Openassistant
Models by this creator
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oasst-sft-6-llama-30b-xor
946
The oasst-sft-6-llama-30b-xor model is an AI model created by OpenAssistant, a collective of AI researchers and developers. It is a 30 billion parameter language model based on the LLaMA architecture developed by Meta AI. Due to licensing restrictions, OpenAssistant provides the model weights in an XOR-encoded format rather than distributing the original LLaMA weights directly. Similar models include the oasst-sft-7-llama-30b-xor model, which uses the same process to create a 30 billion parameter LLaMA-based model, as well as the Llama-2-7b-hf and Meta-Llama-3-8B-Instruct models from Meta which are based on their own LLaMA architecture. Model inputs and outputs Inputs Text**: The model accepts text as input and generates additional text in response. Outputs Text**: The model outputs generated text, which can be used for a variety of natural language processing tasks. Capabilities The oasst-sft-6-llama-30b-xor model is a powerful text generation model capable of producing human-like responses on a wide range of topics. It can be used for tasks such as chatbots, content generation, language translation, and more. The large 30 billion parameter size allows the model to capture a significant amount of knowledge and generate coherent, context-aware text. What can I use it for? The oasst-sft-6-llama-30b-xor model can be used for a variety of natural language processing projects. Some potential use cases include: Chatbots and Conversational AI**: The model can be fine-tuned to engage in helpful, open-ended conversations on a wide range of topics. Content Generation**: The model can be used to generate articles, stories, poems, and other types of text content. Language Translation**: The model's strong understanding of language can be leveraged for translation tasks between supported languages. Question Answering**: The model can be used to answer questions by generating relevant, informative responses. Things to try One interesting aspect of the oasst-sft-6-llama-30b-xor model is its ability to handle long-form text generation. Unlike some smaller language models, this 30 billion parameter model can maintain context and coherence over extended passages of generated text. Try prompting the model with open-ended questions or creative writing prompts and see how it builds out a complete, multi-paragraph response. Another area to explore is the model's performance on specialized tasks like code generation or mathematical problem-solving. While the model was not explicitly trained for these domains, its broad knowledge and language understanding capabilities may allow it to tackle such challenges with some fine-tuning or prompt engineering.
Updated 5/28/2024
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oasst-sft-4-pythia-12b-epoch-3.5
356
The oasst-sft-4-pythia-12b-epoch-3.5 is the 4th iteration of the English supervised fine-tuning (SFT) model from the Open-Assistant project. It is based on the Pythia 12B model from EleutherAI, which was fine-tuned on human demonstrations of assistant conversations collected through the open-assistant.io platform before March 25, 2023. This model can be compared to similar Open-Assistant models like the StableLM-7B SFT-7 and the Llama2 70B SFT v10, which were fine-tuned on different language model backbones. Model Inputs and Outputs The oasst-sft-4-pythia-12b-epoch-3.5 model uses special tokens to mark the beginning of user and assistant turns: ` and . Each turn ends with a ` token. For example, an input prompt might look like: What is a meme, and what's the history behind this word? The model will then generate a response to the user's prompt, continuing the conversation. Inputs Dialogue prompts with special tokens marking user and assistant turns Outputs Continuations of the dialogue, generated by the model to respond to the user's prompt Capabilities The oasst-sft-4-pythia-12b-epoch-3.5 model is a powerful language model that can engage in open-ended dialogue and tackle a variety of tasks, such as answering questions, providing explanations, and generating creative text. It has been fine-tuned on a large dataset of human-written assistant responses, which allows it to produce more natural and contextually-appropriate responses compared to a model trained only on generic text. What Can I Use It For? The oasst-sft-4-pythia-12b-epoch-3.5 model could be used as the foundation for building conversational AI assistants, chatbots, or other applications that require natural language understanding and generation. Its strong performance on a wide range of tasks makes it a versatile model that could be further fine-tuned or adapted for specific use cases. Things to Try One interesting aspect of the oasst-sft-4-pythia-12b-epoch-3.5 model is its ability to engage in multi-turn dialogues. You could try providing the model with a series of prompts and see how it continues the conversation, maintaining context and coherence over multiple exchanges. Additionally, you could experiment with different prompting styles or task-specific instructions to see how the model's responses change.
