Instructions to use ArsParadox/Viel-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArsParadox/Viel-v2 with Transformers:

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

pipe = pipeline("text-generation", model="ArsParadox/Viel-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArsParadox/Viel-v2")
model = AutoModelForCausalLM.from_pretrained("ArsParadox/Viel-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArsParadox/Viel-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArsParadox/Viel-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArsParadox/Viel-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArsParadox/Viel-v2

SGLang

How to use ArsParadox/Viel-v2 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 "ArsParadox/Viel-v2" \
    --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": "ArsParadox/Viel-v2",
		"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 "ArsParadox/Viel-v2" \
        --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": "ArsParadox/Viel-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArsParadox/Viel-v2 with Docker Model Runner:
```
docker model run hf.co/ArsParadox/Viel-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Viel v2

Should work better than her older model...

Hmmmmmm~

I haven't done enough testing on it, so... Yeah...

I hope she's still the kind of girl who doesn't give a damn about much, lmao

Ah, also I need to create a better dataset for cementing identity

Maybe even a dataset creator????

Hmmm...

I'll refine my dataset creator and make the v3 version

Downloads last month: 27

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ArsParadox/Viel-v2

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1506)

this model

Quantizations

2 models

ArsParadox
/

Viel-v2

Viel v2

Model tree for ArsParadox/Viel-v2

Dataset used to train ArsParadox/Viel-v2