Mistral-Nemo-Base-2407
Maintainer: mistralai
232
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| 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 |
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Model Overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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