openchat
Maintainer: openchat
289
🗣️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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 <|end_of_turn|> 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
<|end_of_turn|>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.
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
✅
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 Invalid Date
🎯
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 Invalid Date
🔄
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 Invalid Date
🧠
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 Invalid Date