Orca-2-7b

Maintainer: microsoft

Total Score

204

Last updated 5/21/2024

🗣️

PropertyValue
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

Orca-2-7b is a large language model created by Microsoft for research purposes. It is a fine-tuned version of the LLaMA-2 base model, with a focus on enhancing the model's reasoning abilities. The training data for Orca-2-7b was a synthetic dataset designed to improve the reasoning capabilities of smaller language models.

Model inputs and outputs

Orca-2-7b is designed to provide single-turn responses for tasks such as reasoning over user-provided data, reading comprehension, math problem-solving, and text summarization. The model is particularly focused on excelling in reasoning-related tasks.

Inputs

  • User-provided data, prompts, or instructions for the model to reason about or respond to

Outputs

  • Single-turn textual responses from the model, based on the provided inputs

Capabilities

Orca-2-7b is designed to be a research model, showcasing that capable models and complex workflows can be used to create synthetic data that can teach smaller language models new capabilities, such as reasoning. The model inherits capabilities and limitations from its LLaMA-2 base, but the additional training on the synthetic dataset is intended to enhance its reasoning abilities.

What can I use it for?

Orca-2-7b is intended for research purposes, allowing the research community to assess its abilities and use it as a foundation for building better frontier models. The model is not optimized for chatbot use cases and has not been trained with RLHF or DPO, so it is best used after being fine-tuned for a specific task or chat application.

Things to try

Researchers and developers can use Orca-2-7b to explore new approaches to enhancing the reasoning capabilities of language models, either by fine-tuning the model on additional datasets or by using it as a starting point for further model development and research. The model's performance on reasoning-focused benchmarks and tasks can also be investigated to better understand its strengths and limitations.



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

🏷️

Orca-2-13b

microsoft

Total Score

649

Orca-2-13b is a research model developed by Microsoft that aims to enhance the reasoning capabilities of small language models. It is a fine-tuned version of the LLAMA-2 base model, trained on a synthetic dataset created to improve its reasoning abilities. The model is not optimized for chatting and is best used after being fine-tuned for a specific task or after further training with RLHF or DPO. Similar models include StableBeluga2, which is a LLAMA2 70B model fine-tuned on an Orca-style dataset, and llama2-13b-orca-8k-3319, which is a fine-tuning of the LLAMA-2 13B model with an 8K context size on a long-conversation variant of the Dolphin dataset. Model inputs and outputs Orca-2-13b is designed for research purposes and provides single-turn responses in tasks such as reasoning over user-given data, reading comprehension, math problem-solving, and text summarization. The model is particularly focused on enhancing reasoning capabilities. Inputs User-provided data or instructions for the model to reason about and respond to Outputs Single-turn responses from the model, demonstrating its reasoning and problem-solving abilities Capabilities Orca-2-13b is focused on improving the reasoning capabilities of small language models. It has been evaluated on a wide range of tasks, including BigBench-Hard and AGIEval, and has shown significant improvements over its base LLAMA-2 model. What can I use it for? Orca-2-13b is intended for research purposes, to allow the research community to assess its abilities and provide a foundation for building better frontier models. The model could be useful for researchers and developers working on enhancing the reasoning capabilities of language models, as well as for applications that require strong reasoning skills, such as question-answering, math problem-solving, or text summarization. Things to try Researchers and developers could explore fine-tuning Orca-2-13b on specific datasets or tasks to further improve its performance. They could also investigate the model's capabilities in different areas, such as multi-step reasoning, logical inference, or grounding in real-world knowledge.

Read more

Updated Invalid Date

🛠️

StableBeluga2

stabilityai

Total Score

884

Stable Beluga 2 is a Llama2 70B model finetuned by Stability AI on an Orca-style dataset. It is part of a family of Beluga models, with other variants including StableBeluga 1 - Delta, StableBeluga 13B, and StableBeluga 7B. These models are designed to be highly capable language models that follow instructions well and provide helpful, safe, and unbiased assistance. Model inputs and outputs Stable Beluga 2 is an autoregressive language model that takes text as input and generates text as output. It can be used for a variety of natural language processing tasks, such as text generation, summarization, and question answering. Inputs Text prompts Outputs Generated text Responses to questions or instructions Capabilities Stable Beluga 2 is a highly capable language model that can engage in open-ended dialogue, answer questions, and assist with a variety of tasks. It has been trained to follow instructions carefully and provide helpful, safe, and unbiased responses. The model performs well on benchmarks for commonsense reasoning, world knowledge, and other important language understanding capabilities. What can I use it for? Stable Beluga 2 can be used for a variety of applications, such as: Building conversational AI assistants Generating creative writing or content Answering questions and providing information Summarizing text Providing helpful instructions and advice The model's strong performance on safety and helpfulness benchmarks make it well-suited for use cases that require a reliable and trustworthy AI assistant. Things to try Some interesting things to try with Stable Beluga 2 include: Engaging the model in open-ended dialogue to see the breadth of its conversational abilities Asking it to provide step-by-step instructions for completing a task Prompting it to generate creative stories or poems Evaluating its performance on specific language understanding benchmarks or tasks The model's flexibility and focus on safety and helpfulness make it a compelling choice for a wide range of natural language processing applications.

