Nvidia
Models by this creator
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NVLM-D-72B
621
NVLM-D-72B is a frontier-class multimodal large language model (LLM) developed by NVIDIA. It achieves state-of-the-art results on vision-language tasks, rivaling leading proprietary models like GPT-4o and open-access models like Llama 3-V 405B and InternVL2. Remarkably, NVLM-D-72B shows improved text-only performance over its LLM backbone after multimodal training. Model Inputs and Outputs NVLM-D-72B is a decoder-only multimodal LLM that can take both text and images as inputs. The model outputs are primarily text, allowing it to excel at vision-language tasks like visual question answering, image captioning, and image-text retrieval. Inputs Text**: The model can take text inputs of up to 8,000 characters. Images**: The model can accept image inputs in addition to text. Outputs Text**: The model generates text outputs, which can be used for a variety of vision-language tasks. Capabilities NVLM-D-72B demonstrates strong performance on a range of multimodal benchmarks, including MMMU, MathVista, OCRBench, AI2D, ChartQA, DocVQA, TextVQA, RealWorldQA, and VQAv2. It outperforms many leading models in these areas, making it a powerful tool for vision-language applications. What can I use it for? NVLM-D-72B is well-suited for a variety of vision-language applications, such as: Visual Question Answering**: The model can answer questions about the content and context of an image. Image Captioning**: The model can generate detailed captions describing the contents of an image. Image-Text Retrieval**: The model can match images with relevant textual descriptions and vice versa. Multimodal Reasoning**: The model can combine information from text and images to perform advanced reasoning tasks. Things to try One key insight about NVLM-D-72B is its ability to maintain and even improve on its text-only performance after multimodal training. This suggests that the model has learned to effectively integrate visual and textual information, making it a powerful tool for a wide range of vision-language applications.
Updated 10/14/2024
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Nemotron-4-340B-Instruct
588
The Nemotron-4-340B-Instruct is a large language model (LLM) developed by NVIDIA. It is a fine-tuned version of the Nemotron-4-340B-Base model, optimized for English-based single and multi-turn chat use-cases. The model has 340 billion parameters and supports a context length of 4,096 tokens. The Nemotron-4-340B-Instruct model was trained on a diverse corpus of 9 trillion tokens, including English-based texts, 50+ natural languages, and 40+ coding languages. It then went through additional alignment steps, including supervised fine-tuning (SFT), direct preference optimization (DPO), and reward-aware preference optimization (RPO), using approximately 20K human-annotated data. This results in a model that is aligned for human chat preferences, improvements in mathematical reasoning, coding, and instruction-following, and is capable of generating high quality synthetic data for a variety of use cases. Model Inputs and Outputs Inputs Text**: The Nemotron-4-340B-Instruct model takes natural language text as input, typically in the form of prompts or conversational exchanges. Outputs Text**: The model generates natural language text as output, which can include responses to prompts, continuations of conversations, or synthetic data. Capabilities The Nemotron-4-340B-Instruct model can be used for a variety of natural language processing tasks, including: Chat and Conversation**: The model is optimized for English-based single and multi-turn chat use-cases, and can engage in coherent and helpful conversations. Instruction-Following**: The model can understand and follow instructions, making it useful for task-oriented applications. Mathematical Reasoning**: The model has improved capabilities in mathematical reasoning, which can be useful for educational or analytical applications. Code Generation**: The model's training on coding languages allows it to generate high-quality code, making it suitable for developer assistance or programming-related tasks. Synthetic Data Generation**: The model's alignment and optimization process makes it well-suited for generating high-quality synthetic data, which can be used to train other language models. What Can I Use It For? The Nemotron-4-340B-Instruct model can be used for a wide range of applications, particularly those that require natural language understanding, generation, and task-oriented capabilities. Some potential use cases include: Chatbots and Virtual Assistants**: The model can be used to build conversational AI agents that can engage in helpful and coherent dialogues. Educational and Tutoring Applications**: The model's capabilities in mathematical reasoning and instruction-following can be leveraged to create educational tools and virtual tutors. Developer Assistance**: The model's ability to generate high-quality code can be used to build tools that assist software developers with programming-related tasks. Synthetic Data Generation**: Companies and researchers can use the model to generate high-quality synthetic data for training their own language models, as described in the technical report. Things to Try One interesting aspect of the Nemotron-4-340B-Instruct model is its ability to follow instructions and engage in task-oriented dialogue. You could try prompting the model with open-ended questions or requests, and observe how it responds and adapts to the task at hand. For example, you could ask the model to write a short story, solve a math problem, or provide step-by-step instructions for a particular task, and see how it performs. Another interesting area to explore would be the model's capabilities in generating synthetic data. You could experiment with different prompts or techniques to guide the model's data generation, and then assess the quality and usefulness of the generated samples for training your own language models.
