Everyone-Coder-4x7b-Base
Maintainer: rombodawg
41
❗
| 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 Everyone-Coder-4x7b-Base is a new Mixtral-type model created by leveraging experts that were fine-tuned by the community. This is the first model in the EveryoneLLM series, which aims to be a replacement or alternative to Mixtral-8x7b that is more suitable for general and specific use, as well as easier to fine-tune. The model was created by merging several expert models, including UNA-TheBeagle-7b-v1, openchat-3.5-0106-function-calling, WizardMath-7B-V1.1, and dolphin-2.6-mistral-7b-dpo-laser. The EveryoneLLM series aims to directly compete with Mistral's models, as Mistral has been secretive about the "secret sauce" that makes their Mixtral-Instruct model effective.
Model inputs and outputs
The Everyone-Coder-4x7b-Base is a text-to-text model, meaning it takes text as input and generates text as output. The model is designed to be a coding-specific model, with the goal of assisting users with a variety of programming tasks, such as debugging code, rewriting functions, optimizing scripts, and implementing new features.
Inputs
- Coding-related prompts: The model is trained on a variety of coding-related prompts, such as "Help me debug this code" or "Rewrite this function in Python".
- General language prompts: The model can also handle more general language prompts, such as "How do you" or "Explain the concept of".
Outputs
- Code-related responses: The model generates responses that assist with coding tasks, such as providing suggestions for debugging code, optimizing scripts, or implementing new features.
- Explanatory responses: The model can also generate responses that explain concepts or provide overviews on various topics.
Capabilities
The Everyone-Coder-4x7b-Base model is designed to be a versatile coding assistant, capable of handling a wide range of programming-related tasks. The model's strength lies in its ability to draw upon the expertise of the various models that were merged to create it, allowing it to provide high-quality, contextual responses to coding-related prompts.
What can I use it for?
The Everyone-Coder-4x7b-Base model can be a valuable tool for developers and programmers who need assistance with their coding tasks. Some potential use cases include:
- Code debugging and optimization: The model can help identify and fix issues in code, as well as suggest ways to optimize existing scripts and applications.
- Feature implementation: The model can provide guidance and suggestions for implementing new features or functionalities in a project.
- Code generation and rewriting: The model can generate or rewrite code snippets based on high-level descriptions or requirements.
- Conceptual understanding: The model can help explain programming concepts, algorithms, and best practices to users.
Things to try
One interesting aspect of the Everyone-Coder-4x7b-Base model is its ability to leverage the expertise of the various models that were merged to create it. Developers and researchers may want to experiment with prompts that target specific areas of expertise, such as math-focused prompts or prompts related to certain programming languages or frameworks. By exploring the model's capabilities across a range of coding-related tasks, users can gain a better understanding of its strengths and limitations, and how it can be most effectively utilized.
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
🗣️
mixtralnt-4x7b-test
56
The mixtralnt-4x7b-test model is an experimental AI model created by the maintainer chargoddard. It is a Sparse Mixture of Experts (MoE) model that combines parts from several pre-trained Mistral models, including Q-bert/MetaMath-Cybertron-Starling, NeverSleep/Noromaid-7b-v0.1.1, teknium/Mistral-Trismegistus-7B, meta-math/MetaMath-Mistral-7B, and PocketDoc/Dans-AdventurousWinds-Mk2-7b. The maintainer is experimenting with a hack to populate the MoE gates in order to take advantage of the experts. Model inputs and outputs The mixtralnt-4x7b-test model is a text-to-text model, meaning it takes text as input and generates text as output. The specific input and output formats are not clearly defined, but the maintainer suggests the model may use an "alpaca??? or chatml??? format". Inputs Text prompts in an unspecified format, potentially related to alpaca or chatml Outputs Generated text in response to the input prompts Capabilities The mixtralnt-4x7b-test model is capable of generating coherent text, taking advantage of the experts from the combined Mistral models. However, the maintainer is still experimenting with the hack used to populate the MoE gates, so the full capabilities of the model are not yet known. What can I use it for? The mixtralnt-4x7b-test model could potentially be used for a variety of text generation tasks, such as creative writing, conversational responses, or other applications that require generating coherent text. However, since the model is still in an experimental stage, it's unclear how it would perform compared to more established language models. Things to try One interesting aspect of the mixtralnt-4x7b-test model is the maintainer's approach of combining parts of several pre-trained Mistral models into a Sparse Mixture of Experts. This technique could lead to improvements in the model's performance and capabilities, but the results are still unknown. It would be worth exploring the model's output quality, coherence, and consistency to see how it compares to other language models.
