Maintainer: OpenAssistant

Total Score


Last updated 5/27/2024

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

Create account to get full access


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

Model overview

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


  • Text prompt: The model can take in a text prompt of any length, up to the 8,192 token context limit.


  • Continuation text: The model will generate a continuation of the input text, producing a longer output sequence.


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.

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 llama2-70b-oasst-sft-v10 model is a fine-tuned version of Meta's Llama2 70B LLM developed by the Open-Assistant team. It was first fine-tuned on a mix of synthetic instructions and coding tasks, and then further refined on the best human demonstrations collected through the open-assistant.io platform up to July 23, 2023. This model aims to provide an engaging and helpful AI assistant. Similar models include the codellama-13b-oasst-sft-v10 which is a fine-tuning of Meta's CodeLlama 13B LLM, the llama2-13b-orca-8k-3319 which is a fine-tuning of the Llama2 13B model for long-form dialogue, and the stablelm-7b-sft-v7-epoch-3 which is a supervised fine-tuning of the StableLM 7B model. Model inputs and outputs Inputs Text prompts**: The model takes in text prompts that can include multiple turns of conversation between a user and an assistant, formatted using the OpenAI chatml standard. Outputs Continued conversation**: The model generates continued responses to the provided prompts, in the style of an engaging and helpful AI assistant. Capabilities The llama2-70b-oasst-sft-v10 model has been fine-tuned to engage in open-ended dialogue, answering questions, and assisting with a variety of tasks. It demonstrates strong performance on benchmarks for commonsense reasoning, world knowledge, and reading comprehension compared to other large language models. The model also exhibits improved safety and truthfulness compared to earlier versions, making it suitable for use cases requiring reliable and trustworthy responses. What can I use it for? The llama2-70b-oasst-sft-v10 model can be used to build engaging AI assistants for a variety of applications, such as customer support, task planning, research assistance, and creative ideation. Its broad knowledge and language understanding capabilities make it well-suited for open-ended conversations and complex question-answering. Developers can fine-tune or adapt the model further for specific use cases, leveraging the Hugging Face Transformers library and the Open-Assistant resources to integrate the model into their applications. Things to try One interesting aspect of the llama2-70b-oasst-sft-v10 model is its ability to engage in multi-turn conversations, maintaining context and continuity throughout the dialogue. Developers can experiment with prompting the model with longer conversation threads, observing how it maintains the flow of the discussion and provides relevant and coherent responses. Another aspect to explore is the model's safety and truthfulness features, which have been improved through the fine-tuning process. Developers can assess the model's outputs for potential biases, hallucinations, or unsafe content, and further fine-tune or prompt the model to ensure it behaves in an ethical and trustworthy manner for their specific use cases.

Read more

Updated Invalid Date




Total Score


The OpenAssistant-Llama2-13B-Orca-8K-3319-GGML is a large language model created by OpenAssistant and maintained by TheBloke. It is based on the Llama 2 transformer architecture and has been trained on a mix of publicly available data. TheBloke has provided a variety of quantized GGML model files to enable efficient CPU and GPU inference. This model can be compared to similar models like the OpenOrca-Platypus2-13B-GGML and Llama-2-13B-GGML, all of which leverage the Llama 2 architecture and have been quantized for efficient inference. The key differences are the specific training datasets and fine-tuning approaches used by each model. Model inputs and outputs Inputs Text**: The model takes natural language text as input and can be used for a variety of text generation tasks. Outputs Text**: The model outputs generated natural language text, which can be used for applications like story writing, question answering, and language modeling. Capabilities The OpenAssistant-Llama2-13B-Orca-8K-3319-GGML model is a powerful text generation model that can be used for a variety of tasks. It has shown strong performance on benchmarks like MMLU, BigBench-Hard, and AGIEval, and can generate coherent and contextually relevant text. The model is also designed with safety and helpfulness in mind, aiming to produce outputs that are socially unbiased and positive in nature. What can I use it for? The OpenAssistant-Llama2-13B-Orca-8K-3319-GGML model can be used for a wide range of natural language processing applications, such as: Content generation**: The model can be used to generate creative, informative, and engaging text content, such as articles, stories, or scripts. Question answering**: The model can be used to answer open-ended questions on a variety of topics, drawing upon its broad knowledge base. Dialogue systems**: The model can be used to build conversational AI assistants that can engage in natural, helpful, and context-aware dialogue. Language modeling**: The model can be used as a foundation for building more advanced language models or to fine-tune for specialized tasks. Things to try One interesting aspect of the OpenAssistant-Llama2-13B-Orca-8K-3319-GGML model is its focus on safety and helpfulness. Developers can experiment with different prompting strategies to encourage the model to generate outputs that are respectful, unbiased, and beneficial to users. For example, you could try providing the model with specific instructions or guidelines to follow, such as the Llama-2-Chat prompt template. Another interesting area to explore would be the model's performance on specialized tasks or domains, such as creative writing, technical writing, or question answering on specific subject areas. By fine-tuning the model or incorporating additional training data, you may be able to unlock even more capabilities and tailor the model to your specific use case. Overall, the OpenAssistant-Llama2-13B-Orca-8K-3319-GGML model represents an exciting advancement in large language models and offers a wide range of potential applications for developers and researchers to explore.

