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QuantFactory/NeuralDaredevil-7B-GGUF
NeuralDaredevil-7B-GGUF is a text generation model from QuantFactory. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
- This is quantized version of mlabonne/NeuralDaredevil-7B created using llama.cpp
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From the Hugging Face model README

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. πͺ
The evaluation was performed using LLM AutoEval on Nous suite.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| mlabonne/NeuralDaredevil-7B π | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
| mlabonne/Beagle14-7B π | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
| argilla/distilabeled-Marcoro14-7B-slerp π | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
| mlabonne/NeuralMarcoro14-7B π | 58.4 | 44.59 | 76.17 | 65.94 | 46.9 |
| openchat/openchat-3.5-0106 π | 53.71 | 44.17 | 73.72 | 52.53 | 44.4 |
| teknium/OpenHermes-2.5-Mistral-7B π | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
You can find the complete benchmark on YALL - Yet Another 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 |
!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"])
<p align="center">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>