
# MetaModel_moex8

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)
* [DopeorNope/SOLARC-M-10.7B](https://huggingface.co/DopeorNope/SOLARC-M-10.7B)
* [VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)
* [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
* [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0)

## 🧩 Configuration

```yamlbase_model: jeonsworld/CarbonVillain-en-10.7B-v4
dtype: bfloat16
experts:
- positive_prompts:
  - ''
  source_model: gagan3012/MetaModel
- positive_prompts:
  - ''
  source_model: jeonsworld/CarbonVillain-en-10.7B-v2
- positive_prompts:
  - ''
  source_model: jeonsworld/CarbonVillain-en-10.7B-v4
- positive_prompts:
  - ''
  source_model: TomGrc/FusionNet_linear
- positive_prompts:
  - ''
  source_model: DopeorNope/SOLARC-M-10.7B
- positive_prompts:
  - ''
  source_model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- positive_prompts:
  - ''
  source_model: upstage/SOLAR-10.7B-Instruct-v1.0
- positive_prompts:
  - ''
  source_model: fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
gate_mode: hidden
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/MetaModel_moex8"

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"])
```
{"license": "apache-2.0", "tags": ["moe", "mergekit", "merge", "chinese", "arabic", "english", "multilingual", "german", "french", "gagan3012/MetaModel", "jeonsworld/CarbonVillain-en-10.7B-v2", "jeonsworld/CarbonVillain-en-10.7B-v4", "TomGrc/FusionNet_linear", "DopeorNope/SOLARC-M-10.7B", "VAGOsolutions/SauerkrautLM-SOLAR-Instruct", "upstage/SOLAR-10.7B-Instruct-v1.0", "fblgit/UNA-SOLAR-10.7B-Instruct-v1.0"]}
transformers safetensors mixtral text-generation moe mergekit merge chinese arabic english multilingual german french gagan3012/MetaModel jeonsworld/CarbonVillain-en-10.7B-v2 jeonsworld/CarbonVillain-en-10.7B-v4 TomGrc/FusionNet_linear DopeorNope/SOLARC-M-10.7B VAGOsolutions/SauerkrautLM-SOLAR-Instruct upstage/SOLAR-10.7B-Instruct-v1.0 fblgit/UNA-SOLAR-10.7B-Instruct-v1.0 conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us