---
license: apache-2.0
pipeline_tag: text-generation
tags:
- Solar Moe
- Solar
- Celestria
---

# Celestria-MoE-8x10.7b

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/tiORws6ezzAHGJJODC8PA.png)

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!

Come join the Discord:
[ConvexAI](https://discord.gg/yYqmNmg7Wj)


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"])
```