Instructions to use SteelStorage/Celestria-MoE-8x10.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SteelStorage/Celestria-MoE-8x10.7b with Transformers:

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

pipe = pipeline("text-generation", model="SteelStorage/Celestria-MoE-8x10.7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SteelStorage/Celestria-MoE-8x10.7b")
model = AutoModelForCausalLM.from_pretrained("SteelStorage/Celestria-MoE-8x10.7b")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SteelStorage/Celestria-MoE-8x10.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SteelStorage/Celestria-MoE-8x10.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SteelStorage/Celestria-MoE-8x10.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SteelStorage/Celestria-MoE-8x10.7b

SGLang

How to use SteelStorage/Celestria-MoE-8x10.7b 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 "SteelStorage/Celestria-MoE-8x10.7b" \
    --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": "SteelStorage/Celestria-MoE-8x10.7b",
		"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 "SteelStorage/Celestria-MoE-8x10.7b" \
        --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": "SteelStorage/Celestria-MoE-8x10.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SteelStorage/Celestria-MoE-8x10.7b with Docker Model Runner:
```
docker model run hf.co/SteelStorage/Celestria-MoE-8x10.7b
```

Celestria-MoE-8x10.7b

The Celestria Series, is the "Big Sister" of the Lumosia and Umbra Series. It is an experiment born from the collective wisdom of the AI community, a mosaic of the eight best-performing Solar models (By my prefrences)

its 3am.... again, I have a tendency to do this apparently so im not going to get to creative on this card.

With this model I have created positive and negative prompt sentances:

[Celestria Series] Based on prompt sentances.

[Umbra Series] based on prompt keywords.

[Lumosia Series] based on prompt topics.

Let me know what you think!

Template:

### System:

### USER:{prompt}

### Assistant:

Settings:

Temp: 1.0
min-p: 0.02-0.1

Evals:

To come

Avg:
ARC:
HellaSwag:
MMLU:
T-QA:
Winogrande:
GSM8K:

Examples:

Example 1:

User:

Celestria:

Example 2:

User:

Celestria:

🧩 Configuration

yaml
experts:
  - source_model: Fimbulvetr-10.7B-v1

  - source_model: PiVoT-10.7B-Mistral-v0.2-RP

  - source_model: UNA-POLAR-10.7B-InstructMath-v2

  - source_model: LMCocktail-10.7B-v1

  - source_model: CarbonBeagle-11B

  - source_model: SOLARC-M-10.7B

  - source_model: Nous-Hermes-2-SOLAR-10.7B-MISALIGNED

  - source_model: CarbonVillain-en-10.7B-v4

💻 Usage

python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Steelskull/Celestria-MoE-8x10.7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])

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