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
```

NeuralPipe-7B-slerp-v0.2 / README.md

MaziyarPanahi

Update README.md

aa6ac75 verified over 2 years ago

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	---
	license: apache-2.0
	tags:
	- merge
	- mergekit
	- lazymergekit
	- OpenPipe/mistral-ft-optimized-1227
	- mlabonne/NeuralHermes-2.5-Mistral-7B
	---

	# NeuralPipe-7B-slerp-v0.2

	NeuralPipe-7B-slerp-v0.2 is a merge of the following models:
	* [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
	* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)

	## 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

	```yaml
	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

	```python
	!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"])
	```