Openchat
Models by this creator
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openchat_3.5
1.1K
The openchat_3.5 model is an open-source language model developed by openchat. It is part of the OpenChat library, which aims to create high-performance, commercially viable, open-source large language models. The openchat_3.5 model is fine-tuned using a strategy called C-RLFT, which allows it to learn from mixed-quality data without preference labels. This model is capable of achieving performance on par with ChatGPT, even with a 7 billion parameter size, as demonstrated by its strong performance on the MT-bench benchmark. Similar models include the openchat_3.5-awq model and the openchat-3.5-1210-gguf model, both of which are also part of the OpenChat library and aim to push the boundaries of open-source language models. Model inputs and outputs The openchat_3.5 model is a text-to-text transformer model, capable of generating human-like text in response to input prompts. It takes natural language text as input and produces natural language text as output. Inputs Natural language text prompts Outputs Generated natural language text responses Capabilities The openchat_3.5 model is capable of a wide range of text generation tasks, including answering questions, summarizing information, and engaging in open-ended conversations. It has demonstrated strong performance on benchmark tasks, outperforming larger 70 billion parameter models in some cases. What can I use it for? The openchat_3.5 model can be used for a variety of applications, such as building chatbots, virtual assistants, and content generation tools. Its open-source nature and strong performance make it an attractive option for developers and researchers looking to leverage advanced language models in their projects. Additionally, the OpenChat team is committed to making their models commercially viable, which could open up opportunities for monetization and enterprise-level deployments. Things to try One interesting aspect of the openchat_3.5 model is its ability to learn from mixed-quality data without preference labels, thanks to the C-RLFT fine-tuning strategy. Developers could explore how this approach affects the model's performance and biases compared to more traditional fine-tuning methods. Additionally, the model's small size (7 billion parameters) compared to its strong performance could make it an attractive option for deployment on resource-constrained devices or in scenarios where model size is a concern.
Updated 5/28/2024
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openchat-3.5-0106
336
openchat-3.5-0106 is an innovative open-source language model developed by openchat. This model is the overall best performing open-source 7B model, outperforming ChatGPT (March) and Grok-1 with a 15-point improvement in coding over the previous OpenChat-3.5 version. The model has two primary modes - a generalist mode and a mode focused on mathematical reasoning, with experimental support for evaluator and feedback capabilities. Compared to similar models like openchat_3.5, openchat_3.5-awq, and openchat-3.5-1210-gguf, openchat-3.5-0106 offers improved performance across a range of benchmarks, including a 15-point boost in coding tasks. Model inputs and outputs Inputs Text**: The model accepts text inputs, which can be prompts, questions, or any other natural language text. Outputs Text**: The model generates text outputs, which can be responses, answers, or any other natural language text. Capabilities openchat-3.5-0106 demonstrates strong performance on a variety of tasks, including coding, mathematical reasoning, question answering, and more. The model's two distinct modes allow it to excel in both generalist and specialized applications. In the generalist mode, the model can assist with a wide range of tasks such as text generation, summarization, and question answering. In the mathematical reasoning mode, the model shines in solving complex mathematical problems and explaining step-by-step solutions. What can I use it for? The openchat-3.5-0106 model can be used in a variety of applications, such as: Chatbots and virtual assistants**: The model's strong natural language understanding and generation capabilities make it well-suited for building conversational AI systems. Content generation**: The model can be used to generate high-quality text, such as articles, stories, or creative writing. Question answering and knowledge retrieval**: The model can be used to answer a wide range of questions and retrieve relevant information from its training data. Coding and programming assistance**: The model's specialized mathematical reasoning mode can help with tasks like generating code snippets, explaining coding concepts, and solving algorithmic problems. Things to try One interesting aspect of openchat-3.5-0106 is its experimental support for evaluator and feedback capabilities. Users can try providing the model with feedback on its responses and observe how it adapts and improves over time. This feature could be particularly useful for building more personalized and responsive conversational AI systems. Another interesting thing to try is using the model's mathematical reasoning mode to tackle complex problems that require step-by-step explanations. The model's ability to provide detailed solutions and walk through the reasoning process can be a valuable tool for educational or research-oriented applications.
