Instructions to use MaziyarPanahi/NeuralPipe-7B-slerp-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/NeuralPipe-7B-slerp-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/NeuralPipe-7B-slerp-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/NeuralPipe-7B-slerp-v0.2")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/NeuralPipe-7B-slerp-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaziyarPanahi/NeuralPipe-7B-slerp-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/NeuralPipe-7B-slerp-v0.2

SGLang

How to use MaziyarPanahi/NeuralPipe-7B-slerp-v0.2 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 "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2" \
    --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": "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2",
		"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 "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2" \
        --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": "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MaziyarPanahi/NeuralPipe-7B-slerp-v0.2 with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/NeuralPipe-7B-slerp-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NeuralPipe-7B-slerp-v0.2

NeuralPipe-7B-slerp-v0.2 is a merge of the following models:

Eval

|      Groups      |Version|Filter|n-shot|  Metric   | Value |   |Stderr|
|------------------|-------|------|-----:|-----------|------:|---|-----:|
|ai2_arc           |N/A    |none  |     0|acc        | 0.7554|±  |0.0406|
|                  |       |none  |     0|acc_norm   | 0.7573|±  |0.0332|
|mmlu              |N/A    |none  |     0|acc        | 0.6188|±  |0.1472|
| - humanities     |N/A    |none  |     0|acc        | 0.5645|±  |0.1686|
| - other          |N/A    |none  |     0|acc        | 0.6987|±  |0.1098|
| - social_sciences|N/A    |none  |     0|acc        | 0.7215|±  |0.0887|
| - stem           |N/A    |none  |     0|acc        | 0.5208|±  |0.1392|
|truthfulqa        |N/A    |none  |     0|acc        | 0.4746|±  |0.0024|
|                  |       |none  |     0|bleu_max   |26.7118|±  |0.8092|
|                  |       |none  |     0|bleu_acc   | 0.4957|±  |0.0175|
|                  |       |none  |     0|bleu_diff  | 3.1016|±  |0.8065|
|                  |       |none  |     0|rouge1_max |53.1171|±  |0.8499|
|                  |       |none  |     0|rouge1_acc | 0.5055|±  |0.0175|
|                  |       |none  |     0|rouge1_diff| 4.0629|±  |1.0345|
|                  |       |none  |     0|rouge2_max |39.1331|±  |1.0068|
|                  |       |none  |     0|rouge2_acc | 0.4492|±  |0.0174|
|                  |       |none  |     0|rouge2_diff| 3.7457|±  |1.1652|
|                  |       |none  |     0|rougeL_max |49.8547|±  |0.8818|
|                  |       |none  |     0|rougeL_acc | 0.5006|±  |0.0175|
|                  |       |none  |     0|rougeL_diff| 3.6422|±  |1.0540|

🧩 Configuration

slices:
  - sources:
      - model: OpenPipe/mistral-ft-optimized-1227
        layer_range: [0, 32]
      - model: mlabonne/NeuralHermes-2.5-Mistral-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "MaziyarPanahi/NeuralPipe-7B-slerp-v0.2"
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"])