Hello2pariksit/Mistral-7B-Instruct-v0.3-neuron

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Apr 22, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Mistral-7B-Instruct-v0.3 is a 7 billion parameter instruction-tuned large language model developed by Mistral AI, based on the Mistral-7B-v0.3 architecture. This version features an extended vocabulary to 32768, supports a v3 Tokenizer, and includes native function calling capabilities. It is primarily designed for instruct following and tool use, making it suitable for conversational AI and automated task execution.

Loading preview...

Overview

Mistral-7B-Instruct-v0.3 is an instruction-tuned variant of the Mistral-7B-v0.3 base model, developed by Mistral AI. This 7 billion parameter model builds upon its predecessor with several key enhancements, focusing on improved utility for developers and advanced interaction patterns.

Key Capabilities

  • Extended Vocabulary: Features an expanded vocabulary of 32768 tokens, allowing for broader language representation and potentially better performance across diverse inputs.
  • v3 Tokenizer Support: Utilizes an updated v3 Tokenizer, which can lead to more efficient tokenization and improved model understanding.
  • Native Function Calling: A significant differentiator, this model natively supports function calling, enabling it to interact with external tools and APIs. This capability is crucial for building agents and automating complex workflows.
  • Instruct Following: Optimized for understanding and executing instructions, making it highly effective for chat applications and task-oriented dialogues.

Good for

  • Conversational AI: Its instruction-tuned nature makes it well-suited for chatbots and interactive agents that need to follow user commands.
  • Tool Use and Automation: The integrated function calling feature allows developers to build applications where the model can dynamically invoke external functions, such as fetching real-time data or performing actions.
  • Developers using mistral-inference or transformers: The model provides clear installation and usage examples for both mistral-inference and Hugging Face transformers libraries, including specific code snippets for instruct following and function calling.