Ultiima-78B-v2

4
—
by
Sakalti
Language Model
OTHER
78B params
New
4 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
175GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
73GB+ RAM

Code Examples

Configurationyaml
merge_method: della_linear
base_model: Sakalti/ultiima-78B
dtype: float16
parameters:
  epsilon: 0.015            # Fine-grain scaling for precision.
  lambda: 1.6               # Strong emphasis on top-performing models.
  normalize: true           # Stable parameter integration across models.
adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.75           # Logical reasoning.
    tinyHellaswag: 1.65     # Contextual predictions.
    tinyMMLU: 1.8           # Domain knowledge.
    tinyTruthfulQA: 2.0     # Prioritize truthful reasoning.
    tinyTruthfulQA_mc1: 1.85
    tinyWinogrande: 1.9     # Advanced reasoning and predictions.
    IFEval: 2.1             # Instruction-following and multitasking.
    BBH: 1.9                # Complex reasoning.
    MATH: 2.3               # Mathematical reasoning.
    GPQA: 2.2             # Factual QA.
    MUSR: 2.0             # Multi-step reasoning.
    MMLU-PRO: 2.2       # Domain multitask performance.
  smoothing_factor: 0.1     # Smooth blending across benchmarks.
models:
  - model: MaziyarPanahi/calme-2.4-rys-78B
    parameters:
      weight: 1
      density: 1
  - model: Sakalti/ultiima-78B
    parameters:
      weight: 1
      density: 1
Configurationyaml
merge_method: della_linear
base_model: Sakalti/ultiima-78B
dtype: float16
parameters:
  epsilon: 0.015            # Fine-grain scaling for precision.
  lambda: 1.6               # Strong emphasis on top-performing models.
  normalize: true           # Stable parameter integration across models.
adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.75           # Logical reasoning.
    tinyHellaswag: 1.65     # Contextual predictions.
    tinyMMLU: 1.8           # Domain knowledge.
    tinyTruthfulQA: 2.0     # Prioritize truthful reasoning.
    tinyTruthfulQA_mc1: 1.85
    tinyWinogrande: 1.9     # Advanced reasoning and predictions.
    IFEval: 2.1             # Instruction-following and multitasking.
    BBH: 1.9                # Complex reasoning.
    MATH: 2.3               # Mathematical reasoning.
    GPQA: 2.2             # Factual QA.
    MUSR: 2.0             # Multi-step reasoning.
    MMLU-PRO: 2.2       # Domain multitask performance.
  smoothing_factor: 0.1     # Smooth blending across benchmarks.
models:
  - model: MaziyarPanahi/calme-2.4-rys-78B
    parameters:
      weight: 1
      density: 1
  - model: Sakalti/ultiima-78B
    parameters:
      weight: 1
      density: 1
Configurationyaml
merge_method: della_linear
base_model: Sakalti/ultiima-78B
dtype: float16
parameters:
  epsilon: 0.015            # Fine-grain scaling for precision.
  lambda: 1.6               # Strong emphasis on top-performing models.
  normalize: true           # Stable parameter integration across models.
adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.75           # Logical reasoning.
    tinyHellaswag: 1.65     # Contextual predictions.
    tinyMMLU: 1.8           # Domain knowledge.
    tinyTruthfulQA: 2.0     # Prioritize truthful reasoning.
    tinyTruthfulQA_mc1: 1.85
    tinyWinogrande: 1.9     # Advanced reasoning and predictions.
    IFEval: 2.1             # Instruction-following and multitasking.
    BBH: 1.9                # Complex reasoning.
    MATH: 2.3               # Mathematical reasoning.
    GPQA: 2.2             # Factual QA.
    MUSR: 2.0             # Multi-step reasoning.
    MMLU-PRO: 2.2       # Domain multitask performance.
  smoothing_factor: 0.1     # Smooth blending across benchmarks.
models:
  - model: MaziyarPanahi/calme-2.4-rys-78B
    parameters:
      weight: 1
      density: 1
  - model: Sakalti/ultiima-78B
    parameters:
      weight: 1
      density: 1
Configurationyaml
merge_method: della_linear
base_model: Sakalti/ultiima-78B
dtype: float16
parameters:
  epsilon: 0.015            # Fine-grain scaling for precision.
  lambda: 1.6               # Strong emphasis on top-performing models.
  normalize: true           # Stable parameter integration across models.
adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.75           # Logical reasoning.
    tinyHellaswag: 1.65     # Contextual predictions.
    tinyMMLU: 1.8           # Domain knowledge.
    tinyTruthfulQA: 2.0     # Prioritize truthful reasoning.
    tinyTruthfulQA_mc1: 1.85
    tinyWinogrande: 1.9     # Advanced reasoning and predictions.
    IFEval: 2.1             # Instruction-following and multitasking.
    BBH: 1.9                # Complex reasoning.
    MATH: 2.3               # Mathematical reasoning.
    GPQA: 2.2             # Factual QA.
    MUSR: 2.0             # Multi-step reasoning.
    MMLU-PRO: 2.2       # Domain multitask performance.
  smoothing_factor: 0.1     # Smooth blending across benchmarks.
models:
  - model: MaziyarPanahi/calme-2.4-rys-78B
    parameters:
      weight: 1
      density: 1
  - model: Sakalti/ultiima-78B
    parameters:
      weight: 1
      density: 1
Configurationyaml
merge_method: della_linear
base_model: Sakalti/ultiima-78B
dtype: float16
parameters:
  epsilon: 0.015            # Fine-grain scaling for precision.
  lambda: 1.6               # Strong emphasis on top-performing models.
  normalize: true           # Stable parameter integration across models.
adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.75           # Logical reasoning.
    tinyHellaswag: 1.65     # Contextual predictions.
    tinyMMLU: 1.8           # Domain knowledge.
    tinyTruthfulQA: 2.0     # Prioritize truthful reasoning.
    tinyTruthfulQA_mc1: 1.85
    tinyWinogrande: 1.9     # Advanced reasoning and predictions.
    IFEval: 2.1             # Instruction-following and multitasking.
    BBH: 1.9                # Complex reasoning.
    MATH: 2.3               # Mathematical reasoning.
    GPQA: 2.2             # Factual QA.
    MUSR: 2.0             # Multi-step reasoning.
    MMLU-PRO: 2.2       # Domain multitask performance.
  smoothing_factor: 0.1     # Smooth blending across benchmarks.
models:
  - model: MaziyarPanahi/calme-2.4-rys-78B
    parameters:
      weight: 1
      density: 1
  - model: Sakalti/ultiima-78B
    parameters:
      weight: 1
      density: 1

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