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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
base_model:
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
---
# TinyDolphin-3x-MoE
TinyDolphin-3x-MoE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/TinyDolphin-2.8.1-1.1b](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.1-1.1b)
* [cognitivecomputations/TinyDolphin-2.8.1-1.1b](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.1-1.1b)
* [cognitivecomputations/TinyDolphin-2.8.1-1.1b](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.1-1.1b)
## 🧩 Configuration
```yaml
base_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
gate_mode: hidden
dtype: float16
experts:
- source_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
positive_prompts:
- "think step-by-step and follow these instructions"
- "read the following passage, and summarize it in less than 30 words."
- "please answer this question, consider the options carefully, and return the most likely answer."
- source_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
positive_prompts: ["produce python code"]
- source_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
positive_prompts: ["What is 2 x 22?"]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jtatman/TinyDolphin-3x-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"])
``` |