---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Locutusque/Hercules-4.0-Mistral-v0.2-7B
- Open-Orca/Mistral-7B-OpenOrca
base_model:
- Locutusque/Hercules-4.0-Mistral-v0.2-7B
- Open-Orca/Mistral-7B-OpenOrca
---

# seldonium-2x3b-MoE-v0.1

seldonium-2x3b-MoE-v0.1 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Locutusque/Hercules-4.0-Mistral-v0.2-7B](https://huggingface.co/Locutusque/Hercules-4.0-Mistral-v0.2-7B)
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)

## 🧩 Configuration

```yaml
base_model: NousResearch/Hermes-2-Pro-Mistral-7B
gate_mode: cheap_embed  # Use hidden state representations to determine MoE gates
dtype: bfloat16  # Output data type
experts_per_token: 2  # Number of experts per token
experts:
  - source_model: Locutusque/Hercules-4.0-Mistral-v0.2-7B
    positive_prompts:
      - "Write a Python function to calculate the factorial of a number."
      - "Implement a quicksort algorithm to sort a list of integers."
      - "Design a Python class to represent a binary search tree."
  
  - source_model: Open-Orca/Mistral-7B-OpenOrca
    positive_prompts:
      - "Solve the logic puzzle: 'If Ann is older than Belinda, and Belinda is younger than Cathy, who is the oldest?'"
      - "Analyze the argument: 'All cats are animals. Some animals are pets. Therefore, all cats are pets.' Determine if the conclusion follows logically from the premises."
      - "Evaluate the validity of the statements: 'A is true. A is false.'"
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "jomangbp/seldonium-2x3b-MoE-v0.1"

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"])
```