OmniDimen/OmniDimen-V1.5-4B-Emotion

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedLicense:mitArchitecture:Transformer0.0K Open Weights Warm

OmniDimen/OmniDimen-V1.5-4B-Emotion is a 4 billion parameter language model developed by OmniDimen, fine-tuned from Qwen3-4B-Instruct-2507. This model specializes in emotion recognition and generating emotionally-aware text, offering a 40960 token context length. It is designed for applications requiring nuanced understanding and generation of emotional content in text.

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OmniDimen-V1.5-4B-Emotion Overview

OmniDimen-V1.5-4B-Emotion is a 4 billion parameter language model, fine-tuned from Qwen3-4B-Instruct-2507 by OmniDimen. Its core specialization lies in emotion recognition and emotionally-aware text generation, distinguishing it from general-purpose LLMs. The model is provided in safetensor (BF16) format and also in GGUF (FP16 & Q4_K_M) for optimized deployment with tools like LM Studio, Ollama, and PocketPal.

Key Capabilities

  • Emotion-focused text generation: Excels at producing responses that understand and convey emotional nuances.
  • Emotion recognition: Designed to identify and process emotional cues in input text.
  • High context length: Supports a context window of up to 40960 tokens, allowing for more extensive emotional interactions.
  • Deployment flexibility: Available in safetensors for PyTorch setups and GGUF for efficient inference on various platforms.

Good For

  • Applications requiring empathetic AI interactions.
  • Generating creative content with specific emotional tones.
  • Use cases where understanding and responding to user emotions are critical.
  • Developers seeking a specialized model for emotional intelligence in text, particularly when informing the model of user identity to reduce AI hallucinations.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p