Llama3-ChatQA-1.5-70B

Maintainer: nvidia - Last updated 6/1/2024

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

🔗

Model Overview

The Llama3-ChatQA-1.5-70B model is a large language model developed by NVIDIA that excels at conversational question answering (QA) and retrieval-augmented generation (RAG). It is built on top of the Llama-3 base model and incorporates more conversational QA data to enhance its tabular and arithmetic calculation capability. The model comes in two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using Megatron-LM and then converted to the Hugging Face format.

Model Inputs and Outputs

Inputs

  • Text: The model takes text as input, which can be in the form of a conversation or a question.

Outputs

  • Text: The model generates text as output, providing answers to questions or continuing a conversation.

Capabilities

The Llama3-ChatQA-1.5-70B model excels at conversational question answering and retrieval-augmented generation tasks. It has demonstrated strong performance on benchmarks such as ConvRAG, QuAC, QReCC, and ConvFinQA, outperforming other models like ChatQA-1.0-7B, Command-R-Plus, and Llama-3-instruct-70b.

What can I use it for?

The Llama3-ChatQA-1.5-70B model can be used for a variety of applications that involve question answering and conversational abilities, such as:

  • Building intelligent chatbots or virtual assistants
  • Enhancing search engines with more advanced query understanding and response generation
  • Developing educational tools and tutoring systems
  • Automating customer service and support interactions
  • Assisting in research and analysis tasks by providing relevant information and insights

Things to try

One interesting aspect of the Llama3-ChatQA-1.5-70B model is its ability to handle tabular and arithmetic calculations as part of its conversational QA capabilities. You could try prompting the model with questions that involve numerical data or complex reasoning, and observe how it responds. Additionally, the model's retrieval-augmented generation capabilities allow it to provide responses that are grounded in relevant information, which can be useful for tasks that require fact-based answers.



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

Total Score

274

Related Models

🏅

Llama3-ChatQA-1.5-8B

nvidia

Total Score

475

The Llama3-ChatQA-1.5-8B model is a large language model developed by NVIDIA that excels at conversational question answering (QA) and retrieval-augmented generation (RAG). It was built on top of the Llama-3 base model and incorporates more conversational QA data to enhance its tabular and arithmetic calculation capabilities. There is also a larger 70B parameter version available. Model inputs and outputs Inputs Text**: The model accepts text input to engage in conversational question answering and generation tasks. Outputs Text**: The model outputs generated text responses, providing answers to questions and generating relevant information. Capabilities The Llama3-ChatQA-1.5-8B model demonstrates strong performance on a variety of conversational QA and RAG benchmarks, outperforming models like ChatQA-1.0-7B, Llama-3-instruct-70b, and GPT-4-0613. It excels at tasks like document-grounded dialogue, multi-turn question answering, and open-ended conversational QA. What can I use it for? The Llama3-ChatQA-1.5-8B model is well-suited for building conversational AI assistants, chatbots, and other applications that require natural language understanding and generation capabilities. It could be used to power customer service chatbots, virtual assistants, educational tools, and more. The model's strong performance on QA and RAG tasks make it a valuable resource for researchers and developers working on conversational AI systems. Things to try One interesting aspect of the Llama3-ChatQA-1.5-8B model is its ability to handle tabular and arithmetic calculation tasks, which can be useful for applications that require quantitative reasoning. Developers could explore using the model to power conversational interfaces for data analysis, financial planning, or other domains that involve numerical information. Another interesting area to explore would be the model's performance on multi-turn dialogues and its ability to maintain context and coherence over the course of a conversation. Developers could experiment with using the model for open-ended chatting, task-oriented dialogues, or other interactive scenarios to further understand its conversational capabilities.