Updated 5/28/2024
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oasst-sft-1-pythia-12b
279
The oasst-sft-1-pythia-12b is the first iteration English supervised-fine-tuning (SFT) model of the Open-Assistant project. It is based on a Pythia 12B that was fine-tuned on ~22k human demonstrations of assistant conversations collected through the open-assistant.io human feedback web app before March 7, 2023. This model was developed by the Open-Assistant Contributors. The oasst-sft-4-pythia-12b-epoch-3.5 is the 4th iteration of the Open-Assistant SFT model, fine-tuned on a larger dataset of human demonstrations collected through the same web app before March 25, 2023. The stablelm-7b-sft-v7-epoch-3 is another iteration of the Open-Assistant SFT model, this time fine-tuning the StableLM-7B base model. The llama2-70b-oasst-sft-v10 and codellama-13b-oasst-sft-v10 models are fine-tunings of Meta's Llama2 70B and CodeLlama 13B models respectively, using a mix of synthetic instructions, coding tasks, and the best human demonstrations from Open-Assistant. Model inputs and outputs Inputs Text prompts, which can contain multiple turns of conversation between a user and an assistant, marked with special tokens ` and , and ending each turn with `. Outputs Continuations of the conversation, generated by the model after the `` token. Capabilities The oasst-sft-1-pythia-12b model is capable of engaging in open-ended conversations, drawing upon the knowledge it was fine-tuned on to provide informative and coherent responses. It can discuss a wide range of topics such as explaining the history and meaning of the term "meme". The model demonstrates strong language understanding and generation abilities. What can I use it for? The oasst-sft-1-pythia-12b and other Open-Assistant models could be used as a starting point for building conversational AI assistants or chatbots. By further fine-tuning or combining these models with other techniques, developers can create helpful virtual assistants for tasks like customer support, tutoring, or general information lookup. Things to try One interesting aspect of the Open-Assistant models is their use of the ` and ` tokens to mark the different speakers in a conversation. This structural information could be leveraged to enable more natural multi-turn dialog, where the model maintains context and coherence across multiple exchanges. Developers could experiment with prompting strategies that take advantage of this capability.
Updated 5/28/2024
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reward-model-deberta-v3-large-v2
182
The reward-model-deberta-v3-large-v2 is a model trained by OpenAssistant to predict which generated answer is better judged by a human, given a question. This reward model (RM) can be useful for evaluating QA models, serving as a reward score in RLHF, and detecting potential toxic responses via ranking. The model was trained on datasets including webgpt_comparisons, summarize_from_feedback, synthetic-instruct-gptj-pairwise, and anthropic_hh-rlhf. Model inputs and outputs Inputs Question**: The question to be answered Answer**: The generated answer to be evaluated Outputs Score**: A score indicating the quality of the generated answer Capabilities The reward-model-deberta-v3-large-v2 can be used to evaluate the quality of generated answers for a given question. It can help determine which answer is better, which can be useful for improving QA models or detecting potential toxic responses. What can I use it for? The reward-model-deberta-v3-large-v2 model can be used for a variety of applications, such as: QA model evaluation**: Use the model to score and compare the quality of answers generated by different QA models. Reward score in RLHF**: Leverage the model's scoring mechanism as a reward signal to fine-tune language models using Reinforcement Learning from Human Feedback (RLHF). Toxic response detection**: Use the model to detect potentially toxic or harmful responses by comparing the scores of different candidate responses. Things to try One key insight about the reward-model-deberta-v3-large-v2 model is its ability to detect and score the quality of generated answers. This can be particularly useful for improving QA systems or identifying potentially toxic responses, which can help ensure the safe and responsible deployment of language models.
Updated 5/27/2024
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llama2-13b-orca-8k-3319
131
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.
Updated 5/27/2024
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llama2-70b-oasst-sft-v10
73
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.
