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

Libraries

How to use MTSAIR/multi_verse_model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MTSAIR/multi_verse_model")
model = AutoModelForCausalLM.from_pretrained("MTSAIR/multi_verse_model")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MTSAIR/multi_verse_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MTSAIR/multi_verse_model"
# 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/multi_verse_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MTSAIR/multi_verse_model

SGLang

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

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

You did it?

by Kquant03 - opened Mar 8, 2024

Discussion

Kquant03

Mar 8, 2024

https://docs.google.com/document/d/15i8nZSVJju73kHg7vkRbAw6LOknt9ORoqzdOrZu6UX4/edit?usp=sharing

I wrote a document about this...Have you truly unraveled an entire tree of thoughts within your dataset?

ammarali32

MWS AI org Mar 8, 2024

@Kquant03 Thanks for sharing your document very interesting thinking, the details about our approach is different hopefully will be published soon and would love to share, stay tuned for new models as well

Kquant03

Mar 8, 2024

@Kquant03 Thanks for sharing your document very interesting thinking, the details about our approach is different hopefully will be published soon and would love to share, stay tuned for new models as well

Yeah, we have a couple of different projects cooking too. I'm super excited to read what you guys write...Most people recommend LaTeX for papers.

mruniverse009

Mar 10, 2024

This comment has been hidden

ammarali32

MWS AI org Mar 10, 2024

Hi @mruniverse009 ,interesting project, I don't think it will support your use case without further tuning/training, i would suggest you to check the following:
https://arxiv.org/html/2401.03462v2
https://github.com/dvlab-research/LongLoRA/tree/main?tab=readme-ov-file
There are multiple research with that but these are the fresh recent studies as i expect

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