metadata
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
model-index:
- name: Lumina-3.5
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: 71.59
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
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: 88.82
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
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: 64.48
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
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: 75.66
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
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: 83.98
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
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: 67.93
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5
name: Open LLM Leaderboard
Lumina-3.5
Lumina-3.5 is a Mixture of Experts (MoE) made with LazyMergekit with improved reasoning from Lumina-3. This model uses a context window of up to 32k.
Response Examples
Input: 25 - 4 * 2 + 3 = ?
------------------------------------
Output: To solve this equation, we will follow the order of operations (PEMDAS/BODMAS). This means performing any multiplications and divisions first from left to right, and then additions and subtractions from left to right.
The given equation is: 25 - 4 * 2 + 3
First, let's perform the multiplication:
4 * 2 = 8
Now, our equation looks like: 25 - 8 + 3
Next, perform the subtraction:
25 - 8 = 17
Finally, do the addition:
17 + 3 = 20
So, the result of the equation 25 - 4 * 2 + 3 is 20.
Input: Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step.
------------------------------------
Output: To answer this question, let's analyze the given information step by step:
We are provided with a comparison between three individuals - Jane, Joe, and Sam - in terms of their relative speeds. The statements tell us the following about their ranks:
Jane > Joe (Jane is faster than Joe)
Joe > Sam (Joe is faster than Sam)
Now we need to determine if the opposite relationship holds true: whether Sam is faster than Jane or not. Based on the given comparisons, we know that both Jane and Joe have faster speeds compared to Sam. There isn't any piece of information suggesting otherwise.
Since the original premise established an ordered ranking among these individuals (Jane > Joe > Sam), and the direct opposite of the relationship between Sam and the other two is not present, we cannot conclude that Sam is faster than Jane.
Thus, based on the provided information, our reasoning confirms that Sam is not faster than Jane.
🏆 Open LLM Leaderboard Evaluation Results
| Metric | Value |
|---|---|
| Avg. | 75.41 |
| AI2 Reasoning Challenge (25-Shot) | 71.59 |
| HellaSwag (10-Shot) | 88.82 |
| MMLU (5-Shot) | 64.48 |
| TruthfulQA (0-shot) | 75.66 |
| Winogrande (5-shot) | 83.98 |
| GSM8k (5-shot) | 67.93 |
Quants
Special thanks to GGUFs made by mradermacher
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ppoyaa/Lumina-3.5"
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. | 75.41 |
| AI2 Reasoning Challenge (25-Shot) | 71.59 |
| HellaSwag (10-Shot) | 88.82 |
| MMLU (5-Shot) | 64.48 |
| TruthfulQA (0-shot) | 75.66 |
| Winogrande (5-shot) | 83.98 |
| GSM8k (5-shot) | 67.93 |