Read more

Updated Invalid Date

💬

Llama3-DocChat-1.0-8B

cerebras

Total Score

62

The Llama3-DocChat-1.0-8B model, developed by Cerebras, is an 8 billion parameter large language model built on top of the Llama 3 base. It is designed for document-based conversational question answering, building on insights from NVIDIA's ChatQA model series. Cerebras leveraged their expertise in LLM training and dataset curation to improve upon the limitations of the ChatQA datasets and training recipes. Additionally, they employed synthetic data generation to address gaps that could not be fully resolved with available real data. Model inputs and outputs Inputs Text**: The model takes natural language text as input, which can include questions, instructions, or dialogue. Outputs Text**: The model generates relevant and coherent natural language responses to the input text. Capabilities The Llama3-DocChat-1.0-8B model excels at conversational question answering tasks, particularly when the context is provided in the form of documents. It can understand and respond to queries that require reasoning over the provided information, and it outperforms several popular models on relevant benchmarks. What can I use it for? The Llama3-DocChat-1.0-8B model can be used to build applications that involve document-based question answering, such as: Customer support**: Enabling users to ask questions and get answers based on product manuals, FAQs, or other relevant documentation. Research assistance**: Helping researchers find relevant information and answer questions based on a corpus of academic papers or reports. Intelligent search**: Enhancing search experiences by providing direct answers to queries, rather than just a list of relevant documents. Things to try One interesting aspect of the Llama3-DocChat-1.0-8B model is its ability to handle multi-turn conversations. By leveraging the provided context, the model can engage in a back-and-forth dialogue, building upon previous exchanges to provide more comprehensive and relevant responses. Developers can explore ways to incorporate this capability into their applications to create more natural and helpful conversational experiences.

Read more

Updated Invalid Date

🏅

Llama-3-ChatQA-1.5-8B-GGUF

bartowski

Total Score

42

The Llama-3-ChatQA-1.5-8B-GGUF model is a quantized version of the Llama-3-ChatQA-1.5-8B model, created by bartowski using the llama.cpp library. It is similar to other large language models like the Meta-Llama-3-8B-Instruct-GGUF and LLaMA3-iterative-DPO-final-GGUF models, which have also been quantized for reduced file size and improved performance. Model inputs and outputs The Llama-3-ChatQA-1.5-8B-GGUF model is a text-to-text model, meaning it takes text as input and generates text as output. The input can be a question, prompt, or any other type of text, and the output will be the model's response. Inputs Text**: The input text, which can be a question, prompt, or any other type of text. Outputs Text**: The model's response, which is generated based on the input text. Capabilities The Llama-3-ChatQA-1.5-8B-GGUF model is capable of engaging in open-ended conversations, answering questions, and generating text on a wide range of topics. It can be used for tasks such as chatbots, question-answering systems, and creative writing assistants. What can I use it for? The Llama-3-ChatQA-1.5-8B-GGUF model can be used for a variety of applications, such as: Chatbots**: The model can be used to build conversational AI assistants that can engage in natural language interactions. Question-Answering Systems**: The model can be used to create systems that can answer questions on a wide range of topics. Creative Writing Assistants**: The model can be used to generate text for creative writing tasks, such as story writing or poetry generation. Things to try One interesting thing to try with the Llama-3-ChatQA-1.5-8B-GGUF model is to explore the different quantization levels available and see how they affect the model's performance and output quality. The maintainer has provided a range of quantized versions with varying file sizes and quality levels, so you can experiment to find the right balance for your specific use case. Another thing to try is to fine-tune the model on a specific dataset or task, which can help it perform better on that task compared to the default pre-trained model. This could involve tasks like sentiment analysis, summarization, or task-oriented dialogue.

Read more

Updated Invalid Date

⚙️

Llama-2-7b-chat-hf

NousResearch

Total Score

146

Llama-2-7b-chat-hf is a 7B parameter large language model (LLM) developed by Meta. It is part of the Llama 2 family of models, which range in size from 7B to 70B parameters. The Llama 2 models are pretrained on a diverse corpus of publicly available data and then fine-tuned for dialogue use cases, making them optimized for assistant-like chat interactions. Compared to open-source chat models, the Llama-2-Chat models outperform on most benchmarks and are on par with popular closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety. Model inputs and outputs Inputs Text**: The Llama-2-7b-chat-hf model takes natural language text as input. Outputs Text**: The model generates natural language text as output. Capabilities The Llama-2-7b-chat-hf model demonstrates strong performance on a variety of natural language tasks, including commonsense reasoning, world knowledge, reading comprehension, and math problem-solving. It also exhibits high levels of truthfulness and low toxicity in generation, making it suitable for use in assistant-like applications. What can I use it for? The Llama-2-7b-chat-hf model is intended for commercial and research use in English. The fine-tuned Llama-2-Chat versions can be used to build interactive chatbots and virtual assistants that engage in helpful and informative dialogue. The pretrained Llama 2 models can also be adapted for a variety of natural language generation tasks, such as summarization, translation, and content creation. Things to try Developers interested in using the Llama-2-7b-chat-hf model should carefully review the responsible use guide provided by Meta, as large language models can carry risks and should be thoroughly tested and tuned for specific applications. Additionally, users should follow the formatting guidelines for the chat versions, which include using INST and > tags, BOS and EOS tokens, and proper whitespacing and linebreaks.

Read more

Updated Invalid Date