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---
license: mit
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Open-Orca/Mistral-7B-OpenOrca
- Crystalcareai/Evol-Mistral
base_model:
- Open-Orca/Mistral-7B-OpenOrca
- Crystalcareai/Evol-Mistral
---
# Evolorxa-14b
Evolorxa-14b is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [Crystalcareai/Evol-Mistral](https://huggingface.co/Crystalcareai/Evol-Mistral)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Open-Orca/Mistral-7B-OpenOrca
layer_range: [0, 32]
- model: Crystalcareai/Evol-Mistral
layer_range: [0, 32]
merge_method: slerp
base_model: Open-Orca/Mistral-7B-OpenOrca
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
experts:
- source_model: Open-Orca/Mistral-7B-OpenOrca
positive_prompts:
- "chat"
- "reasoning"
- "Why would"
- "explain"
- source_model: Crystalcareai/Evol-Mistral
positive_prompts:
- "instruction"
- "create a"
- "You must"
- "Your job"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Crystalcareai/Evolorxa-14b"
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"])
``` |