AfkaraLP/gemma-3-270M-coralmc-1ep-8batch-v13

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.3BQuant:BF16Context Size:32kPublished:Apr 26, 2026Architecture:Transformer Featherless Exclusive Cold

AfkaraLP/gemma-3-270M-coralmc-1ep-8batch-v13 is a compact 0.3 billion parameter Gemma-based language model, fine-tuned by AfkaraLP specifically for generating realistic chat conversations in the style of CoralMC. It features a 32768-token context length and is optimized for conditional and unconditional chat generation, as well as single-user response simulation. This model excels at producing authentic, context-aware chat snippets, making it ideal for applications requiring dynamic and natural dialogue generation within a gaming or community chat environment.

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Overview

AfkaraLP/gemma-3-270M-coralmc-1ep-8batch-v13 is a specialized 0.3 billion parameter Gemma-based language model, fine-tuned by AfkaraLP using Unsloth for generating realistic chat conversations, particularly in the style of CoralMC. It is designed to handle various chat generation tasks, including unconditional snippets, conversation continuations, and single-user replies, with a context window of up to 32768 tokens. The model's training focused on a specific chat format, ensuring high fidelity to the [username]: message structure.

Key Capabilities

  • Realistic Chat Generation: Produces authentic chat snippets, continuations, and user-specific replies.
  • Context-Aware Dialogue: Capable of continuing conversations based on provided history, maintaining coherence.
  • Specific User Impersonation: Can generate replies as a designated user within a conversation context.
  • Optimized for CoralMC Style: Adheres to a strict [username]: message format, reflecting its training data.

Good For

  • Simulating Chat Environments: Ideal for creating dynamic and realistic chat logs for testing, development, or content generation.
  • Game Development: Useful for populating game worlds with believable NPC dialogue or simulating player interactions.
  • Dialogue Systems: Can serve as a base for building interactive chat agents that mimic human conversation patterns.
  • Content Creation: Generating chat-based narratives or examples for documentation and tutorials.

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