Monarch-7B

Update 13/02/24: Monarch-7B is the best-performing model on the YALL leaderboard.

Monarch-7B is a merge of the following models using LazyMergekit:

• mlabonne/OmniTruthyBeagle-7B-v0
• mlabonne/NeuBeagle-7B
• mlabonne/NeuralOmniBeagle-7B

🏆 Evaluation

The evaluation was performed using LLM AutoEval on Nous suite. See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
Monarch-7B 📄	62.68	45.48	77.07	78.04	50.14
teknium/OpenHermes-2.5-Mistral-7B 📄	52.42	42.75	72.99	52.99	40.94
mlabonne/NeuralHermes-2.5-Mistral-7B 📄	53.51	43.67	73.24	55.37	41.76
mlabonne/NeuralBeagle14-7B 📄	60.25	46.06	76.77	70.32	47.86
eren23/dpo-binarized-NeuralTrix-7B 📄	62.5	44.57	76.34	79.81	49.27
CultriX/NeuralTrix-7B-dpo 📄	62.5	44.61	76.33	79.8	49.24

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: mlabonne/OmniTruthyBeagle-7B-v0
    parameters:
      density: 0.65
      weight: 0.36
  - model: mlabonne/NeuBeagle-7B
    parameters:
      density: 0.6
      weight: 0.34
  - model: mlabonne/NeuralOmniBeagle-7B 
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Monarch-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

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.	76.25
AI2 Reasoning Challenge (25-Shot)	73.04
HellaSwag (10-Shot)	89.03
MMLU (5-Shot)	64.41
TruthfulQA (0-shot)	77.35
Winogrande (5-shot)	84.61
GSM8k (5-shot)	69.07