Updated 7/16/2024
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Llama3-ChatQA-1.5-8B
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.
Updated 6/1/2024
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Llama3-ChatQA-1.5-70B
274
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.
Updated 6/1/2024
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GPT-2B-001
191
GPT-2B-001 is a transformer-based language model developed by NVIDIA. It is part of the GPT family of models, similar to GPT-2 and GPT-3, with a total of 2 billion trainable parameters. The model was trained on 1.1 trillion tokens using NVIDIA's NeMo toolkit. Compared to similar models like gemma-2b-it, prometheus-13b-v1.0, and bge-reranker-base, GPT-2B-001 features several architectural improvements, including the use of the SwiGLU activation function, rotary positional embeddings, and a longer maximum sequence length of 4,096. Model inputs and outputs Inputs Text prompts of variable length, up to a maximum of 4,096 tokens. Outputs Continuation of the input text, generated in an autoregressive manner. The model can be used for a variety of text-to-text tasks, such as language modeling, text generation, and question answering. Capabilities GPT-2B-001 is a powerful language model capable of generating human-like text on a wide range of topics. It can be used for tasks such as creative writing, summarization, and even code generation. The model's large size and robust training process allow it to capture complex linguistic patterns and produce coherent, contextually relevant output. What can I use it for? GPT-2B-001 can be used for a variety of natural language processing tasks, including: Content generation**: The model can be used to generate articles, stories, dialogue, and other forms of text. This can be useful for writers, content creators, and marketers. Question answering**: The model can be fine-tuned to answer questions on a wide range of topics, making it useful for building conversational agents and knowledge-based applications. Summarization**: The model can be used to generate concise summaries of longer text, which can be helpful for researchers, students, and business professionals. Code generation**: The model can be used to generate code snippets and even complete programs, which can assist developers in their work. Things to try One interesting aspect of GPT-2B-001 is its ability to generate text that is both coherent and creative. Try prompting the model with a simple sentence or phrase and see how it expands upon the idea, generating new and unexpected content. You can also experiment with fine-tuning the model on specific datasets to see how it performs on more specialized tasks. Another fascinating area to explore is the model's capability for reasoning and logical inference. Try presenting the model with prompts that require deductive or inductive reasoning, and observe how it approaches the problem and formulates its responses.
Updated 5/28/2024
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canary-1b
191
The canary-1b model is a part of the NVIDIA NeMo Canary family of multi-lingual, multi-tasking models. With 1 billion parameters, the Canary-1B model supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). The model uses a FastConformer-Transformer encoder-decoder architecture. Model inputs and outputs Inputs Audio files or a jsonl manifest file containing audio data Outputs Transcribed text in the specified language (English, German, French, Spanish) Translated text to/from the specified language pair Capabilities The Canary-1B model demonstrates state-of-the-art performance on multiple benchmarks for ASR and translation tasks in the supported languages. It can handle various accents, background noise, and technical language well. What can I use it for? The canary-1b model is well-suited for research on robust, multi-lingual speech recognition and translation. It can also be fine-tuned on specific datasets to improve performance for particular domains or applications. Developers may find it useful as a pre-trained model for building ASR or translation tools, especially for the supported languages. Things to try You can experiment with the canary-1b model by loading it using the NVIDIA NeMo toolkit. Try transcribing or translating audio samples in different languages, and compare the results to your expectations or other models. You can also fine-tune the model on your own data to see how it performs on specific tasks or domains.