Updated Invalid Date
📉
Mixtral-8x7B-v0.1
1.5K
The Mixtral-8x7B-v0.1 is a Large Language Model (LLM) developed by Mistral AI. It is a pretrained generative Sparse Mixture of Experts model that outperforms the Llama 2 70B model on most benchmarks tested. The model is available through the Hugging Face Transformers library and can be run in various precision levels to optimize memory and compute requirements. The Mixtral-8x7B-v0.1 is part of a family of Mistral models, including the mixtral-8x7b-instruct-v0.1, Mistral-7B-Instruct-v0.2, mixtral-8x7b-32kseqlen, mistral-7b-v0.1, and mistral-7b-instruct-v0.1. Model inputs and outputs Inputs Text**: The model takes text inputs and generates corresponding outputs. Outputs Text**: The model generates text outputs based on the provided inputs. Capabilities The Mixtral-8x7B-v0.1 model demonstrates strong performance on a variety of benchmarks, outperforming the Llama 2 70B model. It can be used for tasks such as language generation, text completion, and question answering. What can I use it for? The Mixtral-8x7B-v0.1 model can be used for a wide range of applications, including content generation, language modeling, and chatbot development. The model's capabilities make it well-suited for projects that require high-quality text generation, such as creative writing, summarization, and dialogue systems. Things to try Experiment with the model's capabilities by providing it with different types of text inputs and observe the generated outputs. You can also fine-tune the model on your specific data to further enhance its performance for your use case.
Updated Invalid Date
📊
Mistral-Nemo-Base-2407
232
The Mistral-Nemo-Base-2407 is a 12 billion parameter Large Language Model (LLM) jointly developed by Mistral AI and NVIDIA. It significantly outperforms existing models of similar size, thanks to its large training dataset that includes a high proportion of multilingual and code data. The model is released under the Apache 2 License and offers both pre-trained and instructed versions. Compared to similar models from Mistral, such as the Mistral-7B-v0.1 and Mistral-7B-v0.3, the Mistral-Nemo-Base-2407 has more than 12 billion parameters and a larger 128k context window. It also incorporates architectural choices like Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. Model Inputs and Outputs The Mistral-Nemo-Base-2407 is a text-to-text model, meaning it takes text as input and generates text as output. The model can be used for a variety of natural language processing tasks, such as language generation, text summarization, and question answering. Inputs Text prompts Outputs Generated text Capabilities The Mistral-Nemo-Base-2407 model has demonstrated strong performance on a range of benchmarks, including HellaSwag, Winogrande, OpenBookQA, CommonSenseQA, TruthfulQA, and MMLU. It also exhibits impressive multilingual capabilities, scoring well on MMLU benchmarks across multiple languages such as French, German, Spanish, Italian, Portuguese, Russian, Chinese, and Japanese. What Can I Use It For? The Mistral-Nemo-Base-2407 model can be used for a variety of natural language processing tasks, such as: Content Generation**: The model can be used to generate high-quality text, such as articles, stories, or product descriptions. Question Answering**: The model can be used to answer questions on a wide range of topics, making it useful for building conversational agents or knowledge-sharing applications. Text Summarization**: The model can be used to summarize long-form text, such as news articles or research papers, into concise and informative summaries. Code Generation**: The model's training on a large proportion of code data makes it a potential candidate for tasks like code completion or code generation. Things to Try One interesting aspect of the Mistral-Nemo-Base-2407 model is its large 128k context window, which allows it to maintain coherence and understanding over longer stretches of text. This could be particularly useful for tasks that require reasoning over extended context, such as multi-step problem-solving or long-form dialogue. Researchers and developers may also want to explore the model's multilingual capabilities and see how it performs on specialized tasks or domains that require cross-lingual understanding or generation.
Updated Invalid Date
🎲
Mixtral-8x22B-v0.1-4bit
53
The Mixtral-8x22B-v0.1-4bit is a large language model (LLM) developed by the Mistral AI community. It is a 176B parameter sparse mixture of experts model that can generate human-like text. Similar to the Mixtral-8x22B and Mixtral-8x7B models, the Mixtral-8x22B-v0.1-4bit uses a sparse mixture of experts architecture to achieve strong performance on a variety of benchmarks. Model inputs and outputs The Mixtral-8x22B-v0.1-4bit takes natural language text as input and generates fluent, human-like responses. It can be used for a wide range of language tasks such as text generation, question answering, and summarization. Inputs Natural language text prompts Outputs Coherent, human-like text continuations Responses to questions or instructions Summaries of given text Capabilities The Mixtral-8x22B-v0.1-4bit is a powerful language model capable of engaging in open-ended dialogue, answering questions, and generating human-like text. It has shown strong performance on a variety of benchmarks, outperforming models like LLaMA 2 70B on tasks like the AI2 Reasoning Challenge, HellaSwag, and Winogrande. What can I use it for? The Mixtral-8x22B-v0.1-4bit model could be useful for a wide range of natural language processing applications, such as: Chatbots and virtual assistants Content generation (articles, stories, poems, etc.) Summarization of long-form text Question answering Language translation Dialogue systems As a large language model, the Mixtral-8x22B-v0.1-4bit could be fine-tuned or used as a base for building more specialized AI applications across various domains. Things to try Some interesting things to try with the Mixtral-8x22B-v0.1-4bit model include: Experimenting with different prompting techniques to see how the model responds Evaluating the model's coherence and consistency across multiple turns of dialogue Assessing the model's ability to follow instructions and complete tasks Exploring the model's knowledge of different topics and its ability to provide informative responses Comparing the model's performance to other large language models on specific benchmarks or use cases By trying out different inputs and analyzing the outputs, you can gain a deeper understanding of the Mixtral-8x22B-v0.1-4bit's capabilities and limitations.
Updated Invalid Date