Instructions to use QuantFactory/NeuralDaredevil-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/NeuralDaredevil-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/NeuralDaredevil-7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/NeuralDaredevil-7B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/NeuralDaredevil-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/NeuralDaredevil-7B-GGUF",
	filename="NeuralDaredevil-7B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/NeuralDaredevil-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/NeuralDaredevil-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/NeuralDaredevil-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/NeuralDaredevil-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/NeuralDaredevil-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/NeuralDaredevil-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/NeuralDaredevil-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/NeuralDaredevil-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/NeuralDaredevil-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/NeuralDaredevil-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/NeuralDaredevil-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/NeuralDaredevil-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/NeuralDaredevil-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/NeuralDaredevil-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/NeuralDaredevil-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/NeuralDaredevil-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/NeuralDaredevil-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NeuralDaredevil-7B-GGUF-Q4_K_M

List all available models

lemonade list

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"])

Downloads last month: 337

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/NeuralDaredevil-7B-GGUF

Base model

mlabonne/Daredevil-7B

Finetuned

mlabonne/NeuralDaredevil-7B

Quantized

(5)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.880
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.620
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.120
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.850
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

82.080
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

73.160