Instructions to use vanillaOVO/supermario_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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base_model: []
tags:
- mergekit
- merge
license: apache-2.0
---
This is a merge of pre-trained language models created based on [DARE](https://arxiv.org/abs/2311.03099) using [mergekit](https://github.com/cg123/mergekit).
More descriptions of the model will be added soon.
### **Loading the Model**
Use the following Python code to load the model:
```python
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:
```python
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))
```
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