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"])