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.
1Input: 25 - 4 * 2 + 3 = ?
2------------------------------------
3Output: 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.
4
5The given equation is: 25 - 4 * 2 + 3
6
7First, let's perform the multiplication:
84 * 2 = 8
9
10Now, our equation looks like: 25 - 8 + 3
11
12Next, perform the subtraction:
1325 - 8 = 17
14
15Finally, do the addition:
1617 + 3 = 20
17
18So, the result of the equation 25 - 4 * 2 + 3 is 20.
1Input: Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step.
2------------------------------------
3Output: To answer this question, let's analyze the given information step by step:
4
5We 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:
6Jane > Joe (Jane is faster than Joe)
7Joe > Sam (Joe is faster than Sam)
8Now 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.
9
10Since 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.
11
12Thus, based on the provided information, our reasoning confirms that Sam is not faster than Jane.
Special thanks to GGUFs made by
mradermacher1!pip install -qU transformers bitsandbytes accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Ppoyaa/Lumina-3.5"
8
9tokenizer = AutoTokenizer.from_pretrained(model)
10pipeline = transformers.pipeline(
11 "text-generation",
12 model=model,
13 model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
14)
15
16messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
17prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
18outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
19print(outputs[0]["generated_text"])
Detailed results can be found
here
