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 |