Updated 5/28/2024
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stablelm-7b-sft-v7-epoch-3
67
The stablelm-7b-sft-v7-epoch-3 model is a 7 billion parameter language model developed by the Open-Assistant project. It is an iteration of their English supervised-fine-tuning (SFT) model, based on the stabilityai/stablelm-base-alpha-7b model. This model was fine-tuned on human demonstrations of assistant conversations collected through the https://open-assistant.io/ web app before April 12, 2023. The model uses special tokens to mark the beginning of user and assistant turns, with each turn ending with an `` token. This allows the model to generate coherent and contextual responses in a conversational format. Model inputs and outputs Inputs Conversational prompts marked with ` and ` tokens Outputs Conversational responses generated by the model Capabilities The stablelm-7b-sft-v7-epoch-3 model is capable of engaging in open-ended conversations, answering questions, and providing helpful information. It can also generate creative content like stories and poems. The model has been trained to be helpful and harmless, and will refuse to participate in anything that could be considered harmful to the user. What can I use it for? The stablelm-7b-sft-v7-epoch-3 model can be used as a foundational base model for developing conversational AI assistants. It can be fine-tuned on specific tasks or datasets to create custom applications, such as chatbots, virtual assistants, or language-based interfaces. The model's broad knowledge and language understanding capabilities make it a versatile tool for a wide range of natural language processing projects. Things to try One interesting aspect of the stablelm-7b-sft-v7-epoch-3 model is its ability to engage in multi-turn conversations. By providing prompts that include both user and assistant turns, you can observe how the model maintains context and generates coherent responses. This can be a useful starting point for exploring the model's conversational capabilities and how they could be applied to real-world scenarios.
Updated 5/28/2024
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codellama-13b-oasst-sft-v10
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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.
Updated 5/23/2024
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oasst-sft-7-llama-30b-xor
63
The oasst-sft-7-llama-30b-xor model is an open-source language model developed by OpenAssistant. It is a fine-tuned version of Meta AI's LLaMA 30B model, with the original LLaMA weights converted to an XOR format due to licensing restrictions. This process enables the distribution of LLaMA-based models while respecting the original model's licensing terms. Similar models include the oasst-sft-6-llama-30b-xor and llama2-70b-oasst-sft-v10 models, which are also fine-tuned versions of LLaMA-based models by OpenAssistant. Model Inputs and Outputs The oasst-sft-7-llama-30b-xor model is a causal language model, which means it generates text one token at a time, conditioning on the previous tokens. The model takes as input a sequence of text tokens and generates a continuation of that sequence. Inputs A sequence of text tokens Outputs A continuation of the input text, generated one token at a time Capabilities The oasst-sft-7-llama-30b-xor model can be used for a variety of natural language processing tasks, such as text generation, language understanding, and language translation. It has been trained on a diverse dataset, enabling it to generate coherent and contextually relevant text on a wide range of topics. What Can I Use it For? The oasst-sft-7-llama-30b-xor model can be used in a variety of applications, such as: Content generation**: The model can be used to generate text for blogs, articles, stories, or other creative content. Chatbots and virtual assistants**: The model can be fine-tuned or used as a base for building conversational AI systems. Language translation**: The model can be fine-tuned for language translation tasks, leveraging its understanding of multiple languages. Text summarization**: The model can be used to generate concise summaries of longer text. Things to Try Some interesting things to try with the oasst-sft-7-llama-30b-xor model include: Exploring the model's capabilities in different domains**: Try prompting the model with topics or tasks outside of its training distribution, such as coding, math, or scientific writing, to see how it performs. Experimenting with prompt engineering**: Craft different types of prompts, such as open-ended questions, instructions, or dialogue, to see how the model responds. Evaluating the model's safety and ethical considerations**: Carefully test the model's outputs for potential biases, hallucinations, or other undesirable behaviors, and think about ways to mitigate these issues. Overall, the oasst-sft-7-llama-30b-xor model is a powerful and flexible language model that can be leveraged for a wide range of natural language processing tasks. By exploring its capabilities and limitations, you can gain valuable insights and potentially develop innovative applications.
Updated 5/28/2024