Maintainer: h2oai

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model is a language model trained by H2O.ai. It is based on the tiiuae/falcon-7b model, which is a 7 billion parameter version of the Falcon model family developed by the Technology Innovation Institute (TII). The model was further personalized using the OpenAssistant/oasst1 dataset.

Similar models include the GPT-2 Medium and OpenAI GPT-1 models, which are also large language models trained on internet data. However, the h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model has been further fine-tuned on a more specialized dataset, which may give it advantages in certain text-to-text tasks.

Model inputs and outputs


  • Text prompt: The model takes a text prompt as input, which can be a question, a statement, or any other natural language text.


  • Generated text: The model outputs generated text that continues or responds to the input prompt. The generated text can be used for a variety of applications, such as question answering, text summarization, or creative writing.


The h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model is capable of generating high-quality, coherent text that is relevant to the input prompt. It can be used for a variety of natural language processing tasks, such as:

  • Question answering: The model can provide detailed and informative answers to questions on a wide range of topics.
  • Text summarization: The model can generate concise summaries of longer passages of text.
  • Creative writing: The model can be used to generate poetry, stories, or other creative text.

The model's performance may be particularly strong in tasks that are similar to the fine-tuning dataset, such as conversational interactions or task-oriented dialog.

What can I use it for?

The h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model can be used for a variety of applications that require natural language generation, including:

  • Chatbots and virtual assistants: The model can be used to power conversational interfaces, providing human-like responses to user queries.
  • Content creation: The model can be used to generate text for blog posts, articles, or other written content.
  • Research and education: The model can be used by researchers and educators to explore the capabilities of large language models and to develop new applications.

When using the model, it's important to keep in mind its limitations and potential biases, as discussed in the maintainer's description.

Things to try

One interesting thing to try with the h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model is to explore its ability to engage in multi-turn conversations. By providing a coherent sequence of prompts and responses, you can see how the model maintains context and continuity over an extended dialog.

Another interesting aspect to explore is the model's ability to follow instructions and complete specific tasks. You can try providing the model with detailed prompts that outline a particular objective or process, and see how well it is able to generate relevant and actionable text.

Overall, the h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 model is a powerful tool for natural language generation, with a wide range of potential applications. By experimenting with its capabilities and limitations, you can gain valuable insights into the state of the art in large language models.

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




Total Score


The h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 is an AI model developed by h2oai. It is a text-to-text model, similar to other large language models like gpt-j-6B-8bit, rwkv-5-h-world, hakoMay, mixtral-8x7b-32kseqlen, and llava-13b-v0-4bit-128g. These models are trained on large amounts of text data and can generate human-like text. Model inputs and outputs The h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 model takes in text as input and generates text as output. The model can understand and generate a wide range of text, from simple sentences to complex paragraphs. Inputs Text prompts Outputs Generated text Capabilities The h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 model can be used for a variety of text-based tasks, such as language generation, text summarization, and translation. It can also be fine-tuned for specific applications, such as content creation, customer service, or creative writing. What can I use it for? The h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 model can be used for a wide range of applications, including content creation, customer service, and creative writing. Its versatility and strong performance make it a valuable tool for businesses and individuals looking to automate or enhance their text-based workflows. Things to try With the h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 model, you can try generating text on a wide range of topics, from creative stories to technical articles. You can also experiment with different prompts and techniques to see how the model performs on various tasks. Additionally, you can explore ways to fine-tune the model for your specific needs, such as by training it on domain-specific data.

