EastAsia-4x7B-Moe-experiment
EastAsia-4x7B-Moe-experiment is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
gate_mode: hidden
dtype: bfloat16
base_model: mlabonne/Marcoro14-7B-slerp
experts:
- source_model: MediaTek-Research/Breeze-7B-Instruct-v0.1
positive_prompts:
- "翻譯"
- source_model: augmxnt/shisa-7b-v1
positive_prompts:
- "翻訳"
- source_model: beomi/OPEN-SOLAR-KO-10.7B
positive_prompts:
- "번역"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Heng666/EastAsia-4x7B-Moe-experiment"
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. | 42.12 |
| AI2 Reasoning Challenge (25-Shot) | 39.51 |
| HellaSwag (10-Shot) | 48.92 |
| MMLU (5-Shot) | 56.20 |
| TruthfulQA (0-shot) | 49.83 |
| Winogrande (5-shot) | 58.09 |
| GSM8k (5-shot) | 0.15 |