NeuralDaredevil-7B-GGUF

• This is quantized version of mlabonne/NeuralDaredevil-7B created using llama.cpp

Model Description

NeuralDaredevil-7B is a DPO fine-tune of mlabonne/Daredevil-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset and my DPO notebook from this article.

Thanks Argilla for providing the dataset and the training recipe here. 💪

🏆 Evaluation

Nous

The evaluation was performed using LLM AutoEval on Nous suite.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/NeuralDaredevil-7B 📄	59.39	45.23	76.2	67.61	48.52
mlabonne/Beagle14-7B 📄	59.4	44.38	76.53	69.44	47.25
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.

Open LLM Leaderboard

Detailed results can be found here

Metric	Value
Avg.	74.12
AI2 Reasoning Challenge (25-Shot)	69.88
HellaSwag (10-Shot)	87.62
MMLU (5-Shot)	65.12
TruthfulQA (0-shot)	66.85
Winogrande (5-shot)	82.08
GSM8k (5-shot)	73.16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralDaredevil-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"])