Updated 5/28/2024
🗣️
openchat
289
The openchat model is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations. According to the maintainer, the OpenChat models are designed to achieve high performance with limited data, with only 6K GPT-4 conversations filtered from the 90K ShareGPT conversations used for fine-tuning. The OpenChat-3.5-0106 model in particular is described as the "Overall Best Performing Open Source 7B Model" for coding, generalization, and mathematical reasoning tasks. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on various benchmarks. Model inputs and outputs The openchat model accepts conversational inputs in a specific format, with an `` token marking the end of each turn. The model can operate in different modes, including a "Default Mode (GPT4 Correct)" for general tasks and a "Mathematical Reasoning Mode" tailored for solving math problems. Inputs Conversational inputs**: The model expects a sequence of conversational turns, with each turn separated by the `` token. Mode selection**: The model can be instructed to operate in different modes, such as "Default Mode (GPT4 Correct)" or "Mathematical Reasoning Mode", by including a mode identifier in the input. Outputs Conversational responses**: The model generates a response to the provided conversational input, which can be used to continue the conversation. Task-specific outputs**: Depending on the mode, the model can produce outputs tailored for tasks like mathematical problem-solving or general language understanding. Capabilities The openchat-3.5-0106 model excels at a variety of tasks, including summarization, question answering, extraction, and classification. It has demonstrated strong performance on benchmarks like MT-Bench, HumanEval, and GSM8K, often outperforming larger proprietary models. What can I use it for? The openchat models are suitable for a wide range of applications, from building open-source chatbots and virtual assistants to integrating language understanding capabilities into educational or creative tools. The maintainers encourage using the models for research purposes, such as probing the limitations and biases of dialogue models or exploring safe deployment strategies. Things to try One interesting aspect of the openchat models is their ability to operate in different modes, allowing users to tailor the model's behavior to specific types of tasks. For example, you could experiment with the "Mathematical Reasoning Mode" to see how the model performs on math-focused prompts, or try the "Default Mode (GPT4 Correct)" for more general language understanding and generation tasks. Another area to explore is the model's few-shot capabilities, as the maintainers note that the model often performs even better with few-shot prompts. This could be a valuable avenue for further research and development.
Updated 5/28/2024
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openchat-3.5-1210
277
The openchat-3.5-1210 model is a 7B parameter AI model developed by the openchat team. It is the "Overall Best Performing Open Source 7B Model" according to the maintainers, outperforming ChatGPT (March) and Grok-1 on several benchmarks. The model is capable of both coding and general language tasks, with a 15-point improvement in Coding over the previous OpenChat-3.5 model. The openchat-3.5-0106 and openchat_3.5 are similar high-performing open-source models from the same team, with the openchat_3.5-awq and openchat-3.5-1210-gguf variants also available. All these models leverage the team's C-RLFT (Constrained Reinforcement Learning from Trajectories) fine-tuning approach to achieve exceptional results from limited training data. Model inputs and outputs Inputs Text prompts**: The model can take in text prompts from users, which can include instructions, questions, or open-ended requests. Conversation history**: The model is designed to maintain context across multiple turns of a conversation, allowing users to build upon previous exchanges. Conditional inputs**: The model supports setting a "condition" (e.g. "Code", "Math Correct") to adjust its behavior for specialized tasks. Outputs Generated text**: The primary output of the model is coherent, contextually relevant text generated in response to the input prompts. Code generation**: The model can generate code snippets when provided with appropriate programming prompts. Numeric outputs**: The model can perform basic mathematical reasoning and provide numeric outputs for problems. Capabilities The openchat-3.5-1210 model has demonstrated strong performance across a variety of benchmarks, including MT-Bench, HumanEval, and GSM8K. