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://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
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 Desktop
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
```

alduin-4b-it-base / README.md

Muhammadreza

Update README.md

057a681 verified 5 months ago

preview code

Raw History Blame Contribute Delete

2.58 kB

---
tags:
- unsloth
- heretic
- uncensored
- decensored
- abliterated
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model:
- google/gemma-3-4b-it
---
# Alduin 4B 

<p align="center">
  <img src="./alduin-logo.jpg" />
</p>

This model is a [heretic](https://github.com/p-e-w/heretic) version based on [Gemma 3 4B](https://hf.co/google/gemma-3-4b-it) 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](https://runpod.io) 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):

```python
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: 

```python
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant"
    },
    {
        "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?"}
        ]
    }
]
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)

	---
	tags:
	- unsloth
	- heretic
	- uncensored
	- decensored
	- abliterated
	license: gemma
	library_name: transformers
	pipeline_tag: text-generation
	extra_gated_heading: Access Gemma on Hugging Face
	extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
	agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
	Face and click below. Requests are processed immediately.
	extra_gated_button_content: Acknowledge license
	base_model:
	- google/gemma-3-4b-it
	---

	# Alduin 4B

	<p align="center">
	<img src="./alduin-logo.jpg" />
	</p>

	This model is a [heretic](https://github.com/p-e-w/heretic) version based on [Gemma 3 4B](https://hf.co/google/gemma-3-4b-it) 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](https://runpod.io) 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):

	```python
	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:

	```python
	messages = [
	{
	"role": "system",
	"content": "You are a helpful assistant"
	},
	{
	"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?"}
	]
	}
	]

	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)