Instructions to use vanillaOVO/supermario_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanillaOVO/supermario_v4 with Transformers:

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

pipe = pipeline("text-generation", model="vanillaOVO/supermario_v4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/supermario_v4")
model = AutoModelForCausalLM.from_pretrained("vanillaOVO/supermario_v4")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vanillaOVO/supermario_v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanillaOVO/supermario_v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanillaOVO/supermario_v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vanillaOVO/supermario_v4

SGLang

How to use vanillaOVO/supermario_v4 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 "vanillaOVO/supermario_v4" \
    --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": "vanillaOVO/supermario_v4",
		"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 "vanillaOVO/supermario_v4" \
        --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": "vanillaOVO/supermario_v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vanillaOVO/supermario_v4 with Docker Model Runner:
```
docker model run hf.co/vanillaOVO/supermario_v4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a merge of pre-trained language models created based on DARE using mergekit.

More descriptions of the model will be added soon.

Loading the Model

Use the following Python code to load the model:

import torch
from transformers import MistralForCausalLM, AutoTokenizer

model = MistralForCausalLM.from_pretrained("vanillaOVO/supermario_v4", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/supermario_v4")

Generating Text

To generate text, use the following Python code:

text = "Large language models are "
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 73

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for vanillaOVO/supermario_v4

Merges

15 models

Quantizations

1 model

Paper for vanillaOVO/supermario_v4

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 33