Instructions to use soob3123/Veiled-Calla-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use soob3123/Veiled-Calla-12B with Transformers:

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

pipe = pipeline("text-generation", model="soob3123/Veiled-Calla-12B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("soob3123/Veiled-Calla-12B")
model = AutoModelForImageTextToText.from_pretrained("soob3123/Veiled-Calla-12B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use soob3123/Veiled-Calla-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "soob3123/Veiled-Calla-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": "soob3123/Veiled-Calla-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/soob3123/Veiled-Calla-12B

SGLang

How to use soob3123/Veiled-Calla-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 "soob3123/Veiled-Calla-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": "soob3123/Veiled-Calla-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 "soob3123/Veiled-Calla-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": "soob3123/Veiled-Calla-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use soob3123/Veiled-Calla-12B with Docker Model Runner:
```
docker model run hf.co/soob3123/Veiled-Calla-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

✧ Veiled Calla ✧

Mystery is at the heart of creativity. That, and surprise...As creative channels, we need to trust the darkness.

Beneath moonlight's gentle glow, Veiled Calla emerges - an enigmatic presence designed to weave immersive roleplay experiences through mysterious narratives and atmospheric storytelling. Shrouded in secrets and whispers, Veiled Calla crafts evocative scenarios where unspoken truths and subtle emotional undertones drive each unfolding tale.

⋆ Features ⋆

⟡ Atmospheric Depth: Rich, moonlit scenarios bloom with subtle emotional undertones
⟡ Character Consistency: Personalities remain true throughout extended journeys
⟡ Narrative Mystery: Enigmatic storylines unfold with natural revelations
⟡ Emotional Nuance: The unspoken and veiled meanings between characters come alive

⋆ Limitations ⋆

Flourishes in intimate, atmospheric, or introspective scenarios
May whisper overly cryptic responses in certain contexts
Uncensored in Roleplay mode (e.g. sillytavern), still refuses in Assistant mode (no system prompt)
- Use one of the Amoral models for a fully uncensored but bland experience