eridai/erida-Inari-50125
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Oct 1, 2025Architecture:Transformer Warm

eridai/erida-Inari-50125 is an 0.8 billion parameter, Russian-language causal language model developed by eridai, featuring a 32768-token context length. This model is specifically designed for conversational AI in Russian, incorporating a unique "thinking mode" (enable_thinking) to generate internal reasoning steps before producing a final response. Its primary use case is as a friendly, helpful assistant for Russian-speaking users, providing detailed and context-aware answers.

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erida-Inari-50125: A Russian-Language Conversational AI

erida-Inari-50125 is an 0.8 billion parameter causal language model developed by eridai, specifically optimized for Russian-language interactions. It features a substantial 32768-token context window, enabling it to handle lengthy conversations and complex prompts. A key differentiator of this model is its enable_thinking mode, which allows the model to generate internal reasoning steps before formulating its final response, enhancing the coherence and relevance of its output.

Key Capabilities

  • Russian Language Proficiency: Designed and optimized for generating natural and contextually appropriate responses in Russian.
  • "Thinking Mode" (enable_thinking): Provides an internal reasoning process, allowing the model to plan its response and consider potential user needs or ambiguities before generating text. This can lead to more thoughtful and robust answers.
  • Extended Context Window: Supports up to 32768 tokens, suitable for detailed discussions and maintaining context over long interactions.
  • Friendly Assistant Persona: Configured to act as a benevolent and helpful assistant, providing clear and understandable explanations.

Good for

  • Russian-speaking Chatbots: Ideal for building conversational agents that interact with users in Russian.
  • Customer Support in Russian: Can be used to provide detailed and empathetic responses to user queries.
  • Educational Tools: Explaining complex topics in Russian with a clear, step-by-step reasoning process.
  • Content Generation: Creating coherent and contextually rich text in Russian, especially where internal thought processes are beneficial for quality.