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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- yanolja/EEVE-Korean-10.8B-v1.0
- upstage/SOLAR-10.7B-v1.0
base_model:
- yanolja/EEVE-Korean-10.8B-v1.0
- upstage/SOLAR-10.7B-v1.0
---
# TEST_MODEL
TEST_MODEL is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0)
* [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
## 🧩 Configuration
```yaml
base_model: yanolja/EEVE-Korean-10.8B-v1.0
dtype: float16
experts:
- source_model: yanolja/EEVE-Korean-10.8B-v1.0
positive_prompts: ["You are an helpful general-pupose assistant."]
- source_model: upstage/SOLAR-10.7B-v1.0
positive_prompts: ["You are helpful assistant."]
merge_method: slerp
gate_mode: cheap_embed
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
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jieunhan/TEST_MODEL"
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"])
``` |