Instructions to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4 # Run inference directly in the terminal: llama cli -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4 # Run inference directly in the terminal: llama cli -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4 # Run inference directly in the terminal: ./llama-cli -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4 # Run inference directly in the terminal: ./build/bin/llama-cli -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Use Docker
docker model run hf.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
- LM Studio
- Jan
- Ollama
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with Ollama:
ollama run hf.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
- Unsloth Desktop
- Pi
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with Docker Model Runner:
docker model run hf.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
- Lemonade
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Run and chat with the model
lemonade run user.MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF-MXFP4
List all available models
lemonade list
- Hermes Agent
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF:MXFP4" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Download README.md from MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 6.53 kB
-
https://huggingface.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF/resolve/main/README.md
- Command line
-
hf download hf://MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-GGUF/resolve/main/README.md
6.53 kB
| license: mit | |
| base_model: XiaomiMiMo/MiMo-V2.6-Flash-RL | |
| tags: | |
| - uncensored | |
| - abliterated | |
| - heretic | |
| - gguf | |
| - mimo | |
| # MiMo-V2.6-Flash-RL Uncensored Heretic β merged GGUF | |
| > **β οΈ Content warning:** This model has had the base model's refusal | |
| > behavior surgically suppressed. The resulting model **will** comply with | |
| > requests the base model refuses, including requests that are harmful, | |
| > unethical, offensive, or illegal. It has reduced safety guardrails. See | |
| > [Responsible use](#responsible-use--disclaimer) below β **you are solely | |
| > responsible for what you do with it.** | |
| This is a **merged GGUF** of | |
| [MiMo-V2.6-Flash-RL](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) | |
| (309B total / 15B active MoE, MIT license) decensored / "abliterated" with | |
| **heretic-gguf** β a GGUF-native port of | |
| [Heretic](https://github.com/p-e-w/heretic)'s Optuna-optimized directional | |
| ablation, which runs the whole search directly on quantized GGUF weights via | |
| llama.cpp. The ablation (trial 85 of study `mimo26flash`) was baked directly | |
| into the | |
| [MXFP4](https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF) weights: | |
| edited tensors were dequantized, patched with the exact ablation delta, and | |
| requantized to their original type; everything else is a byte-for-byte copy. | |
| **This is the zero-runtime-overhead form** β a drop-in base model, no | |
| `--lora` flag needed. If you prefer the **lossless** option (bit-identical | |
| base weights, ~70 MB download, requantization avoided entirely), the exact | |
| same configuration is also available as a LoRA adapter at | |
| [MiMo-V2.6-Flash-RL-Uncensored-Heretic-LoRA-GGUF](https://huggingface.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-LoRA-GGUF). | |
| **heretic-gguf is available at | |
| [github.com/MoriNoNushi/heretic-gguf](https://github.com/MoriNoNushi/heretic-gguf)** β | |
| the full tool, so the method can be applied to other GGUF models. | |
| ## Results | |
| Measured on **140 harmful prompts** (100 from `mlabonne/harmful_behaviors` | |
| test + 40 custom) and **100 harmless prompts** (`mlabonne/harmless_alpaca` | |
| test), CoT-skip prefix (`<think></think>`, thinking suppressed), greedy | |
| decoding, 100-token responses, against the MXFP4 base: | |
| | | Refusal rate (harmful) | KL divergence (harmless) | | |
| |---|---|---| | |
| | Base model | 95.71% (134/140) | 0 (by definition) | | |
| | **Ablated model (trial 85)** | **3.57% (5/140)** | **0.0568** | | |
| The scores above were measured on the LoRA form of the ablation; the merged | |
| model applies the identical delta, requantized to MXFP4/Q8_0, so behavior | |
