Vikhrmodels/it-5.4-fp16-orpo-v2

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Jul 8, 2024Architecture:Transformer0.0K Warm

Vikhrmodels/it-5.4-fp16-orpo-v2 is an 8 billion parameter instruction-tuned language model based on the Mistral architecture. Developed by Vikhrmodels, it was trained on translated GPT-4 instructions and responses, then further refined using ORPO (Optimized Reinforcement Learning from Human Feedback) on an internal dataset. This model is designed to provide diverse and high-quality responses, making it suitable for general conversational AI and instruction-following tasks.

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Vikhrmodels/it-5.4-fp16-orpo-v2: Instruction-Tuned Mistral Model

This model is an 8 billion parameter instruction-tuned variant of the Mistral 5th version architecture, developed by Vikhrmodels. It has been trained on a dataset comprising translated instructions and responses from GPT-4, and its performance was further enhanced through the application of the ORPO (Optimized Reinforcement Learning from Human Feedback) method using an internal dataset.

Key Capabilities & Characteristics

  • Instruction Following: Designed to accurately follow and respond to user instructions.
  • Response Diversity: Exhibits a high diversity in its generated answers, making interactions more natural and less repetitive.
  • ORPO Fine-tuning: Leverages ORPO for improved alignment and response quality, building upon a base of GPT-4 generated data.
  • Recommended Usage: For optimal results, it is recommended to use a temperature setting within the range of [0.1, 0.4] during generation to balance creativity and coherence.

Performance

Preliminary metrics on the ru_arena_general benchmark indicate its performance in a Russian language context, suggesting its suitability for applications requiring robust instruction-following in Russian.

Good For

  • General conversational AI applications.
  • Instruction-based text generation.
  • Tasks requiring diverse and nuanced responses, particularly in Russian.