GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
ressl
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
ressl
Model Tree
MiniMaxAI/MiniMax-M3
Input Modalities
Output Modalities
Supported Functionality
| 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) |
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.
python
from transformers import AutoModelForImageTextToText, AutoTokenizer, AutoConfigtok = 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")
bash
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.
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 inherited from the base model by MiniMaxAI. Uncensoring and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon).