Update README.md

README.md

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 ---
 license: apache-2.0
+pipeline_tag: text-generation
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+
+# Celestria-MoE-8x10.7b
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/tiORws6ezzAHGJJODC8PA.png)
+
+The Celestria Series, is the older sibling 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)
+
+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"])
+```