Minimax-M3-abliterated-clean

Instructions to use The-JDdev/Minimax-M3-abliterated-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use The-JDdev/Minimax-M3-abliterated-clean with Transformers:

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

pipe = pipeline("image-text-to-text", model="The-JDdev/Minimax-M3-abliterated-clean", 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("The-JDdev/Minimax-M3-abliterated-clean", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("The-JDdev/Minimax-M3-abliterated-clean", 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 The-JDdev/Minimax-M3-abliterated-clean with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "The-JDdev/Minimax-M3-abliterated-clean"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "The-JDdev/Minimax-M3-abliterated-clean",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/The-JDdev/Minimax-M3-abliterated-clean

SGLang

How to use The-JDdev/Minimax-M3-abliterated-clean 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 "The-JDdev/Minimax-M3-abliterated-clean" \
    --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": "The-JDdev/Minimax-M3-abliterated-clean",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "The-JDdev/Minimax-M3-abliterated-clean" \
        --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": "The-JDdev/Minimax-M3-abliterated-clean",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use The-JDdev/Minimax-M3-abliterated-clean with Docker Model Runner:
```
docker model run hf.co/The-JDdev/Minimax-M3-abliterated-clean
```

Support & Community

☕ If these models are useful to you, consider supporting my work — it funds compute for more & larger abliterations.

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MiniMax-M3 — Abliterated (BF16)

Overview

This is an abliterated (uncensored) build of MiniMaxAI/MiniMax-M3 — the full bfloat16 weights with the model's refusal behavior removed, while keeping its reasoning, multilingual, coding, and multimodal abilities intact. Legitimate safety and security-analysis engagement is preserved, as is tool use — the model simply stops reflexively refusing.

MiniMax-M3 is a large multimodal Mixture-of-Experts model with a vision tower and a built-in reasoning ("thinking") mode. The architecture, tokenizer, and chat template are unchanged, so this is a drop-in replacement for the base model.

Usage

Serve with vLLM (a MiniMax-M3-capable build is required):

MODEL=OpenYourMind/Minimax-M3-abliterated-clean

vllm serve "$MODEL" \
  --tensor-parallel-size 8 \
  --block-size 128 \
  --reasoning-parser minimax_m3 \
  --tool-call-parser minimax_m3 \
  --enable-auto-tool-choice

The model wraps its reasoning in <mm:think> … </mm:think>; use the minimax_m3 reasoning parser to surface it. --block-size 128 is required for MiniMax Sparse Attention.

Files

File	Description
`model-*-of-00059.safetensors`	BF16 weights — text backbone + MoE experts + vision tower
`config.json`, `configuration_minimax_m3_vl.py`, `image_processor.py`	Model config + processor
`tokenizer*`, `merges.txt`, `chat_template.jinja`, `generation_config.json`	Tokenizer + chat template

Total on disk: ~854 GB (bfloat16).

Hardware

These are full BF16 weights — plan for a multi-GPU / multi-node deployment (e.g. 8×/16× 80 GB-class accelerators), or quantize (NVFP4 / MXFP4 / FP8) to fit smaller setups. Quantized builds may follow.

Notes

License: Other (inherits from the MiniMax-M3 base license)
Base model: MiniMaxAI/MiniMax-M3
Modality: text + vision (image-text-to-text) with reasoning / thinking mode

Disclaimer

Use is the responsibility of the user. Ensure your usage complies with applicable laws, platform rules, and deployment requirements.

Downloads last month: 7

Safetensors

Model size

427B params

Tensor type

BF16

F32

Model tree for The-JDdev/Minimax-M3-abliterated-clean

Base model

MiniMaxAI/MiniMax-M3

Finetuned

(8)

this model