Beyonder-4x7B-v2
Maintainer: mlabonne
120
🛸
| 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 Beyonder-4x7B-v2 is a Mixture of Experts (MoE) model created by mlabonne using the mergekit tool. It combines four base models: openchat/openchat-3.5-1210, beowolx/CodeNinja-1.0-OpenChat-7B, maywell/PiVoT-0.1-Starling-LM-RP, and WizardLM/WizardMath-7B-V1.1. This MoE architecture enables the model to leverage the strengths of these diverse base models, potentially leading to improved capabilities.
Model inputs and outputs
Inputs
- The recommended context length for
Beyonder-4x7B-v2is 8k.
Outputs
- The model can generate natural language responses based on the provided input.
Capabilities
The Beyonder-4x7B-v2 model displays competitive performance on the Open LLM Leaderboard compared to the larger 8-expert Mixtral-8x7B-Instruct-v0.1 model, despite only having 4 experts. It also shows significant improvements over the individual expert models.
Additionally, the Beyonder-4x7B-v2 performs very well on the Nous benchmark suite, coming close to the performance of the much larger 34B parameter Yi-34B fine-tuned model, while only using around 12B parameters.
What can I use it for?
The Beyonder-4x7B-v2 model can be used for a variety of natural language processing tasks, such as open-ended conversation, question answering, and task completion. Its strong performance on the Nous benchmark suggests it may be particularly well-suited for instruction following and reasoning tasks.
Things to try
Experiment with the model's capabilities by prompting it to complete a wide range of tasks, from creative writing to analytical problem-solving. Pay attention to how it handles different types of inputs and whether its responses demonstrate strong reasoning and language understanding abilities.
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
👁️
Beyonder-4x7B-v3
54
Beyonder-4x7B-v3 is an improvement over the popular Beyonder-4x7B-v2 model. It is a Mixture of Experts (MoE) model that combines four specialized models using LazyMergekit: mlabonne/AlphaMonarch-7B beowolx/CodeNinja-1.0-OpenChat-7B SanjiWatsuki/Kunoichi-DPO-v2-7B mlabonne/NeuralDaredevil-7B Model Inputs and Outputs The Beyonder-4x7B-v3 model uses a context window of 8k tokens. It is designed to work well with the Mistral Instruct chat template, which is compatible with LM Studio. Inputs Text prompts for a variety of tasks, including chat, code generation, role-playing, and math problems. Outputs Responses generated by the model, which can include: Coherent and contextual conversations Code snippets for various programming languages Detailed role-playing narratives Solutions to mathematical problems Capabilities The Beyonder-4x7B-v3 model is a well-rounded AI assistant capable of handling a diverse range of tasks. By combining four specialized experts, the model can leverage different capabilities to provide high-quality responses. For example, the model can engage in natural conversations while also demonstrating strong coding and problem-solving abilities. The role-playing expert allows the model to create immersive narrative experiences. What Can I Use It For? The Beyonder-4x7B-v3 model can be used for a variety of applications, including: Conversational AI assistants: The model's strong conversational abilities make it suitable for building chatbots and virtual assistants. Content creation: The model's versatility allows it to assist with tasks like creative writing, scriptwriting, and story generation. Educational tools: The model's problem-solving and explanatory skills can be leveraged to create interactive learning experiences. Programming assistance: The model's coding capabilities can help developers with tasks like code generation, debugging, and algorithm design. Things to Try One interesting aspect of the Beyonder-4x7B-v3 model is its use of a Mixture of Experts (MoE) architecture. This approach allows the model to leverage the strengths of multiple specialized models, leading to improved overall performance. To get the most out of the model, you can experiment with different inference parameters, such as temperature, top-k, and top-p, to find the settings that work best for your specific use case. Additionally, you can try leveraging the model's versatility by combining its different capabilities, such as using its coding skills to help with a math problem or its storytelling abilities to enhance a conversational experience.
