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.

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:
    
    negative_prompts:

  - source_model: Sao10K/Fimbulvetr-10.7B-v1 [Updated]
    positive_prompts:
    
    negative_prompts:

  - source_model: jeonsworld/CarbonVillain-en-10.7B-v4 [Updated]
    positive_prompts:
    
    negative_prompts:

  - source_model: kyujinpy/Sakura-SOLAR-Instruct
    positive_prompts:
    
    negative_prompts:

💻 Usage

python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Steelskull/Lumosia-v2-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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.75
AI2 Reasoning Challenge (25-Shot)	70.39
HellaSwag (10-Shot)	87.87
MMLU (5-Shot)	66.45
TruthfulQA (0-shot)	68.48
Winogrande (5-shot)	84.21
GSM8k (5-shot)	65.13