DreadPoor/Krix-12B-Model_Stock

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:12BQuant:FP8Ctx Length:32kPublished:Oct 7, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

DreadPoor/Krix-12B-Model_Stock is a 12 billion parameter language model created by DreadPoor, formed by merging four distinct models: Ingredient_A-TEST, Ingredient_B-TEST, Ingredient_C-TEST, and Ingredient_D-TEST. This model utilizes the 'model_stock' merge method, building upon DreadPoor/Famino-12B-Model_Stock as its base. It is designed for general language tasks, leveraging the combined strengths of its constituent models.

Loading preview...

Krix-12B-Model_Stock Overview

Krix-12B-Model_Stock is a 12 billion parameter language model developed by DreadPoor. It is a composite model, created through the strategic merging of four distinct base models: DreadPoor/Ingredient_A-TEST, DreadPoor/Ingredient_B-TEST, DreadPoor/Ingredient_C-TEST, and DreadPoor/Ingredient_D-TEST. This merging process was executed using the mergekit tool, specifically employing the model_stock merge method.

Key Configuration Details

  • Base Model: The merging process used DreadPoor/Famino-12B-Model_Stock as its foundational architecture.
  • Tokenizer Source: The tokenizer for Krix-12B-Model_Stock is derived from DreadPoor/Famino-12B-Model_Stock, ensuring consistent tokenization with its base.
  • Merge Method: The model_stock method was applied, indicating a specific approach to combining the weights and biases of the constituent models.
  • Data Types: The model is configured to use bfloat16 for its numerical precision, which balances performance and memory efficiency.
  • Int8 Masking: It includes int8_mask: true, suggesting optimizations for integer-based operations.

Potential Use Cases

Given its merged nature, Krix-12B-Model_Stock is likely suitable for a broad range of general-purpose language generation and understanding tasks, benefiting from the diverse capabilities of its merged components. Developers looking for a model built from multiple specialized sources might find this architecture particularly interesting for achieving balanced performance across various domains.

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