Instructions to use gagan3012/MetaModel_moe_multilingualv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gagan3012/MetaModel_moe_multilingualv1 with Transformers:

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

pipe = pipeline("text-generation", model="gagan3012/MetaModel_moe_multilingualv1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gagan3012/MetaModel_moe_multilingualv1")
model = AutoModelForCausalLM.from_pretrained("gagan3012/MetaModel_moe_multilingualv1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gagan3012/MetaModel_moe_multilingualv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gagan3012/MetaModel_moe_multilingualv1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel_moe_multilingualv1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gagan3012/MetaModel_moe_multilingualv1

SGLang

How to use gagan3012/MetaModel_moe_multilingualv1 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 "gagan3012/MetaModel_moe_multilingualv1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel_moe_multilingualv1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "gagan3012/MetaModel_moe_multilingualv1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel_moe_multilingualv1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gagan3012/MetaModel_moe_multilingualv1 with Docker Model Runner:
```
docker model run hf.co/gagan3012/MetaModel_moe_multilingualv1
```

MetaModel_moe_multilingualv1 / README.md

gagan3012

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	---
	license: apache-2.0
	tags:
	- moe
	language:
	- en
	- hi
	- de
	- fr
	- ar
	- ja
	---

	# 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:
	* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
	* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
	* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
	* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
	* [davidkim205/komt-mistral-7b-v1](https://huggingface.co/davidkim205/komt-mistral-7b-v1)
	* [OpenBuddy/openbuddy-zephyr-7b-v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
	* [manishiitg/open-aditi-hi-v1](https://huggingface.co/manishiitg/open-aditi-hi-v1)
	* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)

	## 🧩 Configuration

	```yaml
	base_model: mlabonne/Marcoro14-7B-slerp
	dtype: bfloat16
	experts:
	- positive_prompts:
	- chat
	- assistant
	- tell me
	- explain
	source_model: openchat/openchat-3.5-1210
	- positive_prompts:
	- code
	- python
	- javascript
	- programming
	- algorithm
	source_model: beowolx/CodeNinja-1.0-OpenChat-7B
	- positive_prompts:
	- storywriting
	- write
	- scene
	- story
	- character
	source_model: maywell/PiVoT-0.1-Starling-LM-RP
	- positive_prompts:
	- reason
	- math
	- mathematics
	- solve
	- count
	source_model: WizardLM/WizardMath-7B-V1.1
	- positive_prompts:
	- korean
	- answer in korean
	- korea
	source_model: davidkim205/komt-mistral-7b-v1
	- positive_prompts:
	- chinese
	- china
	- answer in chinese
	source_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
	- positive_prompts:
	- hindi
	- india
	- hindu
	- answer in hindi
	source_model: manishiitg/open-aditi-hi-v1
	- positive_prompts:
	- german
	- germany
	- answer in german
	- deutsch
	source_model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
	gate_mode: hidden
	```

	## 💻 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"])
	```