metadata

license: cc-by-nc-4.0
tags:
  - merge
  - lazymergekit
dataset:
  - mlabonne/truthy-dpo-v0.1
  - mlabonne/distilabel-intel-orca-dpo-pairs
base_model:
  - mlabonne/Monarch-7B
language:
  - en

👑 NeuralMonarch-7B

NeuralMonarch-7B is a DPO fine-tuned of mlabonne/Monarch-7B using the jondurbin/truthy-dpo-v0.1 and argilla/distilabel-intel-orca-dpo-pairs preference datasets.

It is based on a merge of the following models using LazyMergekit:

Special thanks to Jon Durbin, Intel, and Argilla for the preference datasets.

🔍 Applications

This model uses a context window of 8k. It is compatible with different templates, like chatml, Llama's and Mistral's chat templates.

Compared to other 7B models, it displays good performance in instruction following and reasoning tasks. It can also be used for RP and storytelling.

⚡ Quantized models

GGUF: TBD

🏆 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

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralMonarch-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"])