ExpertRamonda-7Bx2_MoE

Name: ExpertRamonda-7Bx2_MoE
Availability: InStock

ExpertRamonda-7Bx2_MoE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🏆 Benchmarks

Open LLM Leaderboard

Model	Average	ARC_easy	HellaSwag	MMLU	TruthfulQA_mc2	Winogrande	GSM8K
mayacinka/ExpertRamonda-7Bx2_MoE	78.10	86.87	87.51	61.63	78.02	81.85	72.71

MMLU

Groups	Version	Filter	n-shot	Metric	Value		Stderr
mmlu	N/A	none	0	acc	0.6163	±	0.0039
- humanities	N/A	none	None	acc	0.5719	±	0.0067
- other	N/A	none	None	acc	0.6936	±	0.0079
- social_sciences	N/A	none	None	acc	0.7121	±	0.0080
- stem	N/A	none	None	acc	0.5128	±	0.0085

🧩 Configuration

yaml

base_model: mlabonne/AlphaMonarch-7B
gate_mode: hidden 
dtype: bfloat16 
experts_per_token: 2
experts:
  - source_model: mlabonne/AlphaMonarch-7B
    positive_prompts:
      - "You excel at reasoning skills. For every prompt you think of an answer from 3 different angles"
    ## (optional)
    # negative_prompts:
    #   - "This is a prompt expert_model_1 should not be used for"
  - source_model: bardsai/jaskier-7b-dpo-v5.6
    positive_prompts:
      - "You excel at logic and reasoning skills. Reply in a straightforward and concise way"

💻 Usage

python

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/ExpertRamonda-7Bx2_MoE"

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"])