Svngoku/qwen3-black-mirror
Svngoku/qwen3-black-mirror is a 0.6 billion parameter Qwen3-based language model developed by Svngoku, fine-tuned on Black Mirror episode data. This model is optimized for generating coherent text with thematic influences of surveillance, privacy, and ratings, reflecting the show's core themes. It is designed for creative text generation within the Black Mirror narrative style.
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Model Overview
Svngoku/qwen3-black-mirror is a specialized language model developed by Svngoku, built upon the Qwen3-0.6B-Base architecture. This model has been fine-tuned using 33 episodes across 7 seasons of the "Black Mirror" series, with LoRA weights merged into the base model for standalone deployment.
Key Capabilities
- Thematic Text Generation: Excels at producing text that incorporates themes of surveillance, privacy, and ratings, characteristic of the "Black Mirror" universe.
- Efficient Training: The model was trained significantly faster using Unsloth and Huggingface's TRL library.
- Coherent Output: Inference tests indicate the model generates coherent text with a distinct thematic influence.
Training Details
The model underwent 3 epochs of training over 15 steps, with a training time of just 25 seconds. The loss decreased from 3.08 to 2.09 by epoch 2, settling at 2.54 by epoch 3. The final merged 16-bit model is 1.19 GB.
Good For
- Generating creative content, stories, or dialogues in the style of "Black Mirror."
- Exploring narrative themes related to technology, society, and human nature.
- Rapid prototyping of themed text generation applications.