Updated 5/28/2024
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Llama-3.1-Minitron-4B-Width-Base
178
Llama-3.1-Minitron-4B-Width-Base is a base text-to-text model developed by NVIDIA that can be adopted for a variety of natural language generation tasks. It is obtained by pruning the larger Llama-3.1-8B model, specifically reducing the model embedding size, number of attention heads, and MLP intermediate dimension. The pruned model is then further trained with distillation using 94 billion tokens from the continuous pre-training data corpus used for Nemotron-4 15B. Similar NVIDIA models include the Minitron-8B-Base and Nemotron-4-Minitron-4B-Base, which are also derived from larger language models through pruning and knowledge distillation. These compact models exhibit performance comparable to other community models, while requiring significantly fewer training tokens and compute resources compared to training from scratch. Model Inputs and Outputs Inputs Text**: The model takes text input in string format. Parameters**: The model does not require any additional input parameters. Other Properties**: The model performs best with input text less than 8,000 characters. Outputs Text**: The model generates text output in string format. Output Parameters**: The output is a 1D sequence of text. Capabilities Llama-3.1-Minitron-4B-Width-Base is a powerful text generation model that can be used for a variety of natural language tasks. Its smaller size and reduced training requirements compared to the full Llama-3.1-8B model make it an attractive option for developers looking to deploy large language models in resource-constrained environments. What Can I Use It For? The Llama-3.1-Minitron-4B-Width-Base model can be used for a wide range of natural language generation tasks, such as chatbots, content generation, and language modeling. Its capabilities make it well-suited for commercial and research applications that require a balance of performance and efficiency. Things to Try One interesting aspect of the Llama-3.1-Minitron-4B-Width-Base model is its use of Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE), which can improve its inference scalability compared to standard transformer architectures. Developers may want to experiment with these architectural choices and their impact on the model's performance and capabilities.
Updated 9/18/2024
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Llama-3_1-Nemotron-51B-Instruct
172
Llama-3_1-Nemotron-51B-Instruct is a large language model (LLM) that offers a great tradeoff between model accuracy and efficiency. NVIDIA developed this model using a novel Neural Architecture Search (NAS) approach which greatly reduces the model's memory footprint, enabling larger workloads and fitting the model on a single GPU at high workloads. This allows for a desired point in the accuracy-efficiency tradeoff to be selected. The model was fine-tuned on 40 billion tokens of data focused on English single and multi-turn chat use-cases. This model is a derivative of the larger Llama-3.1-70B-instruct model, utilizing a block-wise distillation approach to create multiple variants with different quality vs. computational complexity tradeoffs. The final model was then aligned for human chat preferences through knowledge distillation. Model inputs and outputs Inputs Text**: The model takes text-only input. Outputs Text**: The model generates text-only output. Capabilities Llama-3_1-Nemotron-51B-Instruct is capable of generating high-quality responses for a variety of natural language tasks, with a particular focus on dialogue and chat applications. The model has been optimized for English-based single and multi-turn chat use-cases, and can handle tasks like roleplaying, retrieval augmented generation, and function calling. What can I use it for? This model can be used in a variety of commercial applications that require natural language generation, such as chatbots, virtual assistants, and content creation tools. The emphasis on efficiency and accuracy-tuning makes it well-suited for scenarios where cost-effectiveness and performance are important, such as embedded or on-device deployments. NVIDIA provides additional resources and information on using this model, including a blog post and demo on build.nvidia.com. Things to try One interesting aspect of this model is its use of a novel NAS approach to balance accuracy and efficiency. Developers could experiment with using this model in resource-constrained environments, such as on-device applications, and compare its performance to other efficiency-focused models. Additionally, the model's focus on English chat applications presents opportunities to integrate it into interactive dialogue systems or virtual agents.
