Instructions to use Xenon1/MetaModel_moex8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xenon1/MetaModel_moex8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Xenon1/MetaModel_moex8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xenon1/MetaModel_moex8")
model = AutoModelForCausalLM.from_pretrained("Xenon1/MetaModel_moex8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Xenon1/MetaModel_moex8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xenon1/MetaModel_moex8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenon1/MetaModel_moex8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Xenon1/MetaModel_moex8

SGLang

How to use Xenon1/MetaModel_moex8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Xenon1/MetaModel_moex8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenon1/MetaModel_moex8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Xenon1/MetaModel_moex8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenon1/MetaModel_moex8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xenon1/MetaModel_moex8 with Docker Model Runner:
```
docker model run hf.co/Xenon1/MetaModel_moex8
```

MetaModel_moex8 / README.md

gagan3012

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	---
	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
	---

	# 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"])
	```