rocket-3B

Maintainer: pansophic

Total Score

75

Last updated 5/28/2024

🔍

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

Create account to get full access

or

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

Model overview

rocket-3B is a 3 billion parameter large language model developed by pansophic that was trained on a mix of publicly available datasets using Direct Preference Optimization (DPO). This model sets a new standard for 3B parameter models, outperforming several much larger models on key benchmarks. For example, rocket-3B achieves a higher MT-Bench score than the 65B parameter Guanaco model, and an Alpaca Eval win rate of 79.75%, comparable to the 33B Vicuna v1.3 model. This impressive performance in a compact 3B model is due to the DPO training approach and the use of the ChatML prompt format.

Model inputs and outputs

rocket-3B is a text-to-text model that can be used for a variety of natural language processing tasks. It takes prompts in the ChatML format as input and generates text responses.

Inputs

  • Prompt: A prompt in the ChatML format, e.g. <|user|>List 3 synonyms for the word "tiny"<|endoftext|>

Outputs

  • Generated text: The model's response to the input prompt, e.g. <|assistant|>1. Dwarf\n2. Little\n3. Petite<|endoftext|>

Capabilities

rocket-3B demonstrates strong performance on a range of natural language tasks, including question answering, summarization, and language generation. Its compact size and efficient design make it a powerful tool for applications that require fast and accurate language processing without the need for large, resource-intensive models.

What can I use it for?

rocket-3B can be used as a foundation model for a variety of NLP applications, such as chatbots, virtual assistants, and content generation. Its versatility and strong performance make it a compelling choice for developers and researchers looking to leverage the capabilities of large language models in their projects.

Things to try

One interesting aspect of rocket-3B is its ability to generate long-form, coherent text. Try providing the model with a prompt that requires a detailed, multi-paragraph response, and observe how it is able to maintain context and flow over an extended sequence. This can be a useful feature for applications that require in-depth explanations or narratives.



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

🐍

stablelm-zephyr-3b

stabilityai

Total Score

230

StableLM Zephyr 3B is a 3 billion parameter instruction tuned language model developed by Stability AI. It was trained on a mix of publicly available datasets and synthetic datasets using Direct Preference Optimization (DPO). The model was fine-tuned from stabilityai/stablelm-3b-4e1t and has shown strong performance on benchmarks like MT Bench and Alpaca Benchmark. It is similar in approach to the Zephyr 7B model, which was fine-tuned from mistralai/Mistral-7B-v0.1 and also used DPO. Model inputs and outputs StableLM Zephyr 3B is an auto-regressive language model that generates text based on provided prompts. The model uses a specific input format with user and assistant messages delimited by special tokens: Inputs Text prompt following the format: [User prompt] Outputs Completion of the user prompt, with the assistant's response delimited by special tokens: [Assistant response] Capabilities StableLM Zephyr 3B has been shown to perform well on a variety of natural language tasks, including answering questions, generating coherent text, and following instructions. The model can be particularly useful for building chatbots and virtual assistants that engage in helpful and natural conversations. What can I use it for? You can use StableLM Zephyr 3B to build a wide range of natural language processing applications, such as: Chatbots and virtual assistants Content generation (e.g. articles, stories, poetry) Question answering systems Code generation and programming assistance To use the model commercially, please refer to the Stability AI membership options. Things to try One interesting aspect of StableLM Zephyr 3B is its use of Direct Preference Optimization (DPO) during training. This approach aims to align the model's outputs with human preferences, which can make the model more helpful and less likely to generate problematic content. You could experiment with prompts that test the model's alignment, such as asking it to generate text on sensitive topics or to complete tasks that require ethical reasoning. Another unique feature of the model is its long context support, with a sequence length of up to 4096 tokens. This allows the model to maintain coherence and context over longer passages of text. You could try prompting the model with multi-paragraph inputs to see how it handles longer-form tasks.