Updated Invalid Date
⚙️
NeuralBeagle14-7B
151
The NeuralBeagle14-7B is a 7B parameter language model developed by mlabonne that is based on a merge of several large language models, including fblgit/UNA-TheBeagle-7b-v1 and argilla/distilabeled-Marcoro14-7B-slerp. It was fine-tuned using the argilla/distilabel-intel-orca-dpo-pairs dataset and Direct Preference Optimization (DPO). This model is claimed to be one of the best performing 7B models available. Model inputs and outputs Inputs Text inputs of up to 8,192 tokens Outputs Fluent text outputs generated in response to the input Capabilities The NeuralBeagle14-7B model demonstrates strong performance on instruction following and reasoning tasks compared to other 7B language models. It can also be used for roleplaying and storytelling. What can I use it for? The NeuralBeagle14-7B model can be used for a variety of text-to-text tasks, such as language generation, question answering, and text summarization. Its capabilities make it well-suited for applications like interactive storytelling, virtual assistants, and educational tools. Things to try You can experiment with the NeuralBeagle14-7B model by using it to generate creative fiction, engage in open-ended conversations, or tackle challenging reasoning problems. Its strong performance on instruction following and reasoning tasks suggests it may be a useful tool for developing advanced language applications.
Updated Invalid Date
🔎
NeuralBeagle14-7B-GGUF
45
NeuralBeagle14-7B is a 7B parameter language model that was fine-tuned from mlabonne/Beagle14-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset and a direct preference optimization (DPO) training process. According to the maintainer, this model displays good performance in instruction following and reasoning tasks, and can also be used for role-playing and storytelling. Compared to other 7B models, NeuralBeagle14-7B is considered one of the best-performing models in this size range. Model inputs and outputs NeuralBeagle14-7B is a text-to-text language model, meaning it takes text as input and generates text as output. It uses a context window of 8,192 tokens and is compatible with different templates, like chatml and Llama's chat template. Inputs Text prompts for the model to generate a response to Outputs Coherent and contextually relevant text generated by the model, based on the input prompt Capabilities NeuralBeagle14-7B displays strong performance on a variety of benchmarks, including instruction following, reasoning, and truthfulness tasks. According to the evaluation results, it outperforms other 7B models like mlabonne/Beagle14-7B, mlabonne/NeuralDaredevil-7B, and argilla/distilabeled-Marcoro14-7B-slerp. What can I use it for? NeuralBeagle14-7B can be used for a variety of natural language processing tasks, including: Conversational AI and chatbots Assistants for task completion and information retrieval Creative writing and storytelling Role-playing and interactive narratives The model's strong performance on reasoning and truthfulness tasks also makes it potentially useful for educational applications and decision support systems. Things to try One interesting thing to try with NeuralBeagle14-7B is exploring how it handles more open-ended and creative prompts, such as world-building exercises or collaborative storytelling. Its ability to reason and follow instructions may lend itself well to these types of tasks, allowing for engaging and imaginative interactions.
Updated Invalid Date
🔍
NeuralHermes-2.5-Mistral-7B
148
The NeuralHermes-2.5-Mistral-7B model is a fine-tuned version of the OpenHermes-2.5-Mistral-7B model. It was developed by mlabonne and further trained using Direct Preference Optimization (DPO) on the mlabonne/chatml_dpo_pairs dataset. The model surpasses the original OpenHermes-2.5-Mistral-7B on most benchmarks, ranking as one of the best 7B models on the Open LLM leaderboard. Model inputs and outputs The NeuralHermes-2.5-Mistral-7B model is a text-to-text model that can be used for a variety of natural language processing tasks. It accepts text input and generates relevant text output. Inputs Text**: The model takes in text-based input, such as prompts, questions, or instructions. Outputs Text**: The model generates text-based output, such as responses, answers, or completions. Capabilities The NeuralHermes-2.5-Mistral-7B model has demonstrated strong performance on a range of tasks, including instruction following, reasoning, and question answering. It can engage in open-ended conversations, provide creative responses, and assist with tasks like writing, analysis, and code generation. What can I use it for? The NeuralHermes-2.5-Mistral-7B model can be useful for a wide range of applications, such as: Conversational AI**: Develop chatbots and virtual assistants that can engage in natural language interactions. Content Generation**: Create text-based content, such as articles, stories, or product descriptions. Task Assistance**: Provide support for tasks like research, analysis, code generation, and problem-solving. Educational Applications**: Develop interactive learning tools and tutoring systems. Things to try One interesting thing to try with the NeuralHermes-2.5-Mistral-7B model is to use the provided quantized models to explore the model's capabilities on different hardware setups. The quantized versions can be deployed on a wider range of devices, making the model more accessible for a variety of use cases.
Updated Invalid Date