Llama-3.3-70B-Instruct-abliterated-8bit-mlx

Llama-3.3-70B-Instruct-abliterated 8-bit MLX

Uncensored Llama 3.3 70B on Apple Silicon, at 8-bit for maximum fidelity. Abliterated and converted to MLX — 75 GB, for 96–128 GB Macs. No cloud, no API key, no refusals.

An 8-bit MLX quantization of huihui-ai/Llama-3.3-70B-Instruct-abliterated, packaged for fast local inference on Apple Silicon.

8-bit was chosen instead of the more common 4-bit so the quant preserves as much of the base model's quality as possible — meant for users who have the unified-memory headroom and care more about output fidelity than minimum footprint.

Model details

Hardware requirements

You need an Apple Silicon Mac with enough unified memory to hold the full model in RAM, plus headroom for the KV cache and your OS:

• Will not run on 64 GB Macs — use a 4-bit quant instead.
• Minimum: ~80 GB free unified memory (so a 96 GB Ultra or a 128 GB Max).
• Comfortable: 128 GB or more, especially if you want long contexts.

Usage

Install mlx-lm:

pip install mlx-lm

Generate from the command line:

mlx_lm.generate \
    --model divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx \
    --prompt "Write a short poem about Apple Silicon." \
    --max-tokens 200

Or load it from Python:

from mlx_lm import load, generate

model, tokenizer = load("divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx")

messages = [{"role": "user", "content": "Hello, who are you?"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

Credits

• Base abliteration by huihui-ai, built on Meta's meta-llama/Llama-3.3-70B-Instruct.
• MLX 8-bit conversion by divinetribe using mlx-lm.
• For background on the abliteration technique, see Maxime Labonne's write-up on Hugging Face.

License

Inherits the Llama 3.3 Community License from the upstream model. Use of this quant is bound by the same terms and Acceptable Use Policy — accept the gated-access prompt above to access the weights.

Notes

"Abliterated" means the model's built-in refusal direction has been suppressed so it doesn't refuse benign-but-edgy requests. It is not a general capability upgrade — please use it responsibly and within the bounds of the upstream license.

<!-- nicedreamz-cross-link -->

---

About the author

This model was built by Matt Macosko (@nicedreamzapp) for the claude-code-local stack — run Claude Code 100% on-device with local AI on Apple Silicon (⭐ 2,664 on GitHub).

• 🤗 All my models: nicedreamzwholesale.com/software/huggingface/
• 💻 Software portfolio: nicedreamzwholesale.com/software/
• 🔒 AirGap AI (legal / healthcare / NDA workflows): nicedreamzwholesale.com/airgap/

<!-- /nicedreamz-cross-link -->

More abliterated MLX models

Part of the Abliterated MLX for Apple Silicon collection — 11 uncensored models converted for Macs, from Gemma 4 12B up to Llama 3.3 70B, plus Qwen3, Qwen3-VL, Hermes 4 and Muse Glimmer 30B.

---

<!-- ccl-frontdoor -->

Part of Claude Code Local

This model is one of the fighters in Claude Code Local (3.2k★), which runs Claude Code 100% on-device on Apple Silicon through an MLX-native Anthropic-API server. Not sure which local model to run as an agent? Check the Agent-12 local agent leaderboard: real agent tasks, judged by the filesystem, same hardware for every row.

Built by Matt Macosko in Humboldt, CA. Open to work on local-AI and Apple Silicon inference: [email protected].