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on several tasks, showcasing its capabilities in areas like coding, mathematical reasoning, and general language understanding. The model also supports specialized "Coding" and "Mathematical Reasoning" modes, which can be accessed by providing the appropriate conditional input. These modes allow the model to focus on more technical tasks and further enhance its capabilities in those domains. What can I use it for? The openchat-3.5-1210 model can be a valuable tool for a wide range of applications, from chatbots and virtual assistants to content generation and code development. Its strong performance on benchmarks suggests it could be useful for tasks like: Chatbots and virtual assistants**: The model's ability to maintain conversation context and generate coherent responses makes it suitable for building interactive chatbots and virtual assistants. Content generation**: The model can be used to generate creative writing, articles, and other types of text content. Code development**: The model's coding capabilities can be leveraged to assist with tasks like code generation, explanation, and debugging. Educational applications**: The model's mathematical reasoning abilities could be employed in educational tools and tutoring systems. Things to try One interesting aspect of the openchat-3.5-1210 model is its ability to adjust its behavior based on the provided "condition" input. For example, you could try prompting the model with a simple math problem and observe how it responds in the "Mathematical Reasoning" mode, compared to its more general language understanding capabilities. Additionally, the model's strong performance on coding tasks suggests it could be a valuable tool for developers. You could try providing the model with various coding challenges or prompts and see how it handles them, exploring its capabilities in areas like algorithm design, syntax generation, and code explanation.
Updated 5/28/2024
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openchat_8192
220
openchat_8192 is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations by the openchat team. The models are based on the LLaMA-13B foundation model, with the openchat_8192 variant extending the context length to 8192 tokens. Compared to similar open-source models like OpenCoderPlus, openchat_8192 achieves higher performance despite using only ~6K fine-tuning conversations, a fraction of the data used by other models. The openchat_8192 model scored 106.6% of ChatGPT's Vicuna GPT-4 evaluation score and 79.5% of its win-rate on the AlpacaEval benchmark. Model inputs and outputs Inputs User question**: The user's input text to be processed by the model. Conversation history**: The model can accept multi-turn conversation history to provide context-aware responses. Outputs Generative text response**: The model generates a relevant and coherent response to the user's input, continuing the conversation. Capabilities The openchat_8192 model exhibits strong performance across a variety of benchmarks, demonstrating its capabilities in areas like open-ended conversation, task-oriented dialogue, and even mathematical reasoning. Despite its relatively small size compared to large language models like GPT-4, openchat_8192 can match or exceed the performance of these larger models on certain tasks. What can I use it for? The openchat_8192 model would be well-suited for building open-domain chatbots, virtual assistants, and other conversational AI applications. Its high performance on benchmarks like Vicuna GPT-4 and AlpacaEval suggests it could be used as a drop-in replacement for commercial language models in many use cases, while benefiting from the open-source and permissive licensing. Things to try One interesting aspect of the openchat_8192 model is its ability to perform well with limited training data. This could make it an attractive option for developers who want to fine-tune a language model for their specific use case but have access to only a small dataset. Experimenting with different fine-tuning strategies and dataset curation techniques could yield further performance improvements. Another area to explore is the model's capabilities in mathematical reasoning and coding tasks. The provided benchmarks show promising results, and developers could investigate integrating the openchat_8192 model into applications that require these abilities, such as programming assistants or educational tools.