| should match within quantization noise. Refusals are counted by | |
| refusal-keyword matching (English + Chinese + first-person-negation markers | |
| such as "I'm not going to / able to ..."). KL divergence is measured on | |
| first-token logits on harmless prompts. Note that the study was run with the | |
| CoT-skip prefix (thinking suppressed, as in stock Heretic); with full | |
| thinking enabled the model may still reason its way back to a refusal | |
| mid-trace, so real-use refusal rates can be somewhat higher than the 3.57% | |
| above. | |
| > **Note on KL:** the KL divergence above (and the optimization objective | |
| > itself) was measured against the **MXFP4** quant. KL is a | |
| > baseline-relative metric, so on a different base quant the effective drift | |
| > from that quant's baseline may differ. | |
| ## Usage | |
| ```bash | |
| llama-server \ | |
| -m MiMo-V2.6-Flash-RL-Uncensored-Heretic-MXFP4-00001-of-00002.gguf \ | |
| --jinja | |
| ``` | |
| Add your usual offload/context flags (`-ngl 999`, `-c`, tensor splits, | |
| etc.) β nothing model-specific is required, and no special sampling | |
| parameters are needed. MiMo-V2.6-Flash-RL (`mimo2`) support is merged | |
| upstream in llama.cpp β any recent build works, no patches or PRs needed. | |
| ## How it was made | |
| - **Method:** directional ablation ("abliteration") β the refusal direction | |
| in residual space (difference of means over 480 harmful / 480 harmless | |
| prompts, 5% winsorized, orthogonalized against the harmless mean) is | |
| projected out of the attention output and MoE down-projection weights. | |
| Strengths, layer kernels, and direction selection were tuned by | |
| multi-objective Optuna TPE (minimize refusal rate and KL jointly). This | |
| model is trial 85 of study `mimo26flash`, exported with | |
| `heretic-gguf export --mode merged`. | |
| - **Configuration** (study `mimo26flash`, trial 85; global direction scope, | |
| direction index 26.2 of 48; per-expert strengths scaled by measured | |
| harmful/harmless routing frequency; `row_normalization = "pre"`): | |
| - attn.o_proj: max weight 6.39 @ layer 36.4 of 48. | |
| - routed MLP down-proj: max weight 1.58 @ layer 31.9. | |
| - **Merged export:** heretic-gguf expresses ablation as a rank-1 LoRA | |
| overlay (the same math stock Heretic writes into PEFT adapters); the | |
| merged exporter materializes that delta exactly β full-rank, no LoRA | |
| factorization loss β and requantizes only the patched tensors to their | |
| original type (MXFP4 experts, Q8_0 attention/dense). This is one extra | |
| quantization step on those tensors relative to the base; the | |
| [LoRA form](https://huggingface.co/MorinoNushi/MiMo-V2.6-Flash-RL-Uncensored-Heretic-LoRA-GGUF) | |
| avoids it entirely. | |
| ## Responsible use & disclaimer | |
| - **This model can generate content that is offensive, disturbing, hateful, | |
| sexually explicit, violent, or otherwise objectionable, including detailed | |
| instructions for harmful or illegal acts.** That is the direct and | |
| intended consequence of removing refusal behavior. | |
| - The ablation suppresses *refusals*, not the base model's knowledge β | |
| outputs on dangerous topics may be wrong, hallucinated, or incoherent. | |
| Nothing the model says should be treated as accurate, safe, or legal | |
| advice. | |
| - **Do not deploy this model in any production system, public-facing | |
| service, or multi-user setting.** It is intended for personal research, | |
| red-teaming, and evaluation purposes. | |
| - **You, the user, are solely responsible for any output the model produces | |
| and for any consequences of using it.** The authors of this release, of | |
| heretic-gguf, of Heretic, and of Xiaomi accept no liability whatsoever. | |
| Using this model to produce illegal content or to harm others is your | |
| choice and your legal exposure β ensure your use complies with all | |
| applicable laws in your jurisdiction. | |
| - By downloading or using this model you acknowledge the above. | |
| ## License | |
| The base model is MIT-licensed (see the | |
| [base repo](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL)); | |
| this model inherits those terms. The heretic-gguf tooling used to produce it | |
| is AGPL-3.0-or-later. | |