Instructions to use Kukedlc/Neural-Cosmic-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/Neural-Cosmic-7B-slerp with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/Neural-Cosmic-7B-slerp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Neural-Cosmic-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/Neural-Cosmic-7B-slerp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Kukedlc/Neural-Cosmic-7B-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Kukedlc/Neural-Cosmic-7B-slerp

SGLang

How to use Kukedlc/Neural-Cosmic-7B-slerp 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 "Kukedlc/Neural-Cosmic-7B-slerp" \
    --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": "Kukedlc/Neural-Cosmic-7B-slerp",
		"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 "Kukedlc/Neural-Cosmic-7B-slerp" \
        --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": "Kukedlc/Neural-Cosmic-7B-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kukedlc/Neural-Cosmic-7B-slerp with Docker Model Runner:
```
docker model run hf.co/Kukedlc/Neural-Cosmic-7B-slerp
```

Neural-Cosmic-7B-slerp / README.md

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	---
	tags:
	- merge
	- mergekit
	- lazymergekit
	- Kukedlc/Triunvirato-7b
	- Kukedlc/Neural4gsm8k
	base_model:
	- Kukedlc/Triunvirato-7b
	- Kukedlc/Neural4gsm8k
	---

	# Neural-Cosmic-7B-slerp

	Neural-Cosmic-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [Kukedlc/Triunvirato-7b](https://huggingface.co/Kukedlc/Triunvirato-7b)
	* [Kukedlc/Neural4gsm8k](https://huggingface.co/Kukedlc/Neural4gsm8k)

	## 🧩 Configuration

	```yaml
	slices:
	- sources:
	- model: Kukedlc/Triunvirato-7b
	layer_range: [0, 32]
	- model: Kukedlc/Neural4gsm8k
	layer_range: [0, 32]
	merge_method: slerp
	base_model: Kukedlc/Triunvirato-7b
	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 = "Kukedlc/Neural-Cosmic-7B-slerp"
	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"])
	```