moe mergekit merge chinese arabic english multilingual german french jondurbin/bagel-dpo-34b-v0.2 jondurbin/nontoxic-bagel-34b-v0.2 license:apache-2.0 region:us
---
license: apache-2.0
tags:
- moe
- mergekit
- merge
- chinese
- arabic
- english
- multilingual
- german
- french
- jondurbin/bagel-dpo-34b-v0.2
- jondurbin/nontoxic-bagel-34b-v0.2
---

# MetaModel_moe_yix2

This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2)
* [jondurbin/nontoxic-bagel-34b-v0.2](https://huggingface.co/jondurbin/nontoxic-bagel-34b-v0.2)

## 🧩 Configuration

```yamlbase_model: jondurbin/bagel-dpo-34b-v0.2
dtype: bfloat16
experts:
- positive_prompts:
  - chat
  - assistant
  - tell me
  - explain
  source_model: jondurbin/bagel-dpo-34b-v0.2
- positive_prompts:
  - chat
  - assistant
  - tell me
  - explain
  source_model: jondurbin/nontoxic-bagel-34b-v0.2
gate_mode: hidden
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/MetaModel_moe_yix2"

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"])
```