Read more

Updated Invalid Date




Total Score


The h2o-danube2-1.8b-chat is a large language model fine-tuned for chat by H2O.ai. It is built on the Llama 2 architecture and contains around 1.8 billion parameters. The model is available in three versions: a base model, a Supervised Fine-Tuning (SFT) version, and an SFT model with additional Decoding-Prompt Optimization (DPO) tuning. The model was trained using H2O LLM Studio. Model inputs and outputs The h2o-danube2-1.8b-chat model is a text-to-text model, accepting conversational prompts as input and generating relevant responses. Inputs Conversational prompts in natural language Outputs Generated responses in natural language, up to 256 tokens long Capabilities The h2o-danube2-1.8b-chat model can engage in open-ended conversations, answering questions, and generating coherent and contextual responses. It demonstrates strong language understanding and generation capabilities. What can I use it for? The h2o-danube2-1.8b-chat model can be used for a variety of conversational AI applications, such as chatbots, virtual assistants, and dialogue systems. It can be fine-tuned further for specific domains or use cases to enhance its performance. The model's creators at H2O.ai provide guidance and resources for using the model. Things to try Developers can experiment with the h2o-danube2-1.8b-chat model by generating responses to a range of conversational prompts, observing its capabilities and limitations. The model can also be used as a starting point for further fine-tuning or adaptation to specific tasks or domains.

Read more

Updated Invalid Date




Total Score


h2o-danube-1.8b-chat is an AI model developed by h2oai with 1.8 billion parameters. It is a fine-tuned version of the Llama 2 architecture, incorporating sliding window attention from the Mistral model. The model was trained using the H2O LLM Studio. Similar models include the h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 which was also trained by H2O.ai. Model inputs and outputs Inputs Conversational context**: The model accepts conversational messages formatted using the HuggingFace chat template. Outputs Conversational response**: The model generates a response to the provided conversation, up to 256 new tokens. Capabilities The h2o-danube-1.8b-chat model demonstrates strong performance on various benchmarks, including commonsense reasoning, world knowledge, and reading comprehension tests. It can engage in open-ended conversations and provide informative responses on a wide range of topics. What can I use it for? You can use the h2o-danube-1.8b-chat model for building conversational AI applications, virtual assistants, and chatbots. Its broad knowledge and language understanding capabilities make it suitable for tasks such as customer service, question answering, and general-purpose dialogue. Things to try One interesting aspect of the h2o-danube-1.8b-chat model is its ability to handle longer input contexts, up to 16,384 tokens. This can enable more coherent and contextual responses in multi-turn conversations. You could experiment with providing the model with detailed prompts or task descriptions to see how it handles more complex inputs and generates relevant, informative responses.

Read more

Updated Invalid Date




Total Score


gpt2 is a transformer-based language model created and released by OpenAI. It is the smallest version of the GPT-2 model, with 124 million parameters. Like other GPT-2 models, gpt2 is a causal language model pretrained on a large corpus of English text using a self-supervised objective to predict the next token in a sequence. This allows the model to learn a general understanding of the English language that can be leveraged for a variety of downstream tasks. The gpt2 model is related to larger GPT-2 variations such as GPT2-Large, GPT2-Medium, and GPT2-XL, which have 355 million, 774 million, and 1.5 billion parameters respectively. These larger models were also developed and released by the OpenAI community. Model inputs and outputs Inputs Text sequence**: The model takes a sequence of text as input, which it uses to generate additional text. Outputs Generated text**: The model outputs a continuation of the input text sequence, generating new text one token at a time in an autoregressive fashion. Capabilities The gpt2 model is capable of generating fluent, coherent text in English on a wide variety of topics. It can be used for tasks like creative writing, text summarization, and language modeling. However, as the OpenAI team notes, the model does not distinguish fact from fiction, so it should not be used for applications that require the generated text to be truthful. What can I use it for? The gpt2 model can be used for a variety of text generation tasks. Researchers may use it to better understand the behaviors, capabilities, and biases of large-scale language models. The model could also be fine-tuned for applications like grammar assistance, auto-completion, creative writing, and chatbots. However, users should be aware of the model's limitations and potential for biased or harmful output, as discussed in the OpenAI model card. Things to try One interesting aspect of the gpt2 model is its ability to generate diverse and creative text from a given prompt. You can experiment with providing the model with different types of starting prompts, such as the beginning of a story, a description of a scene, or even a single word, and see what kind of coherent and imaginative text it generates in response. Additionally, you can try fine-tuning the model on a specific domain or task to see how its performance and output changes compared to the base model.

Read more

Updated Invalid Date