UsernameJustAnother/Nemo-12B-Marlin-v8
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:12BQuant:FP8Ctx Length:32kLicense:apache-2.0Architecture:Transformer0.0K Open Weights Warm

Nemo-12B-Marlin-v8 is a 12 billion parameter causal language model developed by UsernameJustAnother, fine-tuned from unsloth/Mistral-Nemo-Base-2407. Optimized for roleplay and storywriting, this model is designed to fit within 16GB of VRAM while supporting context lengths greater than 16K tokens. It was trained on a diverse dataset of approximately 10,000 records, including Reddit Writing Prompts, Claude instruct data, and curated chat logs, using Unsloth for efficient training.

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Nemo-12B-Marlin-v8: Roleplay and Storywriting Specialist

Developed by UsernameJustAnother, Nemo-12B-Marlin-v8 is a 12 billion parameter language model fine-tuned from the Mistral-Nemo-Base-2407 architecture. This iteration, version 8, focuses on enhancing its capabilities for roleplay and storywriting applications, aiming for high performance within a 16GB VRAM constraint and supporting context lengths exceeding 16,000 tokens.

Key Capabilities & Features

  • Optimized for Creative Writing: Specifically fine-tuned for generating engaging roleplay and story content.
  • Efficient VRAM Usage: Designed to operate effectively within 16GB of VRAM, making it accessible for a wider range of hardware.
  • Extended Context Length: Supports context lengths greater than 16K tokens, crucial for maintaining narrative coherence in long-form generation.
  • Diverse Training Data: Trained on an expanded dataset of approximately 10,000 records, including a mix of human conversations and stories from sources like Reddit Writing Prompts, Claude instruct data, and curated chat logs.
  • Unsloth Integration: Leverages Unsloth for faster and more memory-efficient training, enabling development on a single 80GB A100 GPU.

Good For

  • Developers and enthusiasts seeking a specialized model for generating creative text, particularly for roleplay scenarios and narrative development.
  • Applications requiring a balance of model size, VRAM efficiency, and extended context handling for text generation tasks.
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