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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Malikeh1375/llama-3-base-instruct-slerp
- orpo-explorers/hf-llama3-8b-orpo-v0.0
base_model:
- Malikeh1375/llama-3-base-instruct-slerp
- orpo-explorers/hf-llama3-8b-orpo-v0.0
---
# Lamma3merge3-15B-MoE
Lamma3merge3-15B-MoE is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Malikeh1375/llama-3-base-instruct-slerp](https://huggingface.co/Malikeh1375/llama-3-base-instruct-slerp)
* [orpo-explorers/hf-llama3-8b-orpo-v0.0](https://huggingface.co/orpo-explorers/hf-llama3-8b-orpo-v0.0)
## 🧩 Configuration
```yaml
base_model: Malikeh1375/llama-3-base-instruct-slerp
experts:
- source_model: Malikeh1375/llama-3-base-instruct-slerp
positive_prompts: ["why"]
- source_model: orpo-explorers/hf-llama3-8b-orpo-v0.0
positive_prompts: ["what"]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "allknowingroger/Lamma3merge3-15B-MoE"
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"])
``` |