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---
{}
---
---
license: apache-2.0
tags:
- moe
- mergekit
- merge
- gagan3012/MetaModel
- jeonsworld/CarbonVillain-en-10.7B-v2
- jeonsworld/CarbonVillain-en-10.7B-v4
- TomGrc/FusionNet_linear
---
# MetaModel_moe_multilingualv1
This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel)
* [jeonsworld/CarbonVillain-en-10.7B-v2](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v2)
* [jeonsworld/CarbonVillain-en-10.7B-v4](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4)
* [TomGrc/FusionNet_linear](https://huggingface.co/TomGrc/FusionNet_linear)
## 🧩 Configuration
```yaml
base_model: gagan3012/MetaModel
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: gagan3012/MetaModel
- source_model: jeonsworld/CarbonVillain-en-10.7B-v2
- source_model: jeonsworld/CarbonVillain-en-10.7B-v4
- source_model: TomGrc/FusionNet_linear
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "gagan3012/MetaModel_moe_multilingualv1"
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"])
``` |