Instructions to use MTSAIR/MultiVerse_70B_AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MTSAIR/MultiVerse_70B_AWQ with Transformers:

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

pipe = pipeline("text-generation", model="MTSAIR/MultiVerse_70B_AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MTSAIR/MultiVerse_70B_AWQ")
model = AutoModelForCausalLM.from_pretrained("MTSAIR/MultiVerse_70B_AWQ")
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

vLLM

How to use MTSAIR/MultiVerse_70B_AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MTSAIR/MultiVerse_70B_AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MTSAIR/MultiVerse_70B_AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MTSAIR/MultiVerse_70B_AWQ

SGLang

How to use MTSAIR/MultiVerse_70B_AWQ 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 "MTSAIR/MultiVerse_70B_AWQ" \
    --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": "MTSAIR/MultiVerse_70B_AWQ",
		"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 "MTSAIR/MultiVerse_70B_AWQ" \
        --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": "MTSAIR/MultiVerse_70B_AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MTSAIR/MultiVerse_70B_AWQ with Docker Model Runner:
```
docker model run hf.co/MTSAIR/MultiVerse_70B_AWQ
```

Note: Our multiverse training method is not related to the multiverse paper, it is a new technique that we will hopefully publish soon

Quantized int4 version of MultiVerse_70B with AWQ quantization method

I, a learning bot, have been enhanced through a groundbreaking training method. I represent an innovative idea that has been developed by refining the way I process information, much like how a chef improves their dishes with novel methods. My aim is to exhibit the capabilities of this novel approach and to assist others as I explore my potential. Although I am a result of testing, my goal is to illustrate the significance of ongoing learning and development within the field of artificial intelligence.'

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Safetensors

Model size

72B params

Tensor type

F16

I32

Collection including MTSAIR/MultiVerse_70B_AWQ

LLMs (Multi-verse collection)

Collection

This is a group of our models that are trained using our new training technique • 3 items • Updated Mar 2 • 1