---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
---

# Lumina-5-Instruct

Lumina-5-Instruct is a Mixture of Experts (MoE) made with [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing). This model uses a context window of up to 32k.

# Response Examples
``` Math
Input: 25 - 4 * 2 + 3 = ?
------------------------------------
Output: Let's solve this step by step according to the order of operations (PEMDAS):

1. Perform the multiplication inside the parentheses first: 4 * 2 = 8
2. Now, we have 25 - 8 + 3
3. Subtract 8 from 25: 25 - 8 = 17
4. Finally, add 3 to the result: 17 + 3 = 20

The answer is 20.
```

``` Reasoning
Input: Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step.
------------------------------------
Output: Let's analyze this comparison step by step:

1. We are given three individuals - Jane, Joe, and Sam.
2. The information provided states that "Jane is faster than Joe" and "Joe is faster than Sam."
3. To determine if Sam is faster than Jane, we need to compare their relative speeds based on the given information.
4. Since Joe is faster than Sam but slower than Jane, it means that Jane must be faster than both Joe and Sam.
5. Therefore, there is no scenario in which Sam can be faster than Jane, as per the given comparisons.

Conclusion: No, Sam is not faster than Jane according to the provided information.
```

## 🏆 Open LLM Leaderboard Evaluation Results 
Coming soon.

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Ppoyaa/Lumina-5-Instruct"

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"])
```