NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kLicense:apache-2.0Architecture:Transformer0.0K Open Weights Cold

NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 is a 7 billion parameter language model fine-tuned from Open-Orca/Mistral-7B-OpenOrca. This model is further instruction-tuned using the OpenAssistant/oasst_top1_2023-08-25 dataset, which includes multilingual conversational data. It is designed for general-purpose conversational AI and instruction following, supporting a 4096-token context length.

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Model Overview

NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 is a 7 billion parameter language model built upon the robust Open-Orca/Mistral-7B-OpenOrca base model. This version undergoes an additional instruction-tuning phase using the OpenAssistant/oasst_top1_2023-08-25 dataset, enhancing its ability to follow instructions and engage in conversational exchanges.

Key Capabilities

  • Instruction Following: Improved ability to understand and execute user instructions due to fine-tuning on a high-quality instruction dataset.
  • Multilingual Support: Benefits from the diverse language coverage of the OpenAssistant dataset, which includes 20 different languages.
  • Conversational AI: Optimized for generating coherent and contextually relevant responses in dialogue scenarios.
  • Context Length: Supports a context window of 4096 tokens, allowing for more extensive conversations and detailed prompts.

Training and Data

This model leverages the OpenAssistant/oasst_top1_2023-08-25 dataset for its instruction-tuning. This dataset is known for its high-quality, human-annotated conversational turns across a wide array of languages, contributing to the model's versatility in understanding and generating human-like text.

Use Cases

  • General-purpose chatbots: Ideal for applications requiring interactive dialogue.
  • Instruction-based tasks: Suitable for scenarios where the model needs to follow specific commands or generate content based on detailed prompts.
  • Multilingual applications: Can be applied in environments requiring understanding and generation across multiple languages.