metadata
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Gille/StrangeMerges_9-7B-dare_ties
- Gille/StrangeMerges_8-7B-slerp
base_model:
- Gille/StrangeMerges_9-7B-dare_ties
- Gille/StrangeMerges_8-7B-slerp
model-index:
- name: MoE-StrangeMerges-2x7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 70.82
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.83
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.04
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 65.86
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.79
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.7
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B
name: Open LLM Leaderboard
MoE-StrangeMerges-2x7B
MoE-StrangeMerges-2x7B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
base_model: Gille/StrangeMerges_9-7B-dare_ties
gate_mode: cheap_embed
dtype: float16
experts:
- source_model: Gille/StrangeMerges_9-7B-dare_ties
positive_prompts: ["science, logic, math"]
- source_model: Gille/StrangeMerges_8-7B-slerp
positive_prompts: ["reasoning, numbers, abstract"]
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/MoE-StrangeMerges-2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 73.34 |
| AI2 Reasoning Challenge (25-Shot) | 70.82 |
| HellaSwag (10-Shot) | 87.83 |
| MMLU (5-Shot) | 65.04 |
| TruthfulQA (0-shot) | 65.86 |
| Winogrande (5-shot) | 82.79 |
| GSM8k (5-shot) | 67.70 |