Read more

Updated Invalid Date

llama2-13b-orca-8k-3319

OpenAssistant

Total Score

132

The llama2-13b-orca-8k-3319 model is a fine-tuning of Meta's Llama2 13B model with an 8K context size, trained on a long-conversation variant of the Dolphin dataset called orca-chat. This extends the original Llama2 model's capabilities to handle longer contexts, which can be useful for applications like multi-document question answering and long-form summarization. Similar models like the codellama-13b-oasst-sft-v10 from OpenAssistant and the orca_mini_3b from pankajmathur also build on the Llama2 base model with various fine-tunings and adaptations. The LLaMA-2-7B-32K model from Together Computer further extends the context length to 32K tokens. Model inputs and outputs Inputs Text prompt**: The model can take in a text prompt of any length, up to the 8,192 token context limit. Outputs Continuation text**: The model will generate a continuation of the input text, producing a longer output sequence. Capabilities The llama2-13b-orca-8k-3319 model excels at generating coherent, contextual responses even for longer input prompts. This makes it well-suited for tasks like multi-turn conversations, where maintaining context over many exchanges is important. It can also be useful for applications that require understanding and summarizing longer-form content, such as research papers or novels. What can I use it for? This model could be used for a variety of language-based applications that benefit from handling longer input contexts, such as: Chatbots and dialog systems**: The extended context length allows the model to maintain coherence and memory over longer conversations. Question answering systems**: The model can draw upon more contextual information to provide better answers to complex, multi-part questions. Summarization tools**: The model's ability to process longer inputs makes it suitable for summarizing lengthy documents or articles. Things to try An interesting experiment would be to fine-tune the llama2-13b-orca-8k-3319 model further on a specific task or domain, such as long-form text generation or multi-document QA. The model's strong performance on the Dolphin dataset suggests it could be a powerful starting point for building specialized language models.

Read more

Updated Invalid Date

🚀

Orca-2-7B-GGUF

TheBloke

Total Score

56

The Orca-2-7B-GGUF model is a 7B parameter language model created by Microsoft and quantized by TheBloke. It is a variant of the original Orca 2 model, with the GGUF format supporting improved tokenization and extensibility compared to the previous GGML format. The GGUF quantized models provided by TheBloke offer a range of quantization options to balance model size, performance, and quality. This can be useful for deployment on devices with limited compute resources. Similar models available from TheBloke include the Orca-2-13B-GGUF and the Mistral-7B-OpenOrca-GGUF, which provide larger scale variants or alternative model architectures. Model inputs and outputs Inputs Text**: The model accepts arbitrary text input, which it uses to generate a continuation or response. Outputs Text**: The model outputs generated text, which can be a continuation of the input or a response to the input. Capabilities The Orca-2-7B-GGUF model demonstrates strong performance on a variety of language understanding and generation tasks, such as question answering, summarization, and open-ended dialogue. It can be used to generate coherent and contextually relevant text, drawing upon its broad knowledge base. What can I use it for? The Orca-2-7B-GGUF model could be useful for a wide range of natural language processing applications, such as: Chatbots and virtual assistants**: The model's dialogue capabilities make it well-suited for building conversational AI systems that can engage in helpful and engaging interactions. Content generation**: The model can be used to generate human-like text for tasks like creative writing, article summarization, and product description generation. Question answering and information retrieval**: The model's strong language understanding can enable it to provide informative and relevant responses to user queries. Things to try One interesting aspect of the Orca-2-7B-GGUF model is its ability to handle extended context and generate coherent text even for longer input sequences. This could be useful for applications that require maintaining context over multiple turns of dialogue or generating longer-form content. Experimenting with prompts that leverage this capability could yield interesting results. Another area to explore is the model's performance on specialized tasks or domains, such as technical writing, legal analysis, or scientific communication. The broad knowledge of the base model may need to be fine-tuned or adapted to excel in these more specialized areas.

Read more

Updated Invalid Date