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:
🏆 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 |