thetoprated76/pMistral-7B-Instruct-v0.2

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kTool Calling:SupportedPublished:Sep 20, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The thetoprated76/pMistral-7B-Instruct-v0.2 is an instruction-tuned 7 billion parameter large language model developed by Mistral AI. It is a fine-tuned version of Mistral-7B-v0.2, featuring an expanded 32k context window and optimized for instruction-following tasks. This model is designed for general-purpose conversational AI and text generation, leveraging its increased context length for more complex interactions.

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Model Overview

The thetoprated76/pMistral-7B-Instruct-v0.2 is an instruction-tuned large language model based on the Mistral-7B-v0.2 architecture, developed by Mistral AI. This version builds upon its predecessor with significant enhancements, primarily focusing on an expanded context window and improved instruction-following capabilities.

Key Capabilities & Features

  • Instruction Following: Fine-tuned to respond effectively to user instructions, making it suitable for conversational agents and task-oriented applications.
  • Expanded Context Window: Features a 32k token context window, a substantial increase from the 8k context of v0.1, allowing for processing and generating longer, more coherent texts.
  • Rope-theta Optimization: Incorporates a Rope-theta value of 1e6, contributing to better handling of longer sequences.
  • No Sliding-Window Attention: Unlike some previous versions, this model does not utilize Sliding-Window Attention.
  • Hugging Face Transformers Compatibility: Supports inference via the transformers library, with specific instructions provided for optimal use.

Good For

  • General-purpose Chatbots: Its instruction-tuned nature makes it well-suited for interactive conversational AI.
  • Text Generation: Capable of generating diverse and contextually relevant text based on prompts.
  • Applications Requiring Longer Context: The 32k context window is beneficial for tasks that involve processing or generating extensive documents or dialogues.

Limitations

As an instruct fine-tuned model, it serves as a demonstration of compelling performance but currently lacks built-in moderation mechanisms. Users should implement their own guardrails for deployment in environments requiring moderated outputs.

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