It has the same average score as Beagle14-7B ("Show merges"), which could be due to might be due to an unlucky run. I think I might be overexploiting argilla/distilabel-intel-orca-dpo-pairs at this point, since this dataset or its original version are present in multiple models. I need to find more high-quality preference data for the next DPO merge.

Note that some models like udkai/Turdus and nfaheem/Marcoroni-7b-DPO-Merge are unfortunately contaminated on purpose (see the very high Winogrande score).

Nous

The evaluation was performed using LLM AutoEval on Nous suite. It is the best 7B model to date.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/NeuralBeagle14-7B 📄	60.25	46.06	76.77	70.32	47.86
mlabonne/Beagle14-7B 📄	59.4	44.38	76.53	69.44	47.25
mlabonne/NeuralDaredevil-7B 📄	59.39	45.23	76.2	67.61	48.52
argilla/distilabeled-Marcoro14-7B-slerp 📄	58.93	45.38	76.48	65.68	48.18
mlabonne/NeuralMarcoro14-7B 📄	58.4	44.59	76.17	65.94	46.9
openchat/openchat-3.5-0106 📄	53.71	44.17	73.72	52.53	44.4
teknium/OpenHermes-2.5-Mistral-7B 📄	52.42	42.75	72.99	52.99	40.94

You can find the complete benchmark on YALL - Yet Another LLM Leaderboard.

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralBeagle14-7B"
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"])

LoneStriker/NeuralBeagle14-7B-3.0bpw-h6-exl2

🐶 NeuralBeagle14-7B

⚡ Quantized models

🏆 Evaluation

Open LLM Leaderboard

Nous

💻 Usage

LoneStriker/
NeuralBeagle14-7B-3.0bpw-h6-exl2