Llama-2-13b-chat-hf

Maintainer: meta-llama

Total Score

948

Last updated 4/28/2024

👁️

PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model Overview

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.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

⚙️

Llama-2-7b-chat-hf

meta-llama

Total Score

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.

Read more

Updated Invalid Date

🐍

Llama-2-13b-hf

meta-llama

Total Score

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.

Read more

Updated Invalid Date

🌀

Llama-2-13b-chat

meta-llama

Total Score

265

Llama-2-13b-chat is a 13 billion parameter large language model (LLM) 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-2-13b-chat model has been fine-tuned for dialogue use cases, outperforming open-source chat models on many benchmarks. In human evaluations, it has demonstrated capabilities on par with closed-source models like ChatGPT and PaLM. Model inputs and outputs Llama-2-13b-chat is an autoregressive language model that takes in text as input and generates text as output. The model was trained on a diverse dataset of over 2 trillion tokens from publicly available online sources. Inputs Text prompts Outputs Generated text continuations Capabilities Llama-2-13b-chat has shown strong performance on a variety of benchmarks testing capabilities like commonsense reasoning, world knowledge, reading comprehension, and mathematical problem solving. The fine-tuned chat model also demonstrates high levels of truthfulness and low toxicity in evaluations. What can I use it for? The Llama-2-13b-chat model is intended for commercial and research use in English. The tuned dialogue model can be used to power assistant-like chat applications, while the pretrained versions can be adapted for a range of natural language generation tasks. However, as with any large language model, developers should carefully test and tune the model for their specific use cases to ensure safety and alignment with their needs. Things to try Prompting the Llama-2-13b-chat model with open-ended questions or instructions can yield diverse and creative responses. Developers may also find success fine-tuning the model further on domain-specific data to specialize its capabilities for their application.

Read more

Updated Invalid Date

👀

Llama-2-13b

meta-llama

Total Score

307

Llama-2-13b is a 13 billion parameter large language model developed and publicly 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 pretrained on 2 trillion tokens of publicly available data and then fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the models to human preferences for helpfulness and safety. The Llama-2-13b-hf and Llama-2-13b-chat-hf models are 13B versions of the Llama 2 model converted to the Hugging Face Transformers format, with the chat version further fine-tuned for dialogue use cases. These models demonstrate improved performance compared to Llama 1 on a range of academic benchmarks, as well as stronger safety metrics on datasets like TruthfulQA and ToxiGen. Model inputs and outputs Inputs Text**: The Llama-2-13b model takes natural language text as input. Outputs Text**: The model generates natural language text as output. Capabilities The Llama-2-13b model is capable of a variety of natural language generation tasks, including open-ended dialog, question answering, summarization, and more. It has demonstrated strong performance on academic benchmarks covering areas like commonsense reasoning, world knowledge, and math. The fine-tuned Llama-2-13b-chat model in particular is optimized for interactive chat applications, and outperforms open-source chatbots on many measures. What can I use it for? The Llama-2-13b model can be used for a wide range of commercial and research applications involving natural language processing and generation. Some potential use cases include: Building AI assistant applications for customer service, task automation, and knowledge sharing Developing language models for incorporation into larger systems, such as virtual agents, content generation tools, or creative writing aids Adapting the model for specialized domains through further fine-tuning on relevant data Things to try One interesting aspect of the Llama 2 models is their scalability - the 70B parameter version demonstrates significantly stronger performance than the smaller 7B and 13B models across many benchmarks. This suggests there may be value in exploring how to effectively leverage the capabilities of large language models like these for specific application needs. Additionally, the fine-tuned Llama-2-13b-chat model's strong safety metrics on datasets like TruthfulQA and ToxiGen indicate potential for building chat assistants that are more helpful and aligned with human preferences.

Read more

Updated Invalid Date