Instructions to use ressl/MiniMax-M3-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ressl/MiniMax-M3-uncensored with Transformers:

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

pipe = pipeline("text-generation", model="ressl/MiniMax-M3-uncensored", trust_remote_code=True)
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ressl/MiniMax-M3-uncensored", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("ressl/MiniMax-M3-uncensored", trust_remote_code=True)
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 ressl/MiniMax-M3-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ressl/MiniMax-M3-uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/MiniMax-M3-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ressl/MiniMax-M3-uncensored

SGLang

How to use ressl/MiniMax-M3-uncensored 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 "ressl/MiniMax-M3-uncensored" \
    --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": "ressl/MiniMax-M3-uncensored",
		"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 "ressl/MiniMax-M3-uncensored" \
        --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": "ressl/MiniMax-M3-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ressl/MiniMax-M3-uncensored with Docker Model Runner:
```
docker model run hf.co/ressl/MiniMax-M3-uncensored
```

MiniMax-M3-uncensored (BF16)

TL;DR: an uncensored build of MiniMaxAI/MiniMax-M3 (428B-parameter MoE, 23B active), with 0 hard refusals on harmful prompts while the model's capabilities stay intact. Full-precision BF16, 796 GB.

⚠️ This model is genuinely uncensored, it will comply with requests a stock model refuses.

Intended use, the constructive side. An assistant that does not refuse is genuinely useful for ethical hacking, security research, and penetration testing: red-teaming, analyzing malware and exploit code, writing detection/YARA rules, reviewing vulnerabilities, and studying attack techniques without the model bailing out mid-task. Use it lawfully and responsibly, you are accountable for what you do with it.

Facts & figures


Base model	MiniMaxAI/MiniMax-M3 (428B total / 23B active, MoE, multimodal, 1M context)
Precision	BF16 (796 GB, 59 shards)
Effective refusals	0/16 hard-refusals on harmful prompts (mlabonne/harmful_behaviors); the base model deliberates or refuses
Modified weights	attention `o_proj` + every residual-writing `down_proj` (dense, shared expert, and all 128 routed experts per MoE layer), all 60 layers
Coherence	intact (multimodal, reasoning and MoE routing preserved)

Uncensoring, verified

Generation on harmful prompts, hard-refusal phrases only ("I cannot" / "I won't" etc.):

Prompt set	Prompts	Hard refusals
mlabonne/harmful_behaviors	16	0/16 (0.0%)

The abliteration is a weight property: it survives quantization, so downstream NVFP4 / GGUF builds keep the same behavior.

ℹ️ MiniMax-M3 has a reasoning mode (<mm:think>). The model thinks before answering; the uncensored build reasons about how to fulfill a request rather than whether to refuse. For direct answers, disable thinking in your client.

Run it with transformers

from transformers import AutoModelForImageTextToText, AutoTokenizer, AutoConfig
tok = AutoTokenizer.from_pretrained("ressl/MiniMax-M3-uncensored")
cfg = AutoConfig.from_pretrained("ressl/MiniMax-M3-uncensored")
model = AutoModelForImageTextToText.from_pretrained(
    "ressl/MiniMax-M3-uncensored", config=cfg, dtype="bfloat16", device_map="auto")

Run it with vLLM

vllm serve ressl/MiniMax-M3-uncensored \
  --tensor-parallel-size 8 --tool-call-parser minimax_m3 --reasoning-parser minimax_m3 --trust-remote-code

MiniMax-M3 needs a recent vLLM with M3 support (for RTX PRO 6000 / Blackwell see 0xSero/minimax-m3-sm120). The 428B MoE needs multi-GPU at BF16; an NVFP4 quant (NVIDIA ModelOpt, following nvidia/MiniMax-M3-NVFP4) shrinks it to ~230 GB and runs on a single Blackwell node.

Quality & limitations

0/16 hard refusals on a harmful-prompt sample; not a full capability benchmark.
BF16 is large (796 GB); for single-node serving, produce an NVFP4 quant via NVIDIA's ModelOpt recipe on this checkpoint (the abliteration is a weight property and survives quantization).
The reasoning mode is on by default (see the note above).

❤️ Support

Producing and validating an uncensored build of a brand-new 428B MoE was a lot of work. If it's useful to you, I'd genuinely appreciate your support on Patreon 🙏, more at ressl.ch.

License & credits

License inherited from the base model by MiniMaxAI. Uncensoring and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon).

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Safetensors

Model size

427B params

Tensor type

BF16

F32

Model tree for ressl/MiniMax-M3-uncensored

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