You can deploy this model and start earning money today!

ZEUS 8B 🌩️ V2

Name: ZEUS-8B-V2
Availability: InStock

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using unsloth/Meta-Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

base_model: unsloth/Meta-Llama-3.1-8B-Instruct
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
slices:
- sources:
  - layer_range: [0, 32]
    model: akjindal53244/Llama-3.1-Storm-8B
    parameters:
      density: 0.8
      weight: 0.25
  - layer_range: [0, 32]
    model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      density: 0.8
      weight: 0.33
  - layer_range: [0, 32]
    model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
    parameters:
      density: 0.8
      weight: 0.42
  - layer_range: [0, 32]
    model: unsloth/Meta-Llama-3.1-8B-Instruct
tokenizer_source: base

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Based on the listed rankings as of 4/12/24, is the top-rank 8B model.

Metric	Value
Avg.	30.07
IFEval (0-Shot)	80.29
BBH (3-Shot)	31.61
MATH Lvl 5 (4-Shot)	21.15
GPQA (0-shot)	6.94
MuSR (0-shot)	8.24
MMLU-PRO (5-shot)	32.18

Inference Settings

Personal recommendations are to use a i1-Q4_K_M quant with these settings:

num_ctx = 4096
repeat_penalty = 1.2
temperature = 0.85
tfs_z = 1.4
top_k = 0 # Change to 40+ if you're roleplaying
top_p = 1 # Change to 0.9 if top_k > 0

Other recommendations can be found on this paper on mobile LLMs, this paper on balancing model parameters, and this Reddit post about tweaking Llama 3.1 parameters.

T145/ZEUS-8B-V2