Instructions to use DarkArtsForge/Vesper-Zenith-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarkArtsForge/Vesper-Zenith-12B with Transformers:

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

pipe = pipeline("text-generation", model="DarkArtsForge/Vesper-Zenith-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DarkArtsForge/Vesper-Zenith-12B")
model = AutoModelForCausalLM.from_pretrained("DarkArtsForge/Vesper-Zenith-12B")
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]:]))

NeMo
How to use DarkArtsForge/Vesper-Zenith-12B with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DarkArtsForge/Vesper-Zenith-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkArtsForge/Vesper-Zenith-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkArtsForge/Vesper-Zenith-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DarkArtsForge/Vesper-Zenith-12B

SGLang

How to use DarkArtsForge/Vesper-Zenith-12B 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 "DarkArtsForge/Vesper-Zenith-12B" \
    --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": "DarkArtsForge/Vesper-Zenith-12B",
		"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 "DarkArtsForge/Vesper-Zenith-12B" \
        --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": "DarkArtsForge/Vesper-Zenith-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DarkArtsForge/Vesper-Zenith-12B with Docker Model Runner:
```
docker model run hf.co/DarkArtsForge/Vesper-Zenith-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CREATIVITYUNBOUND

ROLEPLAYIMMERSIVE

LOGICENHANCED

🌌 About The Model

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

It's recommended to use the ChatML template with this model, although it also works with Mistral Tekken.

There are some refusals but it's very creative and sadistic when prompted, and it can be ablated or jailbroken if needed.

Merge Details

Merge Method

This model was merged using the DELLA merge method using mistralai/Mistral-Nemo-Instruct-2407 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

architecture: MistralForCausalLM
base_model: B:\12B\mistralai--Mistral-Nemo-Instruct-2407
models:
  - model: B:\12B\MN-Raven-12B-v1
    parameters:
      weight: 0.25
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\Lambent--Arsenic-Shahrazad-12B-v4.4
    parameters:
      weight: 0.25
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\Retreatcost--Evertide-RX-12B
    parameters:
      weight: 0.25
      density: 0.9
      epsilon: 0.09
  - model: B:\12B\WokeAI--Tankie-DPE-12B-SFT-v2
    parameters:
      weight: 0.25
      density: 0.9
      epsilon: 0.09
merge_method: della
parameters:  
  lambda: 1.0
  normalize: false
  int8_mask: false
  rescale: true
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto