45 lines
2.2 KiB
Markdown
45 lines
2.2 KiB
Markdown
|
---
|
||
|
|
license: apache-2.0
|
||
|
|
tags:
|
||
|
|
- moe
|
||
|
|
- mergekit
|
||
|
|
---
|
||
|
|
|
||
|
|
# NeuralMix-2x7b
|
||
|
|
|
||
|
|
This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
|
||
|
|
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
|
||
|
|
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
|
||
|
|
|
||
|
|
## 💻 Usage
|
||
|
|
|
||
|
|
```python
|
||
|
|
!pip install -qU transformers bitsandbytes accelerate
|
||
|
|
|
||
|
|
from transformers import AutoTokenizer
|
||
|
|
import transformers
|
||
|
|
import torch
|
||
|
|
|
||
|
|
model = "mlabonne/NeuralMix-2x7b"
|
||
|
|
|
||
|
|
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"])
|
||
|
|
```
|
||
|
|
|
||
|
|
Output:
|
||
|
|
```
|
||
|
|
A Mixture of Experts (ME) is a neural network architecture that allows for adaptive specialization of its hidden layers. It consists of an input layer, a mixture of expert layers with a set of hidden layers, and an output layer. The expert layers have different specializations and each one is responsible for predicting the output for a particular subset of the input data. The mixture of experts uses a gating network to dynamically select the expert layer that best fits the current input data. This adaptive approach can improve the performance and generalization capabilities of the neural network.
|
||
|
|
|
||
|
|
The Mixture of Experts model is particularly useful in situations where the data is complex, heterogeneous, or has varying structures. By enabling each expert to specialize in a particular type of input, the Mixture of Experts can learn to effectively handle diverse input data and provide more accurate predictions.
|
||
|
|
|
||
|
|
Overall, the Mixture of Experts can be seen as a type of neural network that combines the strengths of multiple models to create a more powerful and flexible predictive tool.
|
||
|
|
```
|