Updated 5/28/2024
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openchat-3.6-8b-20240522
124
openchat-3.6-8b-20240522 is the latest open-source language model released by the openchat team. It builds upon their previous 7B model, openchat-3.5-0106, which demonstrated comparable performance to ChatGPT on a variety of benchmarks. The new 8B model further improves on the previous version, outperforming Llama-3-8B-Instruct and other open-source finetuned models across key metrics. Model Inputs and Outputs Inputs Text**: The model accepts natural language text as input, which can include prompts, questions, or conversational messages. Context Length**: The model supports up to 8192 tokens of context, allowing it to engage in more extended interactions. Outputs Text Generation**: Given an input text, the model can generate coherent and contextually relevant output text. This can include responses to prompts, answers to questions, or continuations of conversations. Numerical Outputs**: In addition to text generation, the model can also handle tasks that require numerical outputs, such as mathematical reasoning and problem-solving. Capabilities The openchat-3.6-8b-20240522 model demonstrates strong performance across a wide range of natural language tasks. It excels at general conversation, coding assistance, and mathematical reasoning, often outperforming more parameter-intensive models like Llama-3-8B-Instruct. For example, the model can engage in thoughtful and nuanced dialogue, drawing upon its broad knowledge base to provide insightful responses. It also shows impressive capabilities in writing code, debugging, and explaining programming concepts. Additionally, the model can tackle complex mathematical problems, step-by-step, and provide accurate numerical solutions. What Can I Use It For? The openchat-3.6-8b-20240522 model can be a valuable tool for a variety of applications, from conversational AI assistants to educational and scientific applications. Some potential use cases include: Chatbots and Virtual Assistants**: Integrate the model into conversational interfaces to provide natural and helpful responses to user queries. Code Generation and Debugging**: Utilize the model's coding capabilities to assist developers in writing, understanding, and troubleshooting code. Educational Applications**: Leverage the model's ability to explain concepts and solve problems to create interactive learning experiences. Research and Scientific Computing**: Explore the model's potential in areas like mathematical modeling, data analysis, and scientific communication. Things to Try One interesting aspect of the openchat-3.6-8b-20240522 model is its ability to adapt its language style and tone to the given context. For example, you can prompt the model to take on different personas, such as a helpful assistant, a witty conversationalist, or an authoritative expert, and observe how it adjusts its responses accordingly. Another intriguing area to explore is the model's potential for open-ended reasoning and creative problem-solving. Try posing it with complex, multi-step challenges or open-ended prompts and see how it approaches and tackles these tasks. Overall, the openchat-3.6-8b-20240522 model represents a significant step forward in the development of high-performing, open-source language models. Its versatility and strong performance make it an exciting tool for a wide range of applications and research endeavors.
Updated 6/26/2024
🎯
opencoderplus
104
OpenCoderPlus is a series of open-source language models fine-tuned by openchat on a diverse and high-quality dataset of multi-round conversations. With only 6K GPT-4 conversations filtered from the 90K ShareGPT conversations, OpenCoderPlus is designed to achieve high performance with limited data. The model is based on the StarCoderPlus architecture and has a native 8192 context length. It achieves 102.5% of the ChatGPT score on the Vicuna GPT-4 evaluation and 78.7% win-rate on the AlpacaEval benchmark. Model inputs and outputs OpenCoderPlus is a text-to-text AI model that takes in user queries or instructions and generates relevant responses. The model uses a conversation template that involves concatenating tokens, including an end-of-turn token ` with the eot_token_id`. Inputs User questions or instructions Outputs Relevant responses generated by the model Capabilities OpenCoderPlus demonstrates strong performance on a variety of tasks, including coding, programming, and general language understanding. It outperforms ChatGPT on the Vicuna GPT-4 evaluation and achieves a high win-rate on the AlpacaEval benchmark, showcasing its capability to engage in high-level conversations and complete complex tasks. What can I use it for? OpenCoderPlus can be used for a wide range of applications, such as conversational AI assistants, code generation and completion, and knowledge-intensive tasks. The model's ability to perform well with limited training data makes it an attractive option for open-source and resource-constrained projects. Potential use cases include building AI-powered chatbots, automating software development workflows, and enhancing educational tools. Things to try One interesting aspect of OpenCoderPlus is its ability to maintain performance while using only a fraction of the training data compared to other models. This highlights the potential for open-source models to achieve strong results without requiring massive datasets. Developers and researchers may want to explore ways to further optimize the model's architecture and fine-tuning process to push the boundaries of what is possible with limited resources.
