README.md

---
license: apache-2.0
tags:
- Solar Moe
- Solar
- Lumosia
pipeline_tag: text-generation
model-index:
- name: Lumosia-v2-MoE-4x10.7
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 70.39
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 87.87
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 66.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 68.48
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 84.21
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 65.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
      name: Open LLM Leaderboard
---
# Lumosia-v2-MoE-4x10.7

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/fKdOLTQNerr2fYYnWOiQD.png)

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:
    
    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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Steelskull__Lumosia-v2-MoE-4x10.7)

|             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|



***

Quantization of Model [Steelskull/Lumosia-v2-MoE-4x10.7](https://huggingface.co/Steelskull/Lumosia-v2-MoE-4x10.7).
Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline