understanding and generation tasks. Developers can experiment with using the model for a variety of natural language applications, from open-ended dialogue to more structured tasks like question answering or summarization. The model's large size and instruction tuning also make it well-suited for few-shot learning, where it can adapt quickly to new tasks with limited training data.
Updated 4/28/2024
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Llama-2-13b-hf
536
Llama-2-13b-hf is a 13 billion parameter generative language model from Meta. It is part of the Llama 2 family, which includes models ranging from 7 billion to 70 billion parameters. The Llama 2 models are designed for a variety of natural language generation tasks, with the fine-tuned "Llama-2-Chat" versions optimized specifically for dialogue use cases. According to the maintainer, the Llama-2-Chat models outperform open-source chat models on most benchmarks and are on par with closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. Model inputs and outputs Inputs Text**: The Llama-2-13b-hf model takes text as input. Outputs Text**: The model generates text as output. Capabilities The Llama 2 models demonstrate strong performance across a range of academic benchmarks, including commonsense reasoning, world knowledge, reading comprehension, and mathematics. The 70 billion parameter Llama 2 model in particular achieves state-of-the-art results, outperforming the smaller Llama 1 models. The fine-tuned Llama-2-Chat models also show strong results in terms of truthfulness and low toxicity. What can I use it for? The Llama-2-13b-hf model is intended for commercial and research use in English. The pretrained version can be adapted for a variety of natural language generation tasks, while the fine-tuned Llama-2-Chat variants are designed for assistant-like dialogue. To get the best performance for chat use cases, specific formatting with tags and tokens is recommended, as outlined in the Meta Llama documentation. Things to try Researchers and developers can explore using the Llama-2-13b-hf model for a range of language generation tasks, from creative writing to question answering. The larger 70 billion parameter version may be particularly useful for demanding applications that require strong language understanding and generation capabilities. Those interested in chatbot-style applications should look into the fine-tuned Llama-2-Chat variants, following the formatting guidance provided.
Updated 4/28/2024