Updated 10/14/2024
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Mistral-NeMo-Minitron-8B-Base
146
The Mistral-NeMo-Minitron-8B-Base is a large language model (LLM) developed by NVIDIA. It is a pruned and distilled version of the larger Mistral-NeMo 12B model, with a reduced embedding dimension and MLP intermediate dimension. The model was obtained by continued training on 380 billion tokens using the same data corpus as the Nemotron-4 15B model. Similar models in the Minitron and Nemotron families include the Minitron-8B-Base and Nemotron-4-Minitron-4B-Base, which were also derived from larger base models through pruning and distillation. These compact models are designed to provide similar performance to their larger counterparts while reducing the computational cost of training and inference. Model Inputs and Outputs Inputs Text**: The Mistral-NeMo-Minitron-8B-Base model takes text input in the form of a string. It works well with input sequences up to 8,000 characters in length. Outputs Text**: The model generates text output in the form of a string. The output can be used for a variety of natural language generation tasks. Capabilities The Mistral-NeMo-Minitron-8B-Base model can be used for a wide range of text-to-text tasks, such as language generation, summarization, and translation. Its compact size and efficient architecture make it suitable for deployment on resource-constrained devices or in applications with low latency requirements. What Can I Use It For? The Mistral-NeMo-Minitron-8B-Base model can be used as a drop-in replacement for larger language models in various applications, such as: Content Generation**: The model can be used to generate engaging and coherent text for applications like chatbots, creative writing assistants, or product descriptions. Summarization**: The model can be used to summarize long-form text, making it easier for users to quickly grasp the key points. Translation**: The model's multilingual capabilities allow it to be used for cross-lingual translation tasks. Code Generation**: The model's familiarity with code syntax and structure makes it a useful tool for generating or completing code snippets. Things to Try One interesting aspect of the Mistral-NeMo-Minitron-8B-Base model is its ability to generate diverse and coherent text while using relatively few parameters. This makes it well-suited for applications with strict resource constraints, such as edge devices or mobile apps. Developers could experiment with using the model for tasks like personalized content generation, where the compact size allows for deployment closer to the user. Another interesting area to explore is the model's performance on specialized tasks or datasets, such as legal or scientific text generation. The model's strong foundation in multidomain data may allow it to adapt well to these specialized use cases with minimal fine-tuning.
Updated 9/21/2024
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Nemotron-4-340B-Base
132
Nemotron-4-340B-Base is a large language model (LLM) developed by NVIDIA that can be used as part of a synthetic data generation pipeline. With 340 billion parameters and support for a context length of 4,096 tokens, this multilingual model was pre-trained on a diverse dataset of over 50 natural languages and 40 coding languages. After an initial pre-training phase of 8 trillion tokens, the model underwent continuous pre-training on an additional 1 trillion tokens to improve quality. Similar models include the Nemotron-3-8B-Base-4k, a smaller enterprise-ready 8 billion parameter model, and the GPT-2B-001, a 2 billion parameter multilingual model with architectural improvements. Model Inputs and Outputs Nemotron-4-340B-Base is a powerful text generation model that can be used for a variety of natural language tasks. The model accepts textual inputs and generates corresponding text outputs. Inputs Textual prompts in over 50 natural languages and 40 coding languages Outputs Coherent, contextually relevant text continuations based on the input prompts Capabilities Nemotron-4-340B-Base excels at a range of natural language tasks, including text generation, translation, code generation, and more. The model's large scale and broad multilingual capabilities make it a versatile tool for researchers and developers looking to build advanced language AI applications. What Can I Use It For? Nemotron-4-340B-Base is well-suited for use cases that require high-quality, diverse language generation, such as: Synthetic data generation for training custom language models Multilingual chatbots and virtual assistants Automated content creation for websites, blogs, and social media Code generation and programming assistants By leveraging the NVIDIA NeMo Framework and tools like Parameter-Efficient Fine-Tuning and Model Alignment, users can further customize Nemotron-4-340B-Base to their specific needs. Things to Try One interesting aspect of Nemotron-4-340B-Base is its ability to generate text in a wide range of languages. Try prompting the model with inputs in different languages and observe the quality and coherence of the generated outputs. You can also experiment with combining the model's multilingual capabilities with tasks like translation or cross-lingual information retrieval. Another area worth exploring is the model's potential for synthetic data generation. By fine-tuning Nemotron-4-340B-Base on specific datasets or domains, you can create custom language models tailored to your needs, while leveraging the broad knowledge and capabilities of the base model.
Updated 7/16/2024