Instructions to use mlabonne/NeuralPipe-7B-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/NeuralPipe-7B-ties with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/NeuralPipe-7B-ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/NeuralPipe-7B-ties")
model = AutoModelForCausalLM.from_pretrained("mlabonne/NeuralPipe-7B-ties")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlabonne/NeuralPipe-7B-ties with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mlabonne/NeuralPipe-7B-ties

SGLang

How to use mlabonne/NeuralPipe-7B-ties 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/NeuralPipe-7B-ties" \
    --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": "mlabonne/NeuralPipe-7B-ties",
		"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 "mlabonne/NeuralPipe-7B-ties" \
        --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": "mlabonne/NeuralPipe-7B-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mlabonne/NeuralPipe-7B-ties with Docker Model Runner:
```
docker model run hf.co/mlabonne/NeuralPipe-7B-ties
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NeuralPipe-7B-ties

This model is a merge of the following models made with mergekit:

⚡ Quantized models

Thanks to TheBloke for the quantized models:

GGUF: https://huggingface.co/TheBloke/NeuralPipe-7B-ties-GGUF
AWQ: https://huggingface.co/TheBloke/NeuralPipe-7B-ties-AWQ
GPTQ: https://huggingface.co/TheBloke/NeuralPipe-7B-ties-GPTQ

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: OpenPipe/mistral-ft-optimized-1218
    parameters:
      density: 0.5
      weight: 0.5
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.55
AI2 Reasoning Challenge (25-Shot)	67.92
HellaSwag (10-Shot)	86.04
MMLU (5-Shot)	64.24
TruthfulQA (0-shot)	61.37
Winogrande (5-shot)	80.19
GSM8k (5-shot)	69.52