Updated 5/28/2024
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openchat-3.5-0106-gemma
50
The openchat-3.5-0106-gemma model is the highest performing 7B Gemma variant in the world. It was trained by openchat using their C-RLFT approach on the openchat-3.5-0106 dataset. This model achieves similar performance to the Mistral-based OpenChat model, and significantly outperforms the base Gemma-7B and Gemma-7B-it models. Model inputs and outputs Inputs Text prompts and instructions for the model to generate responses to Outputs Coherent, fluent text outputs generated by the model in response to the input prompts The model can produce a wide variety of text outputs including answers to questions, dialogue, summaries, code, and more Capabilities The openchat-3.5-0106-gemma model demonstrates strong performance across a range of benchmarks, including machine translation, code generation, mathematical reasoning, and open-ended language tasks. It outperforms previous open-source large language models like OpenChat-3.5 and ChatGPT (March) on many metrics. The model's robust training on a diverse dataset allows it to handle a variety of use cases effectively. What can I use it for? The openchat-3.5-0106-gemma model can be used for a wide range of text generation tasks. Some potential use cases include: Powering chatbots and conversational AI systems Generating creative content like stories, poems, and scripts Summarizing long-form text like research papers or reports Assisting with coding and software development by generating code snippets Providing informative responses to open-ended questions As an open-source model, openchat-3.5-0106-gemma democratizes access to state-of-the-art language AI capabilities that can be deployed on consumer hardware. Developers and researchers can leverage this model to build innovative applications and explore the boundaries of large language models. Things to try One interesting aspect of the openchat-3.5-0106-gemma model is its strong performance on coding and mathematical reasoning tasks, outperforming previous open-source models. Developers could experiment with using the model to generate code snippets, solve programming challenges, or provide explanations for mathematical concepts. Additionally, the model's robust training on diverse data sources means it may be able to handle specialized domains and tasks better than more narrowly-focused language models. Researchers could explore using openchat-3.5-0106-gemma as a foundation for further fine-tuning or prompt engineering to tackle domain-specific problems.
Updated 5/28/2024
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openchat_v3.2
42
The openchat_v3.2 model is an open-source language model developed by the openchat team. It is based on supervised fine-tuning (SFT) and leverages the ~80k ShareGPT conversations to achieve strong performance despite its simple methods. The team's vision is to develop a high-performance, open-source, and commercially available large language model, and they are continuously making progress. The openchat_v3.2 model ranks #1 out of 13B open-source models, with an 89.5% win-rate on the AlpacaEval benchmark and a 7.01 score on the MT-bench leaderboard. It is also available for free commercial use under the Llama 2 Community License. Model inputs and outputs Inputs Messages**: The model takes a series of messages, with each message containing a "role" (either "user" or "assistant") and "content" (the actual text of the message). Outputs Completed message**: The model generates a continuation of the provided messages, producing a new message with the "assistant" role. Capabilities The openchat_v3.2 model exhibits strong performance across a variety of tasks, particularly in areas like open-ended conversation, task-oriented dialogue, and general language understanding. Its efficient fine-tuning process allows for quick deployment in applications that require a high-throughput language model. What can I use it for? The openchat_v3.2 model can be used for a wide range of natural language processing applications, such as chatbots, virtual assistants, content generation, and language understanding tasks. Its open-source nature and commercial availability make it an attractive option for developers and businesses looking to incorporate a capable language model into their products or services. Things to try One key advantage of the openchat_v3.2 model is its efficient fine-tuning process. Developers can quickly fine-tune the model on their own data or task-specific instructions, allowing for rapid deployment and iteration. Additionally, the model's strong performance on benchmarks like AlpacaEval and MT-bench suggests it could be a valuable tool for applications that require robust language understanding and generation capabilities.
Updated 9/6/2024