---
language:
- en
license: llama3
model-index:
- name: L3-8B-Lunaris-v1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 68.95
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 32.11
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 8.46
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.82
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.55
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 30.97
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Sao10K/L3-8B-Lunaris-v1
      name: Open LLM Leaderboard
---

A generalist / roleplaying model merge based on Llama 3. Models are selected from my personal experience while using them.

I personally think this is an improvement over Stheno v3.2, considering the other models helped balance out its creativity and at the same time improving its logic.

Settings:
```
Instruct // Context Template: Llama-3-Instruct
Temperature: 1.4
min_p: 0.1
```

---

Merging seems to be a black box magic though? In my personal experience merging multiple models from different datasets / data works better than combining them all in one.

*Values chosen are from long-running personal experimentation since Llama-2 Merging Era. I have tweaked them to fit this recipe.*

Mergekit Config 
```
models:
  - model: meta-llama/Meta-Llama-3-8B-Instruct
  - model: crestf411/L3-8B-sunfall-v0.1 # Another RP Model trained on... stuff
    parameters:
      density: 0.4
      weight: 0.25
  - model: Hastagaras/Jamet-8B-L3-MK1 - # Another RP / Storytelling Model
    parameters:
      density: 0.5
      weight: 0.3
  - model: maldv/badger-iota-llama-3-8b #Megamerge - Helps with General Knowledge
    parameters:
      density: 0.6
      weight: 0.35
  - model: Sao10K/Stheno-3.2-Beta # This is Stheno v3.2's Initial Name
    parameters:
      density: 0.7
      weight: 0.4
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__L3-8B-Lunaris-v1)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |25.48|
|IFEval (0-Shot)    |68.95|
|BBH (3-Shot)       |32.11|
|MATH Lvl 5 (4-Shot)| 8.46|
|GPQA (0-shot)      | 6.82|
|MuSR (0-shot)      | 5.55|
|MMLU-PRO (5-shot)  |30.97|