Instructions to use DeepDream2045/neuraldaredevil-exl2-6bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepDream2045/neuraldaredevil-exl2-6bpw with Transformers:

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

pipe = pipeline("text-generation", model="DeepDream2045/neuraldaredevil-exl2-6bpw")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepDream2045/neuraldaredevil-exl2-6bpw")
model = AutoModelForCausalLM.from_pretrained("DeepDream2045/neuraldaredevil-exl2-6bpw")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepDream2045/neuraldaredevil-exl2-6bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepDream2045/neuraldaredevil-exl2-6bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepDream2045/neuraldaredevil-exl2-6bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepDream2045/neuraldaredevil-exl2-6bpw

SGLang

How to use DeepDream2045/neuraldaredevil-exl2-6bpw 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 "DeepDream2045/neuraldaredevil-exl2-6bpw" \
    --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": "DeepDream2045/neuraldaredevil-exl2-6bpw",
		"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 "DeepDream2045/neuraldaredevil-exl2-6bpw" \
        --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": "DeepDream2045/neuraldaredevil-exl2-6bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepDream2045/neuraldaredevil-exl2-6bpw with Docker Model Runner:
```
docker model run hf.co/DeepDream2045/neuraldaredevil-exl2-6bpw
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

exl2 quant (measurement.json included)

original readme below

license: cc-by-nc-4.0 base_model: mlabonne/Daredevil-7B tags: - merge - mergekit - lazymergekit - dpo - rlhf - mlabonne/example

NeuralDaredevil-7B

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

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.

💻 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"])

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