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---
license: llama3.1
library_name: transformers
tags:
- moe
- frankenmoe
- merge
- mergekit
base_model:
- Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
model-index:
- name: L3.1-Moe-2x8B-v0.2
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: 73.48
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
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: 32.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
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: 15.26
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
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: 6.71
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
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: 11.38
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
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: 31.76
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-2x8B-v0.2
name: Open LLM Leaderboard
---
# L3.1-Moe-2x8B-v0.2
This model is a Mixture of Experts (MoE) made with mergekit-moe. It uses the following base models:
- [Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base](https://huggingface.co/Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base)
- [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2)
Heavily inspired by [mlabonne/Beyonder-4x7B-v3](https://huggingface.co/mlabonne/Beyonder-4x7B-v3).
## Quantized models
### GGUF by [mradermacher](https://huggingface.co/mradermacher)
- [mradermacher/L3.1-Moe-2x8B-v0.2-i1-GGUF](https://huggingface.co/mradermacher/L3.1-Moe-2x8B-v0.2-i1-GGUF)
- [mradermacher/L3.1-Moe-2x8B-v0.2-GGUF](https://huggingface.co/mradermacher/L3.1-Moe-2x8B-v0.2-GGUF)
## Mergekit config
<details>
<summary>mergekit_moe_config.yml</summary>
```yaml
base_model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
positive_prompts: &common_prompts
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
negative_prompts: &rp_prompts
- "storywriting"
- "write"
- "scene"
- "story"
- "character"
- source_model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
positive_prompts: *rp_prompts
negative_prompts: *common_prompts
```
</details>
# [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_moeru-ai__L3.1-Moe-2x8B-v0.2)
| Metric |Value|
|-------------------|----:|
|Avg. |28.59|
|IFEval (0-Shot) |73.48|
|BBH (3-Shot) |32.95|
|MATH Lvl 5 (4-Shot)|15.26|
|GPQA (0-shot) | 6.71|
|MuSR (0-shot) |11.38|
|MMLU-PRO (5-shot) |31.76|
|