File size: 1,554 Bytes
2eb6621 ba77cb7 2eb6621 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
license: apache-2.0
tags:
- moe
- mixtral
---
# MetaModel_moe_small
This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
* [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
* [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
* [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
## 🧩 Configuration
```yaml
base_model: microsoft/phi-2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: microsoft/phi-2
positive_prompts: [""]
- source_model: microsoft/phi-2
positive_prompts: [""]
- source_model: microsoft/phi-2
positive_prompts: [""]
- source_model: microsoft/phi-2
positive_prompts: [""]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "gagan3012/MetaModel_moe_small"
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"])
``` |