Read more

Updated Invalid Date




Total Score


orca_mini_13b is an OpenLLaMa-13B model fine-tuned on explain-tuned datasets. The dataset was created using instructions and input from WizardLM, Alpaca, and Dolly-V2 datasets, applying approaches from the Orca Research Paper. This helps the model learn the thought process from the teacher model, which is the GPT-3.5-turbo-0301 version of ChatGPT. Model inputs and outputs The orca_mini_13b model takes a combination of system prompts and user instructions as input, and generates relevant text responses as output. Inputs System prompt**: A prompt that sets the context for the model, describing the role and goals of the AI assistant. User instruction**: The task or query that the user wants the model to address. Input (optional)**: Additional context or information that the user provides to help the model complete the task. Outputs Response**: The model's generated text response to the user's instruction, which aims to provide a detailed, thoughtful, and step-by-step explanation. Capabilities The orca_mini_13b model is capable of generating high-quality, explain-tuned responses to a variety of tasks and queries. It demonstrates strong performance on reasoning-based benchmarks like BigBench-Hard and AGIEval, indicating its ability to engage in complex, logical thinking. What can I use it for? The orca_mini_13b model can be used for a range of applications that require detailed, step-by-step explanations, such as: Educational or tutoring applications Technical support and customer service Research and analysis tasks General question-answering and information retrieval By leveraging the model's explain-tuned capabilities, users can gain a deeper understanding of the topics and concepts being discussed. Things to try One interesting thing to try with the orca_mini_13b model is to provide it with prompts or instructions that require it to take on different expert roles, such as a logician, mathematician, or physicist. This can help uncover the model's breadth of knowledge and its ability to tailor its responses to the specific needs of the task at hand. Another interesting approach is to explore the model's performance on open-ended, creative tasks, such as generating poetry or short stories. The model's strong grounding in language and reasoning may translate into an ability to produce engaging and insightful creative output.

Read more

Updated Invalid Date




Total Score


The codellama-13b-oasst-sft-v10 model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B large language model (LLM). It was developed by the OpenAssistant team. This model is a continuation of the OpenAssistant project, which aims to create an open-sourced, safe, and useful AI assistant. Similar models from the OpenAssistant project include the StableLM-7B SFT-7 and LLAMA-30B SFT-6 models, which have also been fine-tuned on human-generated conversations to improve their performance on dialogue tasks. Model inputs and outputs Inputs The model takes text as input, which can include multiple turns of a conversation between a user and an assistant. Outputs The model generates text as output, continuing the conversation from the user's prompt. Capabilities The codellama-13b-oasst-sft-v10 model is capable of engaging in open-ended dialogue, answering questions, and generating informative and coherent text. It has been trained to provide helpful and safe responses, and can be used for a variety of language generation tasks. What can I use it for? The codellama-13b-oasst-sft-v10 model can be used to build conversational AI applications, such as virtual assistants, chatbots, and question-answering systems. It could also be fine-tuned further for specialized tasks, such as code generation, summarization, or creative writing, by training on domain-specific data. Things to try One interesting thing to try with the codellama-13b-oasst-sft-v10 model is to engage it in multi-turn conversations, where the model can demonstrate its ability to maintain context and provide consistent, coherent responses over the course of an exchange. Additionally, you could prompt the model with open-ended questions or tasks to see the breadth of its capabilities.

Read more

Updated Invalid Date