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---
license: apache-2.0
pipeline_tag: text-generation
---
# Lumosia-v2-MoE-4x10.7
The Lumosia Series upgraded with Lumosia V2.
# What's New in Lumosia V2?
Lumosia V2 takes the original vision of being an "all-rounder" and refines it with more nuanced capabilities.
Topic/Prompt Based Approach:
Diverging from the keyword-based approach of its counterpart, Umbra.
Context and Coherence:
With a base context of 8k scrolling window and the ability to maintain coherence up to 16k.
Balanced and Versatile:
The core ethos of Lumosia V2 is balance. It's designed to be your go-to assistant.
Experimentation and User-Centric Development:
Lumosia V2 remains an experimental model, a mosaic of the best-performing Solar models, (selected based on user experience).
This version is a testament to the idea that innovation is a journey, not a destination.
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:
* Avg:
* ARC:
* HellaSwag:
* MMLU:
* T-QA:
* Winogrande:
* GSM8K:
## Examples:
```
Example 1:
User:
Lumosia:
```
```
Example 2:
User:
Lumosia:
```
## 🧩 Configuration
```
yaml
base_model: DopeorNope/SOLARC-M-10.7B
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: DopeorNope/SOLARC-M-10.7B
positive_prompts: [""]
- source_model: maywell/PiVoT-10.7B-Mistral-v0.2-RP
positive_prompts: [""]
- source_model: kyujinpy/Sakura-SOLAR-Instruct
positive_prompts: [""]
- source_model: jeonsworld/CarbonVillain-en-10.7B-v1
positive_prompts: [""]
```
## 💻 Usage
```
python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Steelskull/Lumosia-MoE-4x10.7"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "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"])
``` |