Read more

Updated Invalid Date

👨‍🏫

neural-chat-7b-v3-3

Intel

Total Score

71

The neural-chat-7b-v3-3 model is a fine-tuned 7B parameter large language model (LLM) from Intel. It was trained on the meta-math/MetaMathQA dataset and aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. The model was originally fine-tuned from the mistralai/Mistral-7B-v0.1 model. This model achieves state-of-the-art performance compared to similar 7B parameter models on various language tasks. Model inputs and outputs The neural-chat-7b-v3-3 model is a text-to-text transformer model that takes natural language text as input and generates natural language text as output. It can be used for a variety of language-related tasks such as question answering, dialogue, and summarization. Inputs Natural language text prompts Outputs Generated natural language text Capabilities The neural-chat-7b-v3-3 model demonstrates impressive performance on a wide range of language tasks, including question answering, dialogue, and summarization. It outperforms many similar-sized models on benchmarks such as the Open LLM Leaderboard, showcasing its strong capabilities in natural language understanding and generation. What can I use it for? The neural-chat-7b-v3-3 model can be used for a variety of language-related applications, such as building conversational AI assistants, generating helpful responses to user queries, summarizing long-form text, and more. Due to its strong performance on benchmarks, it could be a good starting point for developers looking to build high-quality language models for their projects. Things to try One interesting aspect of the neural-chat-7b-v3-3 model is its ability to handle long-form inputs and outputs, thanks to its 8192 token context length. This makes it well-suited for tasks that require reasoning over longer sequences, such as question answering or dialogue. You could try using the model to engage in extended conversations and see how it performs on tasks that require maintaining context over multiple turns. Additionally, the model's strong performance on mathematical reasoning tasks, as demonstrated by its results on the MetaMathQA dataset, suggests that it could be a useful tool for building applications that involve solving complex math problems. You could experiment with prompting the model to solve math-related tasks and see how it performs.

Read more

Updated Invalid Date

👀

neural-chat-7b-v3

Intel

Total Score

65

The neural-chat-7b-v3 is a 7B parameter large language model (LLM) fine-tuned by Intel on the open source Open-Orca/SlimOrca dataset. The model was further aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. This fine-tuned model builds upon the base mistralai/Mistral-7B-v0.1 model. Intel has also released similar fine-tuned models like neural-chat-7b-v3-1 and neural-chat-7b-v3-3, which build on top of this base model with further fine-tuning and optimization. Model Inputs and Outputs Inputs Text prompts of up to 8192 tokens, which is the same context length as the base mistralai/Mistral-7B-v0.1 model. Outputs Continuation of the input text, generating coherent and contextually relevant responses. Capabilities The neural-chat-7b-v3 model can be used for a variety of language-related tasks such as question answering, language generation, and text summarization. The model's fine-tuning on the Open-Orca/SlimOrca dataset and alignment using DPO is intended to improve its performance on conversational and open-ended tasks. What Can I Use It For? You can use the neural-chat-7b-v3 model for different language-related projects and applications. Some potential use cases include: Building chatbots and virtual assistants Generating coherent text for creative writing or storytelling Answering questions and providing information on a wide range of topics Summarizing long-form text into concise summaries To see how the model is performing on various benchmarks, you can check the LLM Leaderboard. Things to Try One interesting aspect of the neural-chat-7b-v3 model is its ability to adapt to different prompting styles and templates. You can experiment with providing the model with system prompts or using chat-based templates like the one provided in the how-to-use section to see how it responds in a conversational setting. Additionally, you can try fine-tuning or further optimizing the model for your specific use case, as the model was designed to be adaptable to a variety of language-related tasks.

Read more

Updated Invalid Date

⛏️

neural-chat-7b-v3-2

Intel

Total Score

53

The neural-chat-7b-v3-2 model is a fine-tuned 7B parameter Large Language Model (LLM) developed by the Intel team. It was trained on the meta-math/MetaMathQA dataset using the Direct Performance Optimization (DPO) method. This model was originally fine-tuned from the Intel/neural-chat-7b-v3-1 model, which was in turn fine-tuned from the mistralai/Mistral-7B-v-0.1 model. According to the Medium blog, the neural-chat-7b-v3-2 model demonstrates significantly improved performance compared to the earlier versions. Model inputs and outputs Inputs Prompts**: The model takes in text prompts as input, which can be in the form of a conversational exchange between a user and an assistant. Outputs Text generation**: The model outputs generated text that continues or responds to the provided prompt. The output is an attempt to provide a relevant and coherent continuation of the input text. Capabilities The neural-chat-7b-v3-2 model can be used for a variety of language-related tasks, such as open-ended dialogue, question answering, and text summarization. The model's fine-tuning on the MetaMathQA dataset suggests it may have particular strengths in understanding and generating text around mathematical concepts and reasoning. What can I use it for? This model can be used for a wide range of language tasks, from chatbots and virtual assistants to content generation and augmentation. Developers can fine-tune the model further on domain-specific data to adapt it for their particular use cases. The LLM Leaderboard provides a good overview of the model's performance on various benchmarks, which can help inform how it might be applied. Things to try One interesting aspect of the neural-chat-7b-v3-2 model is its potential for mathematical reasoning and problem-solving, given its fine-tuning on the MetaMathQA dataset. Developers could explore using the model to generate step-by-step explanations for math problems, or to assist users in understanding complex mathematical concepts. The model's broader language understanding capabilities also make it well-suited for tasks like open-ended dialogue, creative writing, and content summarization.

Read more

Updated Invalid Date