Instructions to use mlabonne/NeuralDaredevil-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlabonne/NeuralDaredevil-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlabonne/NeuralDaredevil-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlabonne/NeuralDaredevil-7B") model = AutoModelForCausalLM.from_pretrained("mlabonne/NeuralDaredevil-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mlabonne/NeuralDaredevil-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlabonne/NeuralDaredevil-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/NeuralDaredevil-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mlabonne/NeuralDaredevil-7B
- SGLang
How to use mlabonne/NeuralDaredevil-7B 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 "mlabonne/NeuralDaredevil-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/NeuralDaredevil-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "mlabonne/NeuralDaredevil-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/NeuralDaredevil-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mlabonne/NeuralDaredevil-7B with Docker Model Runner:
docker model run hf.co/mlabonne/NeuralDaredevil-7B
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- dpo
- rlhf
- mlabonne/example
base_model: mlabonne/Daredevil-7B
model-index:
- name: NeuralDaredevil-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.88
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.62
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.12
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 66.85
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.08
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.16
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
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. πͺ
π Evaluation
Nous
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.
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 |
π» 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"])
Prompt Tempalte
This model uses the same prompt template as mistralai/Mistral-7B-Instruct-v0.2
See instruction-format for more details
