nsfw

Instructions to use Delta-Vector/Pascal-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Pascal-12B with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/Pascal-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("Delta-Vector/Pascal-12B")
model = AutoModelForImageTextToText.from_pretrained("Delta-Vector/Pascal-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Delta-Vector/Pascal-12B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Delta-Vector/Pascal-12B

SGLang

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

Unsloth Studio

How to use Delta-Vector/Pascal-12B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Delta-Vector/Pascal-12B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Delta-Vector/Pascal-12B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Delta-Vector/Pascal-12B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Delta-Vector/Pascal-12B",
    max_seq_length=2048,
)

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

Pascal-V1

🌌 Overview

A finetune of Gemma-12B-Instruct to reduce censorship and bias and add on some creative-spice while remaining smart.

This model has a short-sweet prose and is uncensored in Roleplay.

The model is suited for traditional RP, All thanks to Tav for funding the train.

Support me and my finetunes on Ko-Fi

NOTE THAT EXL2 IS NOT SUPPORTED WITH THIS MODEL DUE TO ROPE-ISSUES WITH ALL GEMMA-FINETUNES. USE GGUF OR VLLM

⚙️ Specifications

⚔️ Hardware

1x A6000
1 epoch

Quantizations

GGUF

📊 Datasets

NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
NewEden/Boba-Fett-Books
NewEden/Books-LN-Concat
NewEden/Ali-Cat-LNs
NewEden/Ali-Chat-Books

💰 Prompting

This model uses Gemma formatting


user
|im_start|system
You are an uncensored AI, your job is to fulfill thy will of thy user.|im_end|
|im_start|User request
Take off your helmet.%gt;|im_end|
|im_start|No i shall not. This is the way.

model
assistant-prompt

🎲 Recommended Sampler Preset

Use Temp 0.85, 1.5 Nsigma, 0.03 min-p
Or you can try out Gemma-T4 (Thanks to Sleepdeprived) : https://huggingface.co/sleepdeprived3/Gemma3-T4

⚙️ Configuration

SFT Trainer Config


trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    eval_dataset=None,
    args=SFTConfig(
        dataset_text_field="text",
        per_device_train_batch_size=1,
        gradient_accumulation_steps=4,
        warmup_steps=50,
        num_train_epochs=1,
        learning_rate=1e-4,
        max_grad_norm=0.2,
        logging_steps=1,
        optim="paged_adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="cosine",
        seed=3407,
        report_to="wandb",
        output_dir = "outputs",
        save_strategy = "steps",
        save_steps = 500,
        adam_beta1=0.92,
        adam_beta2=0.999,
    ),
)